The award places FAU among a select group of institutions. It is one of only nine organizations in eight states chosen for NIST’s Regional Alliances and Multistakeholder Partnerships to Stimulate, or RAMPS, Cybersecurity Education and Workforce Development program. The RAMPS model is deliberately regional: rather than funding isolated courses, it builds on existing workforce partnerships to connect employers, educators and community organizations so that local cybersecurity education translates directly into local career pathways. In the case of FAU, that means knitting together a university with growing quantum capabilities, a major quantum computing company, school districts, utilities, federal law enforcement outreach networks and community organizations into a single talent pipeline.

Timing is central to the project’s logic. Quantum computing is advancing from laboratory curiosity toward practical application, and with that transition comes a class of cybersecurity challenges that today’s workforce is largely unprepared to address. Quantum systems will not merely accelerate computation; they will change the assumptions on which much of modern cryptography and infrastructure protection rests. At the same time, the emergence of quantum techniques creates new attack surfaces and new defensive tools that security professionals will need to understand. Yet, as FAU researchers note, few workforce development programs anywhere provide hands-on training that integrates quantum computing, cybersecurity and critical infrastructure protection in one curriculum. The FAU project, titled Building the Quantum Cybersecurity Workforce in the Palm Beach and Treasure Coast Region, is designed to fill precisely that gap.

A distinguishing feature of the initiative is the hardware behind it. Through its partnership with D-Wave Quantum Inc., a publicly traded quantum computing company, FAU will become the first university in Florida to publicly host a dedicated annealing quantum computer, a D-Wave Advantage2 system with more than 4,400 qubits, on its Boca Raton campus. Annealing quantum computers differ from the gate-based machines developed by other vendors; they are engineered specifically for optimization problems, exploring vast solution spaces to find configurations that minimize a defined cost function. That makes them directly relevant to problems in power grid management, logistics and scheduling, and it also makes them a realistic platform on which students can study how quantum systems behave, how they can be secured and how they might be used or misused in attacks on infrastructure.

Stella Batalama, dean of FAU’s College of Engineering and Computer Science, framed the award as a natural extension of the university’s existing investments. According to Batalama, the NIST award builds directly on Florida Atlantic’s commitment to quantum computing and the college’s strengths in cybersecurity, artificial intelligence, advanced computing and power infrastructure systems. With the D-Wave Advantage2 quantum computer arriving on the Boca Raton campus, she said, the university is building the infrastructure, expertise and workforce needed for the next generation of computing, and the new initiative extends that investment into quantum cybersecurity by giving students and professionals hands-on experience with emerging technologies while preparing the talent needed to protect critical systems.

The project will be led by Yufei Tang, principal investigator, associate professor in FAU’s Department of Electrical Engineering and Computer Science, director of the FPL Center for Intelligent Energy Technologies, known as InETech, and faculty fellow of FAU’s Institute for Smarter Cities, Spaces, and Health. Co-principal investigators are Hari Kalva, chair and professor, and Zebo Yang, assistant professor, both from the same department. Tang’s dual role is telling: the InETech Center provides a real-world research and training environment for power and energy systems, including advanced simulation and hardware-in-the-loop capabilities, which means participants can experiment on faithful replicas of grid infrastructure rather than abstract models. Hardware-in-the-loop testing, in which physical control devices are connected to simulated power systems in real time, is a standard technique for validating grid cybersecurity, and coupling it with quantum computing resources is an unusual and technically ambitious combination.

Tang described the educational challenge in stark terms. Quantum computing, he said, is changing not only what computers can do but also what cybersecurity professionals need to know. The project will bring together quantum computing, cybersecurity and critical-infrastructure expertise to give participants practical experience with technologies and security challenges that are emerging now. By combining hands-on training, real-world case studies, access to quantum computing resources and direct engagement with industry and government partners, the team aims to produce a workforce prepared to secure systems in a quantum-enabled future. That formulation reflects a growing consensus among standards bodies, including NIST itself, that the transition to post-quantum cryptography and quantum-aware security practices will require years of preparation across both the public and private sectors.

D-Wave’s involvement signals the industry dimension of the effort. Allison Schwartz, the company’s senior vice president of global government relations and public affairs, said D-Wave is proud to partner with FAU on preparing the next generation of quantum and cybersecurity professionals. As quantum computing moves from research laboratories into real-world applications, she noted, developing a workforce that understands both the opportunities and the cybersecurity implications of these technologies is essential. By combining D-Wave’s quantum technologies with FAU’s academic strengths and regional partnerships, the program is intended to create a pipeline of talent equipped to address the challenges and opportunities of the quantum era. For students, access to a production-grade annealing system rather than a cloud simulator represents a meaningful difference in training fidelity.

The consortium assembled around the project is unusually broad. Key partners include D-Wave; the InfraGard South Florida Members Alliance, which connects the FBI with private-sector critical infrastructure stakeholders; the School District of Palm Beach County; Broward County Public Schools; and Alexander D. Henderson University School, FAU’s public laboratory school serving elementary and middle school students. The inclusion of K-12 partners means the program is not aimed solely at university students and working professionals but also at early pipeline development, introducing younger students to quantum and cybersecurity concepts before they reach college. On the professional side, the project will build on FAU’s established cybersecurity workforce programs, including its National Science Foundation-supported CyberCorps Scholarship for Service and CyberTraining initiatives, layering quantum content onto proven structures.

