The essence of the problem lies in the physics of quantum information itself. Unlike the bits of a conventional computer, which sit comfortably in well-defined states of zero or one, qubits exist in delicate superpositions that can be destroyed by virtually any interaction with the environment. Conventional computers also suffer from errors caused by noise and radiation, but decades of mature error-correction techniques allow those errors to be detected and repaired almost instantly. In the quantum realm, however, the rules are far harsher. If too many errors accumulate before they can be corrected, the entire computation collapses into meaningless noise. The longer any quantum operation takes, the larger the window of vulnerability, which is precisely why speed is not merely a convenience in quantum computing but a fundamental requirement for reliability.

Lei Du, a researcher in Applied Quantum Physics at Chalmers and lead author of the new theoretical study published in Physical Review Letters, explains the stakes plainly. “The fundamental building blocks of quantum computers, known as qubits, are so sensitive that even the smallest disturbance can cause the quantum state to deviate from the target, resulting in the loss of information. If too many errors accumulate before they can be corrected, the computation can fail,” Du says. In other words, every millisecond that a quantum system spends exposed to its environment is a millisecond in which the information it holds risks decaying beyond repair. Cutting the duration of quantum operations by three orders of magnitude therefore does far more than make calculations quicker; it fundamentally changes the error budget within which a working quantum computer must operate.

To confront this fragility, the field has been exploring more resilient ways of storing quantum information. One of the most promising strategies involves bosonic quantum codes, an approach that departs from the idea of encoding information in individual qubits. Instead, bosonic codes distribute quantum information across the microwave fields contained within superconducting circuits, using the rich structure of these electromagnetic oscillations as a protective container. As Tangyou Huang, a researcher in Quantum Technology at Chalmers and co-author of the study, notes, “Rather than storing quantum information in individual qubits, bosonic codes encode information in the microwave fields found within superconducting circuits. This approach has been shown to provide stronger protection against certain types of errors.” In essence, bosonic codes build a measure of error resistance directly into the hardware, providing an intrinsic shield that individual qubits alone cannot offer.

But there has always been a catch. While bosonic codes are excellent at protecting information, the quantum operations needed to create and manipulate these encoded states are notoriously difficult to perform. Previous techniques built up the required quantum states piece by piece, guiding the system through thousands of repeated driving cycles in a slow, painstaking process. Each additional cycle adds another opportunity for environmental disturbances to corrupt the delicate states being assembled. The irony was sharp: the very error-correcting structures designed to protect quantum information had to be constructed through procedures so slow and cumbersome that errors could creep in before the protection was even in place. This paradox has long been recognized as a key obstacle on the road to practical fault-tolerant quantum computing.

The Chalmers team’s breakthrough lies in abandoning the step-by-step construction paradigm altogether. Rather than assembling quantum states incrementally, Du and Huang devised a method that can complete a diverse range of quantum operations on bosonic states within a single driving cycle of the system, rather than the several thousand cycles previously required. “Our method shows that a diverse range of quantum operations on bosonic states can be completed within a single driving cycle, rather than the several thousand cycles that have been required previously. This makes the operations both faster and more efficient, while reducing the risk that disturbances will corrupt the information before the process is finished. It represents an important step towards fault-tolerant quantum computers,” Du says. By compressing operations that once spanned thousands of periods into a single period, the technique reduces the exposure time of fragile quantum information by a factor of more than a thousand, dramatically shrinking the probability that noise will strike mid-operation.

The theoretical engine behind this speed-up is a newly proposed class of operations known as quantum lattice gates, first introduced by the same research team in earlier work. These gates form a universal set of elementary building blocks for controlling bosonic quantum states, functioning much like shortcut commands that allow complex operations to be executed in one stroke rather than through long sequences of elementary steps. Huang offers a vivid analogy: “You can think of it like building a large Lego castle. Instead of assembling it brick by brick and risking mistakes along the way, quantum lattice gates act like pre-built Lego modules that can be connected quickly and efficiently.” The image captures the conceptual shift precisely: where previous approaches stacked up thousands of small, error-prone interventions, the new framework provides robust, prefabricated units that snap together with minimal overhead.

Underneath this framework lies a control technique known as Floquet control, in which a quantum system is driven by carefully designed periodic control signals. Floquet engineering has become a powerful tool in modern quantum physics, allowing researchers to sculpt the effective dynamics of a quantum system by shaping how it is periodically driven. Previous Floquet-based implementations of bosonic operations, however, relied on slow processes that demanded many driving cycles to converge. The new method achieves what earlier schemes could not: it implements quantum lattice gates directly within a single driving period, exploiting the fine structure of the system’s driven dynamics so that the desired transformation occurs essentially immediately. The result, documented in the paper “Single-Period Floquet Control of Bosonic Codes with Quantum Lattice Gates,” is a control paradigm in which some operations become more than a thousand times faster than their predecessors.

Crucially, the method is not confined to an abstract theory. It is tailored for superconducting quantum computers, one of the leading hardware platforms in the global race toward large-scale quantum machines, and the same technology being pursued at Chalmers itself, where a 100-qubit quantum computer is currently under development. “A key advantage of our approach is that it can be implemented using existing superconducting quantum circuit platforms. We are already discussing possible experimental realisations with colleagues at Chalmers, and we hope to see a demonstration of the method in the near future,” Huang says. Because the technique builds on hardware architectures that already exist in laboratories around the world, the path from theory to experiment may be considerably shorter than for approaches that would require entirely new physical platforms. An experimental demonstration would mark a decisive step in validating whether the dramatic theoretical speed-up survives contact with the imperfections of real devices.

For the field at large, the significance of the work goes beyond a single impressive number. The creation and manipulation of error-correcting quantum states, such as those encoded in bosonic codes, is widely regarded as one of the major unsolved engineering challenges in quantum computing. Every fault-tolerant architecture ultimately depends on being able to prepare, control and measure protected quantum states quickly and reliably, faster than errors can accumulate. By showing that such operations can, in principle, be executed within a single driving cycle on standard superconducting hardware, the Chalmers researchers have demonstrated that the speed barrier was not an unavoidable feature of quantum physics but a limitation of control strategies, one that clever theoretical design can shatter. “Our results address one of the major bottlenecks in the field: how to quickly and reliably create and control the error-correcting quantum states that could play an important role in future quantum computers,” Du says.

The study, authored by Tangyou Huang, Lei Du and Lingzhen Guo, was conducted by researchers affiliated with Chalmers University of Technology in Sweden and Tianjin University in China, and was funded by the National Natural Science Foundation of China, the Wallenberg Centre for Quantum Technology, and the Knut and Alice Wallenberg Foundation. As quantum computers worldwide continue to grow in size and ambition, techniques like single-period Floquet control may prove essential in converting raw hardware into machines that can actually deliver on the field’s long-standing promises. If the coming experimental demonstrations succeed, the thousand-fold acceleration could be remembered as one of the pivotal steps that carried quantum computing out of its fragile infancy and into the era of genuine fault tolerance.

News Publication Date: 10-Sep-2026

Web References: https://doi.org/10.1103/tnb8-3m8m; https://www.nature.com/articles/s42005-025-02354-0

References: Huang, T., Du, L., & Guo, L. (2026). Single-period Floquet control of bosonic codes with quantum lattice gates. Physical Review Letters. https://doi.org/10.1103/tnb8-3m8m

The study was led by corresponding author Bernard Le Foll, MD, PhD, of the Centre for Addiction and Mental Health in Toronto, Canada, a researcher whose career has focused on the pharmacology and treatment of substance use disorders. By selecting a randomized crossover design — widely regarded as the gold standard for examining the acute behavioral effects of psychoactive substances — the research team was able to compare each participant’s driving performance across different conditions while that individual served as his or her own control. This methodological choice matters enormously in a field cluttered with confounding variables: cannabis users differ widely in tolerance, metabolism, body composition, and baseline driving skill. By randomizing the order of exposure and washing out between sessions, the investigators could isolate the causal contribution of THC dose itself, rather than merely observing correlations between cannabis use and poor driving outcomes in observational data.

The pharmacological subtlety at the heart of the research concerns edibles specifically. Unlike inhaled cannabis, which delivers THC rapidly to the bloodstream through the lungs and produces peak effects within minutes, edible cannabis undergoes first-pass metabolism in the liver, where THC is converted to 11-hydroxy-THC, a metabolite that is itself powerfully psychoactive and may cross the blood-brain barrier even more readily than the parent compound. This transformation produces a delayed onset of intoxication — often thirty minutes to two hours — a longer duration of effect, and a notoriously unpredictable relationship between the dose consumed and the degree of impairment experienced. The delayed onset is also what makes edibles uniquely hazardous in the driving context: consumers who do not immediately feel “high” may ingest additional doses, misjudge their state, and then get behind the wheel while their impairment is still climbing.

