NY Creates, the Albany-based New York Center for Research, Economic Advancement, Technology, Engineering, and Science, has announced its participation in the newly funded Manufacturable and Resilient superconducting QUantum Information Systems, or MARQUIS, Quantum Leap Challenge Institute. The institute is a flagship program of the U.S. National Science Foundation and is led by Princeton University. According to the agency, MARQUIS is receiving $27.9 million in NSF funding over five years, and NY Creates will receive approximately $1.25 million of that total to support research, technology scaling, and workforce development activities aimed at advancing quantum computing technologies.

The central obstacle MARQUIS confronts is one that has shadowed superconducting quantum computing since its earliest days: the materials and fabrication techniques underpinning today’s qubits have barely evolved in decades. Nathalie de Leon, professor of electrical and computer engineering at Princeton University and director of the new institute, described the problem bluntly. “The whole community has been using essentially the same materials technology for about a quarter century,” she said. “That technology has worked well for experimental prototypes and small-scale systems. But to build quantum computers at a scientifically useful scale, the most basic elements must be reinvented.” She added that having a few key experts in the field who know what the right waypoints are and how to think about it is really crucial.

Understanding why this reinvention is necessary requires a brief look at how superconducting quantum computers work. At the heart of these machines are qubits, the quantum analogues of the bits in classical computing. Unlike ordinary bits, which must be either zero or one, qubits can exist in superpositions of both states simultaneously, and they can become entangled with one another in ways that allow certain calculations to be performed with dramatically fewer resources than classical machines would require. Superconducting qubits, the approach pursued by MARQUIS, rely on tiny electrical circuits cooled to temperatures near absolute zero, where resistance vanishes and quantum effects dominate. These circuits incorporate Josephson junctions, nanoscale sandwich structures of superconducting materials separated by insulating barriers, which act as the nonlinear elements that make quantum computation possible.

The trouble is that these junctions and the materials surrounding them are exquisitely sensitive. Microscopic defects, impurities, and inconsistencies introduced during fabrication can cause decoherence, the process by which fragile quantum states leak information to their environment and collapse into useless classical noise. As researchers scale from processors with dozens or hundreds of qubits to the thousands or millions needed for scientifically and commercially meaningful computation, the variability of fabrication becomes an increasingly punishing bottleneck. A process that works acceptably for a handful of devices in a university cleanroom may fail catastrophically when applied across an entire wafer of thousands of components.

This is precisely where NY Creates brings distinctive value to the consortium. The organization operates advanced 300-millimeter semiconductor research and development infrastructure, the wafer format that dominates industrial chip manufacturing worldwide. As part of MARQUIS, the NY Creates team, led by Dr. Satyavolu Papa Rao, Senior Director of Emerging Technologies and Research at the organization, will work with institute partners to test innovative materials and processes at the 300-millimeter wafer scale, targeting mid-scale superconducting quantum processors. Working at wafer scale means that new materials and fabrication recipes can be evaluated under conditions that closely resemble real industrial production, rather than the small-batch conditions typical of academic laboratories.

“Creates is uniquely positioned to help bridge the gap between breakthrough quantum research and scalable manufacturing,” Papa Rao said. “Through our advanced 300mm semiconductor R&D infrastructure and deep industry partnerships, we will work alongside MARQUIS collaborators to evaluate, translate, and scale promising quantum materials and processes, helping to accelerate the development of manufacturable quantum technologies while strengthening the nation’s quantum workforce and semiconductor innovation ecosystem.”

Beyond testing new materials, the NY Creates team will collaborate with MARQUIS researchers to identify critical data and process insights that could improve the scalability of materials and fabrication approaches for quantum devices within industry-standard CMOS-compatible manufacturing environments. CMOS, the complementary metal-oxide-semiconductor technology that underpins virtually all modern electronics, represents the gold standard for manufacturability. Ensuring that quantum device innovations remain compatible with CMOS-style processes is a strategic bet: it means that whatever succeeds in the laboratory has a plausible path into the enormous existing infrastructure of the semiconductor industry, rather than requiring an entirely new fabrication ecosystem to be built from scratch.

The institute’s ambitions extend well beyond fabrication alone. NY Creates will also support MARQUIS efforts to develop databases of materials, processes, and device structures that can help researchers better understand the factors influencing quantum device performance, decoherence, and process variability. Such databases could prove transformative for the field, which has historically relied on scattered, hard-to-compare experimental results. Systematic, shared data on how specific material combinations and processing steps affect qubit coherence times and yield would allow researchers to iterate far more quickly, applying the kind of data-driven optimization that revolutionized classical semiconductor development over past decades.