What makes the new findings so legally significant is the disconnect they expose between blood THC concentration and functional impairment. Several jurisdictions — including a number of U.S. states that have legalized recreational cannabis, as well as Canada under its nationwide framework — have adopted per se laws that mirror drunk-driving statutes: if a driver’s blood contains a specified nanogram-per-milliliter quantity of THC, that driver is presumed impaired by law, regardless of observed behavior. These thresholds were adopted largely for administrative convenience, because blood THC levels offer an objective, enforceable number. But THC pharmacokinetics frustrate this logic. Unlike alcohol, whose blood concentration tracks intoxication reasonably well across individuals, THC is highly lipophilic, sequestering rapidly in fatty tissues and releasing slowly over days or weeks in regular users. A chronic cannabis consumer may register blood THC levels far above any legal per se threshold while being functionally unimpaired, while a naive user may exhibit profound cognitive and psychomotor deficits at concentrations that would be legally permissible.

Against this backdrop, the crossover trial’s central result acquires its sting: measurable, dose-dependent degradation of simulated driving performance occurred at blood THC concentrations below the very thresholds that define legal impairment. In practical terms, this means a driver could pass a roadside per se screening and still be operating a vehicle with compromised abilities — slower reaction times, degraded lane-keeping, impaired divided attention, and reduced capacity to respond to unexpected hazards. Simulated driving paradigms, the standard instrument of this research field, quantify such deficits with precision, capturing metrics such as standard deviation of lateral position, speed variability, response latency to unexpected events, and collision rates, all while eliminating the ethical impossibility of testing truly impaired drivers on public roads.

The implications ripple outward in several directions. For policymakers, the study suggests that per se THC thresholds may be simultaneously over-inclusive — penalizing tolerant regular users who are not impaired — and under-inclusive, missing impairment in less tolerant individuals whose blood levels have already fallen below the cutoff. The alternative approaches that toxicologists have long advocated, such as combining blood or oral fluid testing with standardized field sobriety testing, or developing functional impairment assessments, gain empirical support from these results. For public health communicators, the study supplies a clear and urgent message that legal limits do not function as a “safe to drive” certificate the way blood alcohol limits roughly do. For consumers, particularly the young adults who represent the demographic most likely to consume edibles and also most likely to be involved in motor vehicle crashes, the message is that the absence of a legally detectable blood concentration offers no protection against the pharmacological reality of impairment.

The publication also arrives amid a broader re-evaluation of cannabis and road safety driven by the rapid normalization of the drug. Legalization in Canada in 2018 and in a growing roster of American states has been accompanied by increases in self-reported cannabis-impaired driving and by stubborn uncertainty among law enforcement agencies about how to detect it. Unlike alcohol, for which the breathalyzer provides a cheap, instant, and legally robust measurement, cannabis detection requires blood or oral fluid sampling, laboratory analysis, and interpretation against thresholds whose scientific validity this study now directly challenges. The research community has warned for years that THC blood levels are a poor proxy for impairment; this trial elevates that warning from pharmacokinetic theory to controlled experimental evidence.

It is worth emphasizing what the crossover design contributes to the credibility of this conclusion. Placebo-controlled cannabis administration studies face substantial regulatory and ethical hurdles, and few research centers in the world possess the licenses, facilities, and expertise to conduct them. The fact that impairment tracked dose systematically — rather than appearing as a scattered or inconsistent pattern across participants — strengthens the inference that the relationship is causal and pharmacological rather than driven by expectancy effects. Participants in such trials are typically aware they may receive active drug, which can bias performance; the dose-dependency observed here suggests that expectancy alone cannot account for the results, since expectation would not scale neatly with the amount of THC actually administered and absorbed.

The study is accompanied by a commentary in JAMA Network Open, a signal that the journal and its editors view the findings as consequential enough to warrant explicit scholarly interpretation. Commentaries attached to clinical trials often serve to translate technical results into clinical and policy guidance, and in this case the pairing underscores the tension between the scientific evidence and the enforcement frameworks currently in place. Researchers, toxicologists, and legal scholars will now face intensified pressure to develop impairment-detection tools that reflect functional capacity rather than chemical concentration — a shift comparable to what would be required if blood alcohol levels were discovered to diverge substantially from actual driving deficit.

The work also carries lessons for the evolving edible marketplace. As legalized products migrate toward higher-potency edibles, gummies, beverages, and novel formulations, the dose-response relationship documented here becomes a matter of product labeling, dosing guidance, and consumer education. Many jurisdictions mandate standard serving sizes of a few milligrams of THC, yet studies of edible consumption repeatedly show that users frequently exceed recommended doses, misjudge onset, and combine edibles with alcohol. Each of these behaviors amplifies the risks that the trial quantifies, and none of them is captured by a blood threshold measured hours after consumption.

For the researchers at the Centre for Addiction and Mental Health and their collaborators, the trial represents a milestone in translating cannabinoid pharmacology into road-safety policy. The rigorous demonstration that impairment occurs below legal thresholds does not by itself rewrite any statute, but it hands regulators, prosecutors, and public health authorities a scientific mandate to reconsider how cannabis-impaired driving is defined, detected, and deterred. As edible cannabis continues its expansion into mainstream markets, the gap between what the law can measure and what the brain can no longer do has now been documented under the most controlled conditions science can provide — and closing that gap has become an urgent task for legislators and scientists alike.

The findings, published as “Dose-dependent effects of cannabis edibles on simulated driving performance” in JAMA Network Open, are available to the public through an access-token link provided by the journal, with an accompanying commentary offering further interpretation of the results and their policy significance.

News Publication Date: 31-Aug-2026

Web References: EurekAlert! news release, JAMA Network Media Center

The fellowship itself offers up to five years of funding, combining a generous stipend with full tuition equivalent, a structure deliberately designed to free recipients from the conventional constraints that often shape doctoral research. Rather than tethering students to specific grants, laboratories, or pre-defined projects, the Hertz model gives its fellows the latitude to pursue bold, high-risk, high-reward lines of inquiry that might otherwise wither under the pressures of conventional graduate funding mechanisms. This philosophy reflects a long-standing conviction at the foundation that transformative science emerges not from incremental, tightly managed projects, but from individuals with the initiative and imagination to follow their curiosity wherever it leads. Beyond the money, fellows gain lifelong access to mentorship, professional development events, and a global network of more than 1,300 innovators spanning academia, industry, government, and entrepreneurship.

Derek Haseltine, senior director of the Hertz Fellowship Program, framed the foundation’s search in characteristically ambitious terms. The foundation, he explained, is looking for students defined by initiative, relentless curiosity, and creativity, drawn to solving the challenges that matter most. His invitation to prospective applicants captured the intellectual spirit the program prizes: those who find themselves endlessly pushing past the question of “why” in their research are precisely the candidates the foundation wants to hear from. That framing is not mere rhetoric. The fellowship’s selection process is famously grueling, involving multiple rounds of technical interviews conducted by scientists and engineers who probe not just what candidates know, but how they think, how they respond to unfamiliar problems, and whether they possess the originality and resilience to pursue ideas that others might abandon.

The rigor of that process is by design, and its track record speaks for itself. Since 1957, the Hertz Foundation has supported innovators whose work has transformed fields ranging from astrophysics to artificial intelligence. Among its alumni are Nobel laureate John Mather, senior astrophysicist at NASA’s Goddard Space Flight Center and former project scientist for the James Webb Space Telescope; Kimberly Budil, director of Lawrence Livermore National Laboratory; and Mung Chiang, president of Northwestern University. More recently, the fellowship’s reach extended into the frontier of artificial intelligence: Dario Amodei and Jared Kaplan, co-founders of Anthropic, the prominent AI safety and research company, are both Hertz Fellows. The concentration of fellows in positions of extraordinary scientific and technological leadership has made the fellowship something of a leading indicator of where consequential research is headed.

The selection process for the 2027 cohort is being led by two distinguished Hertz Fellows: Philip Welkhoff, director of the malaria program at the Gates Foundation, and Anna Bershteyn, managing director of science and global health R&D at Coefficient Giving and associate professor at NYU Grossman School of Medicine. Their involvement underscores a hallmark of the program: the interviews and evaluations are conducted largely by fellows themselves, ensuring that those who assess candidates share the deep technical grounding and firsthand understanding of what it takes to do original scientific work. Bershteyn’s own career, bridging mathematical modeling and global health policy, exemplifies the kind of boundary-crossing ambition the fellowship seeks to cultivate.

One distinctive feature of the fellowship remains a moral, though non-binding, commitment: every Hertz Fellow pledges to make their skills available to the United States in times of national emergency. The provision dates to the foundation’s Cold War origins and reflects its enduring mission of advancing American scientific and technological leadership. While the commitment has rarely if ever been formally invoked, it remains a defining element of the fellowship’s identity, signaling that the foundation views scientific talent as a strategic national resource and its fellows as citizens whose expertise carries obligations beyond their own careers.

The Hertz Community, as the fellowship’s alumni network is known, has in recent years deliberately extended its reach beyond its own membership, drawing leading minds and institutions from around the world into its orbit. The 2026 Hertz Summer Workshop, held outside Chicago, featured a keynote address from Mary Brunkow, the 2025 Nobel laureate and distinguished investigator at the Institute for Systems Biology. This month, the foundation launched a salon series pairing Hertz Fellows with researchers at ARIA, the United Kingdom’s research funding agency, which is led by Hertz Fellow Kathleen Fisher. These initiatives reflect a conscious effort to keep the community’s thinking connected to the broader currents of science and society, ensuring that the fellowship functions not merely as a funding mechanism but as a living intellectual network capable of convening Nobel laureates, national laboratory directors, and frontier researchers across disciplines and borders.

The foundation has also built an extensive infrastructure of partnerships that translate the fellowship’s intellectual capital into real-world application. Collaborations with organizations including Analog Devices, Breakthrough Energy Discovery, the Gates Foundation, and Lawrence Livermore National Laboratory provide fellows with opportunities such as internships and collaborative research placements. These arrangements connect doctoral research in fields like semiconductor physics, clean energy technology, global health, and high-performance computing to the industrial and governmental settings where such work often achieves its greatest impact. For fellows weighing whether to pursue academic, entrepreneurial, or policy-oriented careers, the partnerships offer rare early exposure to the full spectrum of pathways available to technically exceptional scientists.

Practical considerations have also been addressed for the many strong applicants who will not ultimately receive the fellowship. More than 50 universities maintain standing agreements with the Hertz Foundation, allowing successful candidates to accept their awards quickly and seamlessly without protracted negotiations over funding terms. In addition, the foundation has established an agreement with Case Western Reserve University, which offers full financial benefits to Hertz Fellowship finalists who choose to attend that institution for graduate school. The arrangement softens the blow for finalists, a group that already represents a tiny fraction of an exceptionally competitive applicant pool, and ensures that even those who fall just short of the award retain access to a fully funded doctoral education.

Prospective applicants will have two opportunities to learn more about the process before the application deadline. Fellowship Director Derek Haseltine will lead information sessions with current Hertz Fellows on Thursday, September 10, at 6 p.m. EDT, and Monday, September 14, at Noon EDT. The sessions are designed to help candidates prepare their strongest application packages, with Hertz Fellows facilitating breakout sessions drawn from their own application, academic, and research experiences. Planned topics include strategies for applying at different academic stages, whether as a college senior, during a gap year, or as a current graduate student; choosing a compelling research project; and navigating STEM fields as an underrepresented student. The inclusion of the latter topic reflects a growing emphasis within the community on broadening the pipeline of candidates and ensuring that the fellowship draws from the widest possible pool of talent.

For more than 60 years, the Hertz Foundation has stood as a pillar of independent support for American science, cultivating a multidisciplinary network of innovators whose work has positively impacted millions of lives. With the 2027 application cycle now open, the foundation is once again searching for the rare students who combine technical brilliance with the audacity to pursue ideas capable of transforming society. For those who believe they fit that description, the path begins with an application due by the end of October, and, for a chosen few, leads to five years of freedom, a lifelong community, and a place in a lineage of scientists whose fingerprints mark some of the most significant scientific achievements of the modern era.

The sessions will be led by Derek Haseltine, Director of the Hertz Fellowship, who will be joined by current Hertz Fellows. Rather than a conventional webinar format, the sessions have been structured around interaction: after a short introduction covering the fellowship and the mechanics of the application, participants will move into breakout rooms where Hertz Fellows will facilitate open question-and-answer discussions. This format reflects a deliberate philosophy at the foundation, one that treats the application process not as a bureaucratic hurdle but as the first stage of a mentoring relationship. Prospective applicants will have the rare opportunity to speak directly with people who have already navigated the process, earned the award, and gone on to careers at the highest levels of research, entrepreneurship, and public service.

The topics scheduled for the breakout rooms reveal a great deal about the fellowship’s character and the kinds of questions that most often occupy applicants’ minds. One group will focus on the timing of the application, addressing how the fellowship fits the circumstances of college seniors, students taking gap years, and first-year graduate students, each of whom occupies a different position along the pipeline toward doctoral study. Another session, offered on September 10 only, will address the particular challenges faced by dual-degree MD/PhD applicants, a population whose extended training timelines and dual commitments raise questions that standard fellowship guidance often leaves unanswered. Choosing a graduate school that aligns with one’s research interests, and choosing a research project once enrolled, will also be explored, reflecting the foundation’s view that the fellowship is not merely a funding instrument but a framework for shaping an entire scientific career.

Additional breakout rooms will tackle questions of personal sustainability and identity in research careers. A session on building resiliency in graduate school acknowledges what many experienced researchers know well: that doctoral training can be psychologically grueling, and that the ability to withstand setbacks, failed experiments, rejected papers, and long stretches of uncertainty is as essential to scientific success as intellectual brilliance. A dedicated session for those underrepresented in STEM reflects the foundation’s ongoing effort to widen the demographic and disciplinary reach of its fellowship community. Finally, a session on coordination with other fellowships will address the practical realities of stacking or sequencing awards, a topic of considerable importance given that many strong applicants will also be weighing offers from the National Science Foundation Graduate Research Fellowship program, the Department of Defense’s National Defense Science and Engineering Graduate fellowships, and institutional awards.

The Hertz Fellowship itself occupies a unique position in the American research landscape. Established more than sixty years ago by the Fannie and John Hertz Foundation, the fellowship is explicitly committed to advancing American scientific and technological leadership by identifying the nation’s most promising young technical minds and giving them the freedom to pursue their most ambitious ideas. Unlike many fellowships that specify fields or constrain research direction, the Hertz Fellowship is famously open-ended: recipients may pursue doctoral studies in any of the applied physical, biological, or engineering sciences at any institution in the United States. That freedom extends to the money itself. Hertz Fellowships provide five years of support, structured so that Fellows can follow their intellectual curiosity across disciplines and institutions without the financial pressures that often push early-career researchers toward safer, more incremental projects.

The selection process has become legendary for its rigor. Beyond the standard review of academic records, essays, and letters of recommendation, Hertz finalists are subjected to a multi-day examination process that includes extended technical interviews probing not just what candidates know, but how they think. Interviewers frequently pose open-ended problems drawn from real research frontiers, watching for creativity, logical precision, the ability to recover from dead ends, and a certain playful courage in the face of the unfamiliar. The foundation has long argued that this method identifies a quality that transcripts cannot measure: the capacity for genuine scientific invention. The argument is supported by outcomes. Over its history, the fellowship has produced a remarkable roster of alumni, including Nobel laureates, founders of transformative technology companies, leaders of national laboratories, and researchers whose work has shaped fields from artificial intelligence and materials science to molecular biology and quantum information.

For the foundation, the upcoming sessions are also part of a broader effort to demystify the application and, implicitly, to broaden the pool of people who consider applying. The Hertz Foundation’s announcement states plainly that it wants every prospective applicant to fully understand the fellowship and feel confident in assembling a strong application. That framing matters. Selective fellowships have historically drawn disproportionate numbers of applicants from a narrow set of elite institutions, and outreach efforts such as these are increasingly recognized as important tools for ensuring that extraordinary talent at less prominent universities has the information and encouragement needed to compete. The inclusion of breakout rooms specifically devoted to underrepresented students and to non-traditional pathways, such as gap years and first-year graduate applications, signals a deliberate push in this direction.