Workforce development forms a third pillar of NY Creates’ contribution, and it addresses a widely recognized vulnerability in the American quantum enterprise. The quantum industry is growing faster than the supply of people trained to work in it, and the specialized skills required, spanning quantum physics, cryogenic engineering, materials science, and precision fabrication, are scarce. Through MARQUIS, NY Creates will work with institute partners to expand opportunities for students and postdoctoral researchers to gain hands-on experience with advanced semiconductor manufacturing environments through periodic site visits and collaborative learning experiences. The goal is to cultivate a generation of scientists, engineers, and technologists who are equally comfortable with quantum theory and with the practical realities of high-volume chip fabrication.

The breadth of the consortium reflects the scale of the challenge. The Princeton-led MARQUIS institute brings together researchers from Princeton University, Cornell University, the Massachusetts Institute of Technology, the University of California at Santa Barbara, Stanford University, Dartmouth College, NY Creates, Michigan State University, and the University of Iowa. These institutions contribute expertise across materials science, quantum devices, and semiconductor processing, spanning two dozen laboratories in total. The initiative is further supported by an advisory board that includes representatives from Google Quantum AI, NVIDIA, Applied Materials, Oxford Instruments, Bluefors, KU Leuven/imec, and MIT Lincoln Laboratory, a lineup that spans the leading quantum computing companies, the dominant suppliers of semiconductor fabrication equipment, and the major providers of cryogenic systems essential to superconducting quantum hardware.

Dave Anderson, President and CEO of NY Creates, framed the award as a milestone for both the organization and the region. “This award reflects the growing importance of integrating world-class semiconductor manufacturing expertise with leading-edge quantum research,” Anderson said. “As a partner in this exceptional consortium of leading universities, industry partners, and research institutions, Creates will help advance technologies that are critical to the future of quantum computing and America’s scientific and economic competitiveness. The MARQUIS initiative also reinforces New York’s leadership in semiconductor innovation while creating valuable opportunities to educate and train the next generation of scientists, engineers, and technologists who will drive quantum-based economic growth.”

The stakes for the United States are considerable. Quantum computing is widely viewed as a strategic technology, one with potential implications for national security, pharmaceutical development, financial modeling, and materials design. Nations around the world, including China and members of the European Union, have committed billions of dollars to quantum research and development, and leadership in the field is increasingly seen as inseparable from broader economic and technological competitiveness. By pairing the fundamental scientific expertise of its leading universities with the manufacturing know-how embedded in organizations like NY Creates, the MARQUIS institute represents a deliberate attempt to ensure that American quantum discoveries do not stall at the prototype stage but instead flow into scalable, domestic production.

MARQUIS is one of several Quantum Leap Challenge Institutes established by the National Science Foundation as part of a broader federal push in quantum science, and it embodies a philosophy that is gaining traction across the field: the bottleneck to useful quantum computers is no longer only a matter of physics but also of engineering discipline, manufacturing rigor, and human capital. If the institute succeeds in reinventing the basic elements of superconducting qubits and embedding them in CMOS-compatible, wafer-scale processes, the consequences could ripple far beyond the laboratory. Quantum processors that can be fabricated reliably, in quantity, and with predictable performance would move the field decisively closer to the day when quantum machines tackle problems no classical computer ever could, and the groundwork for that transition is now being laid, one wafer at a time, in Albany and its partner institutions across the country.

Subject of Research: Manufacturable superconducting quantum computing hardware and fabrication

Article Title: NY Creates joins Princeton-led NSF Quantum Institute to advance next-generation quantum computing

Article References: NY Creates joins Princeton-led NSF Quantum Institute to advance next-generation quantum computing. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: quantum computing, superconducting qubits, MARQUIS, National Science Foundation, Princeton University, NY Creates, semiconductor manufacturing, 300mm wafer, CMOS compatibility, decoherence, workforce development, Josephson junctions

John George Francis and Anil Shaji focused on transmon qubits, the workhorse of IBM’s quantum processors and many other superconducting platforms. Transmons are engineered from Josephson junctions and superconducting circuits to be relatively insensitive to charge noise, a design choice that has made them the most widely deployed qubit technology in the world. Yet even the best-fabricated transmons deviate from the idealized textbook description. Stray couplings, higher energy levels, and control imperfections all conspire to make the actual dynamics of the system richer and messier than the standard Hamiltonian predicts.

The team targeted one of the most important operations in the superconducting quantum computing repertoire: the cross-resonance gate. In this scheme, microwaves are applied to a target qubit at the frequency of its control qubit neighbor, creating an entangling interaction that serves as a fundamental building block for two-qubit logic. The cross-resonance interaction has a well-known effective Hamiltonian description, developed in earlier theoretical work, but that description is itself an approximation whose accuracy depends on the details of the hardware and the drive parameters. When the idealized model is used to predict what a real gate does, errors accumulate quickly.