The timing of the sessions, roughly seven weeks before the October 30 application deadline, is strategic as well. Information gathered in September can meaningfully shape the essays, recommendation strategies, and school-selection decisions that applicants must finalize in the following weeks. Fellowship advisors at universities often note that one of the most common mistakes in applications like the Hertz is generic writing, essays that describe ambition in abstract terms without connecting it to concrete technical problems the applicant has grappled with. Hearing directly from Fellows about what distinguished successful applications, and about how the foundation evaluates intellectual independence rather than conventional prestige, can help candidates calibrate their materials in ways that generic advice cannot.

For students still deciding whether doctoral study in the applied sciences is right for them, the sessions also serve a wider informational purpose. Discussions about how to choose a research project, how to align graduate school choice with research interests, and how to balance fellowship obligations with other funding sources offer guidance that extends well beyond the Hertz application itself. In an era when the economics of doctoral education are under scrutiny, when many PhD students face stipend pressures and uncertain career pipelines, fellowships that grant genuine financial independence have acquired an outsized significance. The Hertz model, which frees recipients to take intellectual risks and discourages narrowly defined, grant-driven research at the earliest career stage, is often cited as a counterexample to prevailing trends in graduate training.

Prospective applicants who wish to attend either of the two sessions can register online through the Hertz Foundation’s fellowship website, with separate registration links for the September 10 evening session and the September 14 midday session. The foundation has also opened a direct channel for other questions about the fellowship or the events, directing inquiries to fellowshipinfo@hertzfoundation.org. With the application window now open through the end of October 2026, and with the fellowship’s reputation for identifying the architects of America’s future technical leadership intact after more than six decades, the two September sessions represent a low-cost, high-value opportunity for anyone contemplating one of the most consequential applications in American graduate education. For students whose ambitions reach beyond incremental science, an evening or midday spent in conversation with Hertz Fellows may well be the first step in a career measured in decades of impact.

The new study, published in Nature Communications, describes a generalised approach to quantum computational spectroscopy, an idea that extends the reach of a technique traditionally performed in laboratories with lasers, detectors and samples. Instead of measuring a physical material directly, the researchers used a quantum processor to carry out simulations of spectroscopic measurements, reconstructing key indicators of quantum behaviour from within the computation itself. The paper, titled "Generalised quantum computational spectroscopy on a quantum chip," suggests that quantum hardware could serve not merely as a calculator but as an exploratory tool for understanding complex quantum matter.

The motivation behind the work lies in a fundamental limitation of classical computing. Quantum systems, such as interacting molecules or engineered materials with exotic electronic properties, can be exceptionally difficult to model using conventional machines. The difficulty arises because the number of quantum states that must be tracked grows exponentially with system size: adding just a handful of interacting quantum particles can multiply the computational burden many times over. Even the most powerful supercomputers in the world eventually run out of capacity, forcing scientists to rely on approximations that may miss important physics. Quantum computers, which operate on quantum-mechanical principles themselves, hold the promise of representing such systems far more naturally, because their fundamental units of information — quantum bits, or qubits — can exist in superpositions and become entangled with one another in the same way that the particles of a real quantum system do.

Computational approaches have long complemented experimental spectroscopy by allowing scientists to predict properties using theoretical models and simulations before any measurement is attempted. In fields ranging from quantum chemistry to condensed matter physics, researchers routinely calculate the spectra of candidate molecules and materials on classical computers, comparing the results with laboratory measurements to refine their understanding. What the Queen Mary-led team has done is generalise the computational version of spectroscopy so that it applies to a much broader range of quantum systems. In particular, their method is not restricted to relatively simple, static systems in isolation. It can also handle systems that are affected by their surrounding environment, a scenario known as open-system dynamics, as well as systems whose properties change over time. These are precisely the situations where real-world materials and molecules live, bathed in thermal noise and undergoing constant evolution, and they are among the hardest cases for existing computational techniques.

The distinction between closed and open quantum systems is central to modern quantum science. In an idealised textbook description, a quantum system evolves in isolation, its behaviour governed purely by its internal structure. In reality, no system is perfectly isolated: molecules collide with their surroundings, electrons in a material interact with vibrations of the crystal lattice, and qubits in a quantum computer are disturbed by stray electromagnetic fields. This environmental coupling, often described as noise or dissipation, is usually treated as an obstacle to be minimised. But in many contexts it is an essential part of the physics, shaping chemical reaction rates, energy transport in photosynthetic complexes, and the behaviour of quantum devices. A spectroscopic method that can incorporate open-system dynamics, rather than treating them as an afterthought, therefore brings the computational tool closer to the conditions of real experiments and real materials.

At the heart of the method is a quantum computing technique known as an ancilla-assisted Hadamard test. In this procedure, an additional quantum bit, or ancilla, is used alongside the quantum system being simulated to extract measurable quantities from the computation. The ancilla acts as a probe: by preparing it in a particular state and allowing it to interact with the simulated system through controlled quantum operations, the researchers can read out information encoded in quantities that would be prohibitively expensive to estimate using classical algorithms. The Hadamard test, a well-established primitive in quantum computation, is the mechanism that makes this readout possible, allowing the team to evaluate complex quantities that encode the spectral characteristics of the system under study. By running this test on a quantum chip, the team was able to reconstruct a key measure of quantum behaviour, effectively performing the analogue of a spectroscopic measurement entirely within a quantum processor. This is the sense in which the spectroscopy is "on a quantum chip": rather than probing matter with light, the quantum computer probes a simulated quantum system and returns the kind of information that a spectroscopic experiment would yield.

To demonstrate the power of the approach, the researchers applied it to unusual quantum phenomena that sit at the frontier of modern physics. The first is parity-time symmetry breaking. In conventional quantum mechanics, physical systems are expected to obey certain fundamental symmetries, and when those symmetries break, the behaviour of the system can change dramatically. Parity-time symmetry and its breaking describe transitions that have been studied in specially engineered systems and have connections to novel optical and electronic behaviour. Capturing such transitions computationally is demanding, because they involve subtle features of a system's spectrum that approximate methods can easily miss.

The second phenomenon, topological holonomy, belongs to the family of topological effects that have become central to condensed matter physics. Topological properties are global features of a quantum system that are remarkably robust against local disturbances, which is why they are of interest for potential technologies such as fault-tolerant quantum computing and protected electronic states. Holonomy describes how a quantum system changes when it is transported around a loop in parameter space, a geometric effect that leaves a fingerprint in the system's behaviour. Such geometric phases have deep roots in physics, appearing in contexts from molecular dynamics to the design of quantum gates, and they often reveal structure that is invisible to more conventional measurements. By successfully probing both parity-time symmetry breaking and topological holonomy with their quantum chip method, the researchers showed that quantum computational spectroscopy can reach insights into quantum behaviour that are difficult to access either through conventional spectroscopy or through existing quantum computational approaches.

The significance of these demonstration cases lies in what they represent. Both phenomena involve aspects of quantum dynamics and spectral structure that standard experimental spectroscopy may struggle to isolate, and that earlier quantum algorithms were not designed to capture in full generality. A generalised framework that can address static systems, open systems interacting with their environment, and time-dependent dynamics in a single coherent method gives researchers a more flexible toolkit. It means that phenomena which are difficult to reproduce in a laboratory, or difficult to calculate on classical machines, could potentially be studied through quantum simulation instead. The choice of test cases is also telling: rather than demonstrating the method on trivial model systems chosen for convenience, the team targeted examples with genuine physical content, strengthening the case that the approach can capture meaningful science rather than merely executing a formal procedure.

The broader implications extend across several scientific disciplines. According to the research team, the work could ultimately be relevant to physics, chemistry and materials science. Computational spectroscopy allows researchers to investigate the properties of real or hypothetical materials before they are ever produced experimentally. In molecular engineering, this could mean screening candidate molecules for desired optical or electronic characteristics without synthesising them first, dramatically accelerating the search for new compounds. In drug design, it could aid in understanding how molecular structures respond to energy and interact with their surroundings, questions that lie at the heart of how pharmaceutical molecules bind to their biological targets. In advanced materials research, it could help scientists predict the behaviour of novel materials, including those whose exotic quantum properties are of technological interest, before committing resources to fabricating them. In each of these areas, the ability to simulate spectroscopic signatures on a quantum device could complement, and in some cases substitute for, costly and time-consuming laboratory work.

Dr Jinzhao Sun, from the School of Physical and Chemical Sciences at Queen Mary University of London, led the theoretical aspect of the study. The research, the team notes, represents a step towards using quantum computers not simply to perform calculations, but as tools for exploring and understanding the behaviour of complex quantum systems. That distinction matters. Much of the current excitement around quantum computing focuses on speed: the hope that quantum machines will eventually solve certain problems faster than any classical computer. This work points toward a complementary vision in which quantum processors function as scientific instruments, generating knowledge about quantum phenomena in ways that neither classical computation nor laboratory experiment can easily replicate. In this view, the quantum computer occupies a conceptual position closer to that of the telescope or the particle accelerator — an instrument through which new observations become possible — rather than simply a faster successor to the desktop machine.

As with any emerging approach, the work comes with caveats. The demonstrations described in the paper were carried out within the constraints of current quantum computing technology, and today's quantum processors remain limited in size and susceptible to errors. Qubits can lose their quantum state through interaction with their environment, and the operations performed on them are imperfect, which places practical limits on the depth and complexity of the computations that can be reliably executed. The study is best understood as a proof of principle: a demonstration that the generalised quantum computational spectroscopy framework works and can capture phenomena of genuine physical interest. Scaling the approach to systems of practical complexity will depend on continued progress in quantum hardware, including larger numbers of high-quality quantum bits and improved error correction. The researchers themselves frame the method as something whose usefulness will grow as quantum computing technology develops.

Even so, the study contributes to a growing body of work aimed at finding practical applications for near-term quantum devices. Around the world, research groups are exploring how modest-sized quantum processors, imperfect as they are, might already deliver value in areas such as simulation of quantum materials, optimisation and machine learning.

For decades, the sector label has been the first thing an analyst reaches for when sizing up a company: technology versus energy, healthcare versus financials, utilities versus consumer discretionary. A new study of every one of the 500 firms in the S&P 500 suggests that habit captures far less than investors assume. When researchers in Spain trained artificial intelligence models to recognize a company’s sector using nothing but its accounting numbers, the best-performing model — an algorithm known as K-nearest neighbors — succeeded only 49.3 percent of the time: well above blind guessing, but wrong more often than right. And when the team let an unsupervised algorithm group the firms purely by financial similarity, ignoring official labels altogether, it uncovered nine distinct financial families, every one of which cut across sector boundaries. The findings, published in The Journal of Finance and Data Science, stop short of declaring sector classifications obsolete. But they make a striking case that the labels organizing the world’s most closely watched stock index describe only part of each company’s financial anatomy — and that data-driven peer groups can reveal the rest.

Comparing companies within a sector is one of the oldest rituals in finance. Analysts benchmark profit margins against industry rivals, screen stocks by sector to diversify portfolios, and build risk models on the premise that firms facing similar markets should resemble one another on the balance sheet. Entire index families and research disciplines are organized around that assumption. Yet it is, at bottom, an empirical claim — one that had rarely been tested at the scale of an entire flagship index. A research team at the Catholic University of Ávila in Spain, working within its Dekis Research Group and led by corresponding author Ricardo Reier Forradellas, decided to put the claim to a formal test. Their question was deceptively simple: if you strip away a company’s name, its industry narrative and its stock chart, and hand a machine only its financial ratios, how often does the machine land on the same sector label that humans assigned? The answer, according to the new paper, is not nearly often enough for sectors to be treated as complete financial descriptions.

The study began with a comprehensive snapshot of the U.S. large-cap market: the fiscal year 2022 financial statements of all 500 constituents of the S&P 500. From each statement, the researchers distilled a battery of accounting ratios spanning the dimensions that fundamental analysts track most closely — profitability, which measures how efficiently a firm converts sales and assets into earnings; leverage, which captures its reliance on borrowed money; liquidity, which gauges its ability to meet short-term obligations; efficiency, which reflects how productively it deploys its resources; and cash generation, which reveals whether reported earnings are backed by real cash flow. Ratios like these compress thousands of line items into comparable numbers, making them the raw material of fundamental analysis. The team’s first step was classical rather than computational: a statistical examination of how strongly those ratios actually differed from sector to sector. The verdict was nuanced rather than clean. The ratios did vary across sectors — the labels are not arbitrary — but those differences accounted for only part of the financial variation among the 500 firms.

Then came the artificial intelligence. The researchers trained seven different supervised machine-learning models on a single task: given a company’s accounting ratios, predict its sector. Supervised learning of this kind works by letting an algorithm study examples whose answers are known — here, firms carrying official sector labels — and internalize the patterns connecting inputs to outputs. Among the seven contenders, the strongest performer was K-nearest neighbors, a deceptively simple method that makes predictions by analogy. Rather than deriving an explicit formula, the algorithm stores the training companies as points in a multidimensional space of financial ratios and classifies each new company by finding its closest neighbors and adopting whatever label dominates among them. In effect, the model asks: which established sector residents does this firm most resemble on paper? Performance was measured on validation data withheld from training — a safeguard that prevents the algorithm from simply memorizing answers it has already seen — and K-nearest neighbors reached a validation accuracy of 49.3 percent, the highest figure any of the seven approaches achieved.

To judge whether 49.3 percent is impressive or damning, one must consult the study’s baseline. Because the S&P 500’s sectors are unevenly populated, a lazy classifier that always guessed the most common sector — the majority class — would have been correct 14.8 percent of the time. Viewed against that yardstick, the machine-learning result is more than three times better, confirming that accounting ratios do carry a genuine sector signal: utilities genuinely do look different from banks on a balance sheet. But the same number carries a more provocative message. Even the best model misidentified a company’s sector more often than it identified it correctly. In practical terms, most of the index’s members behave as financial hybrids, their ratio profiles confusable with those of firms from entirely different industries. If sector membership were a full description of financial structure, a well-trained classifier should approach near-perfect accuracy. Instead, the evidence indicates that a company’s industry tells you something real about its finances — but far from everything.

Faced with that ceiling, the team changed tactics. Instead of asking the data to reproduce the human-made labels, they asked it to ignore the labels entirely. “Hence, we used unsupervised learning to group firms by financial similarity rather than by their existing labels,” explains corresponding author Ricardo Reier Forradellas of the Catholic University of Ávila. “This produced nine economically interpretable clusters.” Unsupervised learning is the branch of machine learning that finds structure without a teacher: the algorithm receives no answers, only measurements, and must discover on its own which companies naturally bunch together in ratio space. The result was not a mirror of the official taxonomy but an alternative map of the U.S. corporate economy, drawn exclusively in the currency of profitability, leverage, liquidity, efficiency and cash flow. Crucially, the clusters were not statistical noise. Each of the nine could be described in plain financial language, giving the researchers confidence that the algorithm had surfaced economically meaningful structure rather than accidental groupings.

The clearest evidence for that meaningfulness lay in how tightly knit the new groups were. Every one of the nine clusters contained companies drawn from more than one official sector, confirming that financial similarity respects no industry border. Yet the clusters were generally more internally coherent than the sectors themselves: across most of the accounting ratios, firms inside a data-driven group showed lower internal dispersion — a smaller statistical spread around the group’s typical value — than firms sharing a sector label. In other words, a company’s closest financial peers were more likely to be found inside its algorithmic cluster than inside its sector. Some familiar signatures did survive the analysis. Utilities, real estate companies and financial firms proved more readily identifiable than several other sectors, a reflection of business models that imprint themselves unmistakably on the accounts: capital-intensive networks, property-heavy balance sheets and debt-fueled intermediation leave deep accounting fingerprints. Other kinds of firms, by contrast, proved harder to pin down, slipping quietly across sector lines on the machine’s map.

Forradellas is careful to frame the result as an addition to financial practice rather than a demolition of it. “Our findings do not mean that sector classifications are obsolete,” he says. “They show that sectors tell only part of the story. When the aim is to compare companies by financial structure, accounting-based peer groups can provide a useful additional perspective.” The distinction matters for anyone who relies on comparisons professionally. For questions about regulation, industry competition or supply chains, sector membership remains the natural organizing principle. But for questions about valuation, credit risk or benchmarking financial performance — questions that turn on how a company actually funds itself, generates cash and manages its short-term obligations — the study suggests that peers defined by accounting similarity may be the more honest reference group. The two lenses answer different questions, and neither one alone captures the whole financial creature.

The research also carries a warning for anyone tempted to enshrine the new clusters as a permanent replacement taxonomy. When the team compared cluster assignments across later annual reporting periods, they found only moderate persistence: companies did not stay put in their financial families from one reporting period to the next. “This indicates that these peer groups should be updated rather than treated as fixed categories,” Forradellas adds. “Our approach complements sector taxonomies for benchmarking, peer comparison, and financial analysis.” That drift is not a flaw in the method so much as a feature of corporate life. Firms alter their capital structures, pivot their strategies, acquire rivals and ride macroeconomic cycles, and their ratio profiles shift accordingly. A company can migrate from a cash-rich cluster to a heavily leveraged one without ever changing its ticker symbol or its industry. Any financial map built from accounting data, the authors imply, must be redrawn periodically — a living taxonomy rather than a carved-in-stone one.