To quantify those errors and correct them, Francis and Shaji turned to real experimental data from ibm_kyiv, one of the processors accessible on the IBM Quantum cloud platform. They collected measurement outcomes for two transmon qubits driven by microwave pulses of varying amplitudes and evolution times, spanning a range of operating conditions. Each data point captured the probabilities of the different computational basis states after the system evolved under the cross-resonance drive, providing a rich empirical fingerprint of the true dynamics that no purely theoretical model could be expected to reproduce exactly.

The heart of their method is a correction operator added to the standard Hamiltonian. Rather than guessing the correction from first principles, the researchers wrote down an ansatz, a flexible mathematical form whose matrix elements are free parameters to be optimized. The goal of the optimization is simple to state and hard to achieve: find the correction terms that make the corrected Hamiltonian’s predicted evolution match the observed hardware data as closely as possible across all the measured amplitudes and times. Because the ansatz is not unique, different parameter choices can yield equally good fits, so the team developed methods to characterize an entire equivalence class of effective corrections rather than a single answer.

Optimizing parameters inside a differential equation is exactly the kind of problem that scientific machine learning was built to solve. The researchers employed adjoint sensitivity analysis, a technique that efficiently computes how the final outcome of a dynamical simulation changes with respect to every parameter in the model, without the prohibitive cost of naive approaches. Combined with gradient descent, this allowed the correction terms to be tuned iteratively until the simulated and measured probability distributions converged. The approach draws on the framework of universal differential equations, in which physical models are embedded in differentiable programs that machine learning tools can optimize end to end.

The results are striking. With the uncorrected Hamiltonian, the worst-case average absolute error in predicting the final measurement probabilities reached 0.48, meaning the theoretical model could be wrong by nearly half a full probability unit, a level of disagreement that would render any gate calibration based on the model unreliable. After the data-driven correction was applied, that worst-case error dropped below 0.05, an improvement of nearly a factor of ten. The corrected Hamiltonian tracked the survival probabilities of all four initial two-qubit states across the full range of pulse amplitudes tested, reproducing the fine structure of the hardware’s time evolution that the standard model missed entirely.

Why does this matter beyond one pair of qubits on one machine? Gate fidelities in today’s quantum processors are limited not only by decoherence but by systematic modeling errors, and as the field marches toward error-corrected, fault-tolerant machines, every fraction of a percent of gate accuracy counts. Accurate Hamiltonian models are also the foundation of optimal control techniques, which design pulse sequences to implement gates as faithfully as possible. If the underlying model is wrong, even the most sophisticated pulse optimization will steer the quantum system to the wrong destination. A data-driven correction loop, in which the model continuously learns from the hardware it describes, offers a path to calibrations that stay faithful as devices drift and age.

The work also highlights a broader shift in how quantum engineers think about the relationship between theory and experiment. Traditional approaches either derive effective Hamiltonians perturbatively from circuit theory or fit a handful of parameters to targeted experiments. The scientific machine learning approach is more ambitious: it treats the Hamiltonian itself as a learnable object, constrained by physics but refined by data, and it embraces the fact that multiple effective descriptions may be equally valid. That equivalence-class perspective is philosophically interesting, but it is also practically useful, because it tells engineers which features of a correction are physically meaningful and which are artifacts of a particular parameterization.

Francis and Shaji’s study, published in Quantum Information Processing, was supported in part by India’s National Quantum Mission and used the high-performance computing facilities at IISER Thiruvananthapuram. The underlying data have been made openly available, allowing other groups to test and extend the method on different hardware platforms. As quantum processors scale to hundreds and eventually thousands of qubits, manual calibration of every interaction will become impossible, and techniques like this one, which let the machine teach itself the physics of its own imperfections, may become as essential to quantum computing as the qubits themselves.

Subject of Research: Data-driven correction of the cross-resonance Hamiltonian of transmon qubits using scientific machine learning

Article Title: Data-driven Hamiltonian correction of transmon qubits

Article References: Francis, J. G., & Shaji, A. (2026). Data-driven Hamiltonian correction of transmon qubits. Quantum Information Processing, 25(10), Article 328. https://doi.org/10.1007/s11128-026-05350-7

Image Credits: AI Generated

DOI: 10.1007/s11128-026-05350-7

Keywords: transmon qubits, Hamiltonian correction, cross-resonance gate, scientific machine learning, IBM Quantum, quantum computing, gradient descent, adjoint sensitivity, quantum control, superconducting qubits, gate fidelity, quantum information processing