The findings arrive as machine learning steadily permeates quantitative finance, and they offer a template for how data-driven classification might sit alongside traditional taxonomies rather than clash with them. For index providers, the results hint at complementary ways to construct peer sets for benchmarking; for analysts and portfolio managers, they suggest that screening by algorithmic financial similarity could surface valuation signals and risks that sector screens miss. The study is also a sober reminder of the limits of AI: even the best of seven supervised models fell well short of the accuracy that would make sectors predictable from accounts alone, and unsupervised groupings still demand expert interpretation before they become economically meaningful. Published open access by KeAi, a publishing venture of Elsevier and China Science Publishing & Media Ltd, the paper — “Characterization of S&P 500 companies by sector using artificial intelligence: Statistical evidence and machine learning application” — reexamines a tool so familiar that few thought to question it. The sector, the study concludes in effect, is where a company works. The balance sheet is who it is. Investors reading only the first are seeing half the picture.

The study involved 60 young adults with an average age of 20. Before viewing the videos, participants completed validated assessments of anxiety and depression symptoms. They then watched two types of short-form content. The first consisted of videos personally recommended by the participants’ own social media accounts, reflecting the recommendations generated by the platforms’ algorithms. The second consisted of generally trending videos that were not selected specifically for each participant. Researchers categorized the videos according to their emotional and social content, while electroencephalography, or EEG, measured electrical activity across the brain. This design allowed the team to compare how the same viewers responded to content shaped by their individual digital histories with how they responded to material popular across a broader audience.

The researchers focused on frontal alpha asymmetry, an EEG measure associated with motivational direction and emotional processing. Alpha waves are relatively slow electrical oscillations that can be detected from the scalp, and differences in alpha activity between the left and right frontal regions have often been used as an indicator of approach- versus withdrawal-related responses. Greater relative activity on the left side is generally associated with more positive emotional processing and approach motivation, while stronger right-sided activity is commonly linked with negative processing, withdrawal and reduced engagement. The measure is not a diagnostic test for depression, but it can provide a real-time physiological signal of how the brain is responding while a person is encountering emotionally meaningful information. In this study, that signal was captured within milliseconds as participants watched their personalized feeds.

The use of EEG gave the study a different perspective from conventional social media research. Surveys can reveal how often people use an app or how they believe it affects their mood, but they typically rely on memory and self-interpretation after the experience has ended. The Texas researchers instead examined neural responses during exposure to algorithmically selected videos and compared those responses with participants’ reported depressive symptoms. Dr. Alva Tang, the study’s corresponding author, said the approach made it possible to observe what participants were viewing and how they were reacting while their feeds were actively serving the content. Dr. Stacie Warren, a co-author, described EEG as a measure of brain processing that operates within milliseconds, providing an objective complement to participants’ reports about their mental health.

The results pointed to a relationship between depressive symptoms and the emotional direction of personalized recommendations. Participants reporting more depressive symptoms tended to receive feeds containing more depressive or negatively toned material. Their frontal alpha asymmetry while watching these personalized videos also showed a pattern characteristic of more negative emotional processing, a response that was not observed in the same way when they watched generally trending videos. The findings do not establish that an algorithm directly causes depression, nor do they show that a single video can determine a person’s mood. Instead, they suggest that recommendation systems may learn from a user’s behavior and emotional signals in ways that repeatedly expose the person to content consistent with an existing negative state.

The most common type of recommended video in the study involved social relationships, including romance, friendship and interpersonal conflict. Within that broad category, videos could depict arguments, tension or rejection, but they could also show friends supporting one another or engaging in positive social interactions. Neutral videos contained little obvious emotional or social meaning. Researchers found that viewing fewer positive social-relationship videos was associated with relative right frontal alpha asymmetry, indicating a more negative or withdrawal-related response. The pattern is consistent with the possibility that people experiencing depression may pay greater attention to negative material, overlook positive content or interact with posts in ways that teach recommendation systems to supply more of the same. The resulting feed may then become increasingly narrow in emotional tone.

This possible feedback loop is central to the researchers’ interpretation. Modern recommendation systems are designed to predict what will hold a user’s attention. They draw on signals such as viewing duration, replays, likes, comments, follows and skips, then use those signals to rank future content. If a user lingers on emotionally intense or negative videos, the system may interpret that behavior as evidence of interest rather than distress. Continued exposure can produce more engagement, which in turn provides additional data for the algorithm. In a person already experiencing depressive symptoms, that cycle could repeatedly connect negative attention patterns with negative recommendations. The study’s authors emphasize that their findings concern this interaction between user behavior, algorithmic selection and emotional processing rather than an isolated effect of screen exposure.

The researchers also accounted for total screen time, a factor frequently examined in studies of digital media and mental health. Their argument is that time alone may be an incomplete measure because social media use is not uniformly harmful or beneficial. Ten minutes spent watching supportive, humorous or informative material may have a different psychological effect from ten minutes spent consuming conflict-driven content that intensifies rumination. By separating exposure to personalized recommendations from exposure to general trends, the study attempted to examine the content and selection process more closely. Because the participants used their own phones and accounts, the experiment preserved some of the conditions of ordinary social media use, although the relatively small sample and proof-of-concept design mean that the findings require replication in larger and more diverse groups.

The results point toward a different approach to digital well-being than simply imposing strict limits on phone use. The researchers argue that young people may benefit from learning how recommendation systems respond to their actions and how deliberately shaping those actions can alter a feed. Following creators who produce constructive or positive material, interacting with content that supports a desired mood and removing sources of repeated negativity may gradually change what an app recommends. Users can also reset some accounts to default recommendations and begin training the system around new interests. These strategies are not substitutes for professional treatment of depression, and they cannot eliminate the complex causes of mental illness, but they may give users more control over an environment that otherwise adapts continuously to their attention.

Parents and educators may also need to move beyond advice focused only on reducing screen time. For many teenagers, social media is woven into friendships, identity and everyday communication, making total avoidance difficult and sometimes socially isolating. Understanding how algorithms amplify patterns of attention could be more practical than treating every minute online as equivalent. The UT Dallas team is now collecting data from adolescents between 13 and 16 years old for a potential long-term study. Tracking participants over time could help determine whether algorithmically selected emotional content predicts later changes in mood, whether mood changes the content people receive, or whether both processes reinforce each other. Until such evidence is available, the current study provides an important warning: the most influential part of a social media feed may not be how long a person stays online, but the emotional world the algorithm keeps placing in front of them.

The method, called DCA2MC, was developed by a research team led by Professor Kazuhiro Ogata, together with Associate Professor Tsubasa Takagi and Senior Lecturer Canh Minh Do. It is designed for model checking linear temporal properties, a formal language used to describe how system behavior unfolds over time. Such properties can express requirements such as “a request is eventually acknowledged,” “a critical section is never entered by two processes simultaneously,” or “once an alarm is activated, the system will eventually reach a safe state.” Rather than treating the entire reachable state space as one massive verification problem, DCA2MC breaks it into smaller sub-state spaces that can be analyzed independently.

The underlying challenge is easier to appreciate by considering a concurrent system with several components, each capable of changing independently. Every combination of component states may represent a distinct global state, and every possible transition between those states can generate still more behaviors. Even a system with a modest number of variables can therefore produce millions or billions of reachable configurations. Traditional model checking explores this state graph while searching for a violation of the specified property. When the graph exceeds the computer’s memory, verification may stop before a definitive answer is obtained. DCA2MC addresses this state-space explosion by organizing the reachable behavior from an initial state into layers, effectively constructing a collection of smaller verification domains.

The researchers’ approach uses the tableau method, a formal procedure that transforms temporal-logic reasoning into structured sets of states and transitions. In DCA2MC, the reachable state space is divided according to layer configurations that determine how deeply the behavior is partitioned. Each resulting sub-state space contains a limited portion of the system’s possible evolution, allowing the model checker to examine it without loading the full state graph into memory. The process resembles dividing a vast map into independently searchable regions, except that the regions are created according to the system’s transition behavior and the logical structure of the property being checked. This division is not merely a practical shortcut: the researchers proved a theorem establishing that the collection of smaller model-checking problems is equivalent to the original problem.

That equivalence is critical. A verification method would be of limited value if it reduced memory use by silently overlooking behaviors that cross the boundaries between layers. According to the researchers, their theoretical result ensures that checking the independently generated sub-state spaces can preserve the answer that would be obtained by checking the original reachable state space as a whole. If any subproblem reveals a violation, the overall system fails the property; if all the required subproblems satisfy the property under the method’s formal conditions, the original model is also verified. The result gives DCA2MC a mathematical foundation rather than relying solely on empirical evidence that the partitioning appears to work.

To turn the concept into a usable verification system, the team implemented DCA2MC in Maude, a specification and programming language based on rewriting logic. Rewriting logic is well suited to describing systems whose states evolve through rule-based transformations, making it possible to represent both the system being analyzed and the operations used to divide its state space. DCA2MC supports sequential execution, in which subproblems are processed one after another, and parallel execution, in which independent tasks can run simultaneously on multiple processor cores. This structure could be particularly valuable for modern verification environments, where adding computational capacity is often easier than redesigning the underlying model-checking algorithm.

The tool can also work with external model checkers, including Spin, one of the best-known platforms for verifying concurrent and distributed software. This interoperability means that researchers and engineers may be able to apply DCA2MC’s decomposition strategy without rewriting the algorithms inside their preferred verification tools. In tests involving five mutual-exclusion protocols, the researchers compared DCA2MC with the Maude LTL model checker, Spin, and LTSmin. Mutual exclusion protocols are designed to ensure that competing processes do not enter a protected critical section at the same time, a fundamental requirement in operating systems, embedded controllers, communication protocols, and distributed computing.

The experiments indicated that DCA2MC completed several verification tasks that exceeded the memory capacity of other tools. In multiple case studies, it also required substantially less time than the Maude LTL model checker. The results suggest that dividing the state space can deliver two related benefits: each individual task becomes small enough to fit within available memory, and the tasks can be distributed across processors rather than being handled by a single monolithic computation. However, the researchers emphasize that performance depends on how the division is configured. A poor partition may produce too many subproblems, create unnecessary overhead, or fail to reduce the size of the most demanding portions of the state space.

To address that limitation, the team introduced a semi-automated procedure for identifying effective layer configurations. These configurations specify how the reachable space should be divided and how deep each layer should extend. DCA2MC includes custom commands that help users explore candidate configurations and identify those likely to reduce memory consumption and verification time. Experimental results showed that the procedure could find useful configurations without forcing users to rely entirely on trial and error. This feature is important for practical adoption because the best configuration may depend on the model’s structure, the temporal property under examination, the number of available processor cores, and the memory resources of the verification machine.

The researchers say the approach could help extend formal verification to larger and more complex systems, including autonomous vehicles, industrial control platforms, and safety-critical digital infrastructure. Model checking does not replace testing or engineering judgment, but it can examine classes of behaviors that are difficult to cover through conventional testing alone. By making exhaustive reasoning more scalable, DCA2MC may help developers detect synchronization failures, unsafe transitions, and violations of temporal requirements earlier in the design process. The method is not a universal solution to state-space explosion, and its effectiveness remains tied to suitable partitioning and the characteristics of each model. Nevertheless, the combination of a formal equivalence theorem, parallel computation, external-tool integration, and semi-automated configuration offers a promising route toward verification systems capable of confronting the rapidly expanding complexity of modern technology.

Subject of Research: Model checking and formal verification of software and hardware systems

Article Title: A Divide and Conquer Approach to Model Checking Linear Temporal Properties

News Publication Date: July 31, 2026

Web References: https://doi.org/10.1145/3836770

References: Canh Minh Do, Tsubasa Takagi, and Kazuhiro Ogata, “A Divide and Conquer Approach to Model Checking Linear Temporal Properties,” ACM Transactions on Software Engineering and Methodology. DOI: 10.1145/3836770

Image Credits: Prof. Kazuhiro Ogata from the Japan Advanced Institute of Science and Technology (JAIST)

Keywords: Model checking, formal verification, linear temporal logic, state-space explosion, divide-and-conquer algorithms, DCA2MC, Maude, Spin, LTSmin, software engineering, computer science, concurrent systems, mutual exclusion protocols

The research addresses a problem known as informative path planning. Unlike ordinary navigation, in which a vehicle travels from one known location to another, informative path planning requires a robot to choose a route that will collect valuable information. An unmanned aerial vehicle searching for a missing person, for example, must decide which regions to photograph, how to arrange those observations, and when the evidence gathered so far justifies changing direction. The vehicle is not merely moving through space; it is selecting a sequence of measurements that may reduce uncertainty about the world. Every decision therefore has two consequences: it determines where the robot will travel and it influences what the robot will know when it arrives.

This challenge is becoming more urgent as unmanned vehicles spread into military surveillance, package delivery, scientific exploration, precision agriculture, infrastructure inspection, and emergency response. Market research company Grand View Research estimates that the unmanned systems market was worth $29.3 billion in 2025 and could reach $67.6 billion by 2033. As these systems operate in more complex environments, they must cope with incomplete maps, changing conditions, limited battery capacity, communication delays, and the high cost of transmitting or processing data. A drone surveying a forest may have only a rough estimate of where a person is located. Images collected during the first part of the mission can alter that estimate, but using every new image to immediately calculate a completely new route may consume time and energy that the robot cannot afford to waste.

At one end of the planning spectrum is a fully adaptive strategy. In this model, the vehicle follows a route for a short period, receives new observations, and then recomputes its next move whenever information becomes available. If a drone’s camera detects a possible signal, an unusual shape, or an area that appears more promising than expected, the system can react immediately. This flexibility can produce highly effective routes because the vehicle continually responds to evidence. However, the computational burden can be substantial. Replanning requires algorithms to evaluate possible future paths, compare their expected information value, account for travel costs, and often communicate new instructions to the vehicle. In a rapidly changing mission, repeated calculations may become a bottleneck rather than an advantage.

At the opposite extreme is a nonadaptive strategy. The operator or planning system calculates a route before the mission begins, and the vehicle follows it regardless of what it discovers. This approach is easier to execute and may be faster because it avoids repeated optimization. It can also reduce communication demands and make battery consumption easier to estimate. Its weakness is that it treats the future as if it were already known. A search drone could continue scanning low-probability areas even after early observations indicate that the missing person is likely elsewhere. A utility-inspection robot might persist along a predetermined sequence after detecting evidence that the fault lies in another direction. The result may be operationally simple but scientifically and practically inefficient.

Ghuge, working with Rayen Tan and Viswanath Nagarajan of the University of Michigan, investigated a middle path between these two extremes. Their proposed framework limits adaptivity by dividing a mission into a small number of sequential rounds. During the first round, the robot follows a designated route without changing it in response to observations collected along the way. At the end of that round, the system uses the accumulated information to recompute the next route. The process can then be repeated, allowing later stages of the mission to become increasingly responsive without requiring continuous replanning. Technically, the method separates data collection from major optimization decisions. Instead of solving a new path-planning problem after every observation, the system solves it only at selected checkpoints.

The researchers tested this limited-adaptivity strategy through computerized simulations and compared it with a fully adaptive model. Their results indicate that two adaptive rounds could be about 15 times faster than a continuously adaptive approach. That acceleration came with a relatively small financial or operational penalty: the cost of the two-round strategy was only 12% higher than that of a comparable fully adaptive search. The simulations also showed that additional rounds produced diminishing returns. After the first three rounds, accuracy did not increase markedly, suggesting that constant route revision may deliver little extra value once the system has incorporated the most important early information. In practical terms, the robot may need only a few opportunities to reconsider its mission rather than an unbroken stream of decisions.

The reason this compromise works is that early observations often provide the largest improvements in situational understanding. Before a mission begins, the robot may face broad uncertainty about the location of a target or the source of a problem. The first sweep can eliminate large portions of the search area or reveal patterns that change the probability distribution over possible locations. A second route can then focus on the most promising regions. By the third round, the remaining uncertainty may be narrower, while the cost of further optimization continues to accumulate. Limited adaptivity attempts to capture the high-value information gained early in a mission while avoiding the computational and logistical expense of reacting to every minor change in the data.

The implications extend beyond search-and-rescue operations. A drone inspecting power lines could fly an initial route, analyze signs of damage, and then direct a second pass toward the structures most likely to contain the source of an outage. In agriculture, an autonomous vehicle could survey a field, identify areas with unusual moisture or plant stress, and allocate later measurements more intelligently. Scientific robots exploring remote terrain could use staged planning to balance coverage with the need to investigate anomalies. In each case, the system must optimize more than geographic distance. It must weigh the expected value of information against flight time, battery use, sensor operation, data-transfer requirements, and the consequences of delaying a response.

The study does not suggest that continuous adaptation is never useful. In missions involving rapidly changing hazards, extremely valuable targets, or sudden safety threats, immediate replanning may justify its computational cost. Instead, the findings indicate that adaptivity should be treated as a limited resource and scheduled strategically. A robot that changes its solution only two or three times may retain most of the performance advantage of a fully adaptive system while operating much faster and with fewer communication demands. For organizations deploying autonomous vehicles, that balance could determine whether a system remains theoretical or becomes practical. In a winter search for a missing hiker, minutes matter; a route that is slightly less precise but can be executed far sooner may ultimately offer the better chance of success. The research, published as “Informative Path Planning with Limited Adaptivity” in INFORMS Journal on Computing, provides a mathematical and computational foundation for making that trade-off deliberately rather than assuming that more frequent decision-making is always better.

Subject of Research: Limited-adaptivity algorithms for informative path planning by autonomous vehicles

Article Title: Informative Path Planning with Limited Adaptivity

News Publication Date: 27-May-2026

Web References: https://www.mccombs.utexas.edu/faculty-and-research/faculty-directory/profile/?username=rg53727 ; https://www.grandviewresearch.com/industry-analysis/unmanned-systems-market-report ; https://pubsonline.informs.org/doi/abs/10.1287/ijoc.2024.0893

References: INFORMS Journal on Computing, DOI: 10.1287/ijoc.2024.0893

Keywords: Computer science, algorithms, applied mathematics, business, autonomous vehicles, drones, informative path planning, adaptive robotics, search and rescue, unmanned systems

Rather than attempting to build one enormous quantum processor, HQAN researchers are developing modular quantum architectures. In this approach, multiple smaller quantum processing units, or QPUs, are connected so that they can operate as a coordinated system. The idea resembles the development of conventional computing, where memory, processors, storage and communication components are integrated instead of being forced into one monolithic device. For quantum computers, modularity could be especially valuable because different platforms excel at different tasks. Superconducting circuits can perform rapid operations, trapped or neutral atoms can offer long-lived quantum states and dense arrays, while optical systems can transport quantum information over distance. Connecting these technologies may provide a more realistic path to achieving quantum advantage than trying to scale a single platform indefinitely.

“The first phase of HQAN has made substantial progress in terms of both research advances and building the quantum workforce of the future,” said Brian DeMarco, an Illinois physics professor and the institute’s director and principal investigator. DeMarco said modular quantum computing was largely unexplored when the center began, but has since appeared on the technology roadmaps of major companies. He also emphasized HQAN’s regional role, highlighting its partnerships with the Chicago Quantum Exchange and its contributions to initiatives such as the Illinois Quantum Microelectronics Park. The institute brings together 45 senior researchers from six institutions, including Illinois, the University of Chicago, the University of Wisconsin–Madison, Northwestern University, Stanford University and MIT Lincoln Laboratory.

During its first five-year phase, NSF HQAN reported a series of advances spanning quantum hardware, networking, algorithms and communications. Researchers created entangled states across a four-node superconducting-circuit network, demonstrating that quantum correlations could be distributed among multiple connected modules. Entanglement is a distinctly quantum resource in which the state of one system is linked to the state of another, even when the systems are physically separated. Although entanglement cannot be used to transmit information faster than light, it is essential to distributed quantum computing, quantum sensing and secure communication. The center also achieved quantum-limited millimeter-wave-to-optical transduction using cold atoms coupled to a superconducting resonator, addressing a difficult interface problem between microwave-based processors and optical communication networks.

Other first-phase achievements focused on making modular machines controllable and useful. The team developed reconfigurable superconducting quantum-computing modules and demonstrated autonomous stabilization of remote entanglement in a network. Stabilization is critical because quantum states are fragile and easily disrupted by environmental noise, imperfect control and interactions with unwanted degrees of freedom. HQAN researchers also implemented the first algorithms on a small neutral-atom array and built atom-array modules containing more than 1,000 sites. In addition, they demonstrated a two-species neutral-atom array with gates between different atomic species and realized quantum secret sharing in a triangular superconducting modular processor. The institute says its researchers have published more than 210 peer-reviewed papers to date.

The second phase will focus on closing the gap between individual demonstrations and a complete modular quantum-computing system. Researchers plan to perform basic computational operations, known as application primitives, across modular platforms. These primitives are the building blocks from which larger applications can be assembled, including simulations, optimization routines and scientific calculations. The program will also lay foundations for software capable of coordinating distributed QPUs, including algorithms, compilers and quantum-error-correction protocols. A compiler for a modular quantum computer must do more than translate instructions into pulses: it must decide where operations should occur, how quantum states should move between modules and how communication delays and hardware differences should be managed.

Quantum error correction will be central to that effort. Quantum information is vulnerable to errors caused by decoherence, control imperfections and thermal fluctuations. Unlike classical bits, quantum bits cannot simply be copied to create backups because of the no-cloning theorem. Instead, quantum-error-correction schemes distribute information across many physical qubits so that errors can be detected and corrected without directly measuring the encoded quantum state. In a modular architecture, the problem becomes even more complicated because errors can arise not only inside individual QPUs but also in the interconnects that link them. HQAN will therefore develop improved interfaces for transmitting quantum information, while studying chip-scale integration, more energy-efficient quantum photonics and compact methods for generating entanglement between distant modules.

The renewed center will include 16 industry partners, among them Google, IBM, IonQ and Quantinuum. Their participation reflects a growing consensus across the quantum sector that useful machines will likely depend on interconnected components rather than unlimited expansion of one hardware platform. “Illinois has made a bold commitment to becoming a global leader in quantum technology,” said Rashid Bashir, dean of the Grainger College of Engineering, where NSF HQAN is hosted. Bashir said the collaboration would advance the architectures required to make quantum computing scalable and useful while strengthening the talent and innovation networks needed to support the emerging industry. Preeti Chalsani, Illinois’ chief quantum officer, described HQAN as a driver of quantum research and workforce development for the state, the Midwest and the nation.

The institute’s ambitions extend beyond laboratories and corporate partnerships. Its education programs have brought quantum science to more than 12,000 participants, including students and teachers across the United States. TeachQuantum gives educators a six-week research experience followed by a year of curriculum-development support, while Wonders of Quantum Physics brings quantum concepts into classrooms through demonstrations, hands-on activities and inquiry-based learning. HQAN also trains graduate students and postdoctoral researchers for careers in academia, national laboratories and industry. The center reports that 27 alumni have moved into high-profile industry positions, 17 have accepted faculty roles and nine have joined national laboratories, illustrating how rapidly demand is growing for specialists who understand both quantum physics and engineering.

The renewed program arrives as governments and companies compete to turn decades of fundamental research into practical quantum technologies. Brian Stone, performing the duties of NSF director, said the agency’s long-term investments in quantum science, sensing and communication had created a foundation for more focused efforts. HQAN’s next phase will attempt to transform that foundation into a coherent pathway for modular quantum computing, combining hardware, networking, software and workforce development. The institute’s researchers will work alongside a related NSF institute, the Quantum Leap Challenge Institute for Physics and Engineering of Practical Quantum Error Correction, led by Yale University. Illinois physics professor Wolfgang Pfaff, who is a member of both initiatives, will contribute expertise in superconducting quantum circuits to efforts aimed at identifying and correcting errors in real quantum systems. If the program succeeds, quantum advantage may emerge not from a single spectacular processor, but from a coordinated network of specialized machines working together.

Subject of Research: Modular quantum computing, quantum networking, quantum interconnects, quantum error correction and workforce development.

Article Title: NSF Renews Illinois-Led Quantum Institute With $37.5 Million to Build Networked Quantum Computers

Web References:
https://www.nsf.gov/news/eight-nsf-research-institutes-propel-us-quantum-science-290m
https://hqan.illinois.edu/
https://physics.illinois.edu/people/directory/profile/bdemarco
https://ece.illinois.edu/about/directory/faculty/rbashir
https://physics.illinois.edu/people/directory/profile/wpfaff

Image Credits: Brian Stauffer, University of Illinois Urbana-Champaign; The Grainger College of Engineering at the University of Illinois Urbana-Champaign.

Keywords: Quantum computing, quantum networking, modular quantum architectures, quantum processors, quantum information science, quantum error correction, superconducting circuits, neutral atoms, quantum photonics, NSF HQAN.