Concretely, the initiative will deliver a hands-on Quantum Cybersecurity Workshop Series along with reusable curriculum, case studies and online training materials aligned with the NICE Workforce Framework for Cybersecurity and CyberSeek, the federal tools that standardize cybersecurity roles, skills and career pathways. Participants will also have access to mentoring, internships and employer engagement, while a regional consortium will connect universities, K-12 schools, industry, government, utilities and community organizations across the served counties. Crucially, the NIST-funded project is designed to outlast the grant itself: the shared curriculum, online training, employer engagement mechanisms and the regional consortium are intended to establish a lasting framework for quantum cybersecurity workforce development in South Florida. For FAU, the award reinforces a broader ambition. The university, which serves more than 32,000 students and holds R1 research status, has positioned itself as Florida’s first quantum university, and through its Center for Quantum Technologies it continues to bring together faculty, students and industry partners to advance quantum research and prepare the workforce for an emerging quantum economy in which security, not just computation, will be the defining challenge.

Subject of Research: Quantum cybersecurity workforce development funded by a NIST grant at Florida Atlantic University

Article Title: FAU receives NIST grant to build South Florida’s quantum cybersecurity workforce

Article References: FAU receives NIST grant to build South Florida’s quantum cybersecurity workforce. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: quantum computing, cybersecurity, NIST, Florida Atlantic University, D-Wave, workforce development, critical infrastructure, post-quantum cryptography, STEM education, South Florida, annealing quantum computer, power systems

The research, conducted by Seon-Geun Jeong, Dinh Cong Mai, Dae-Il Noh, Quoc-Viet Pham, and Won-Joo Hwang, addresses a fundamental problem that has haunted quantum annealing since its commercialization by D-Wave Systems. Quantum annealing is a heuristic realization of adiabatic quantum computation, in which a quantum system is slowly evolved from an easily prepared initial state toward a final Hamiltonian whose ground state encodes the solution to a combinatorial optimization problem. In theory, if the evolution is slow enough, the system remains in its instantaneous ground state and emerges with the optimal answer. In practice, finite annealing times, thermal fluctuations, and hardware imperfections turn the process into a statistical sampling exercise rather than a guaranteed convergence procedure.

The core difficulty lies in what physicists call minor embedding. D-Wave processors, from the earlier Chimera topology through the Pegasus generation to the newest Zephyr architecture, arrange superconducting flux qubits in sparse but structured connectivity graphs. The Zephyr topology, for example, supports up to 7440 qubits in a graph with maximum degree 20, a substantial improvement over its predecessors, but still nowhere near fully connected. When a logical problem graph, such as a fully connected clique representing interactions among many variables, must be mapped onto this hardware, each logical variable is represented by a chain of ferromagnetically coupled physical qubits. The longer these chains become, the more vulnerable they are to what researchers call chain breaks, events in which the physical qubits within a chain disagree, destroying the correspondence between the logical and physical representations of the problem.

Previous studies of embedding have been largely empirical, measuring clique sizes, approximation ratios, and chain break fractions across hardware generations without offering a predictive theoretical framework. The new work changes that by extending the integrated control error (ICE) model, a statistical description of local field and coupler errors commonly used in D-Wave documentation, into a rigorous analytic tool. The researchers distinguish between a moment-level uncorrelated ICE baseline, which requires only that errors have zero mean and finite variance with negligible covariance terms, and a Gaussian closure that converts accumulated variance into a closed-form expression for chain break probability using the complementary error function. This careful separation clarifies exactly which results depend on which assumptions, providing a transparent baseline against which real hardware behavior can be measured.

At the heart of the framework is a deceptively simple variance law. When errors on individual qubits and couplers are independent, the variance of the accumulated perturbation along a chain grows linearly with chain length. Since clique embeddings on Zephyr hardware produce chain lengths that grow approximately linearly with problem size, verified experimentally with a fitted slope of about 0.124 per logical variable and a coefficient of determination of 0.974, the model predicts precisely how embedding overhead translates into noise overhead. The framework further derives a design rule: to maintain a fixed target break probability, the intra-chain coupling strength, known as chain strength, should scale as the square root of chain length under the independent-noise assumption. This square-root law has long been treated as the reference for chain-strength tuning in practice.

To test these predictions, the team ran systematic experiments on the D-Wave Advantage2 system with Zephyr topology, embedding fully connected random QUBO instances ranging from 5 to 105 logical variables and sweeping annealing times from 5 to 200 microseconds and chain strengths from 0.1 to 2.5. Each data point aggregated 20,000 samples across 10 independent QPU replicates. The calibrated baseline model successfully captured the dominant growth trend of the chain break fraction with embedding size, and held-out validation splits showed that the fit was not merely memorizing calibration points. Fitted effective parameters remained within comparable ranges across annealing schedules, and longer annealing times systematically suppressed chain breaks while leaving the fundamental scaling with embedding size intact.

Then came the surprise. When the researchers extracted the empirical critical chain strength, the minimum chain strength needed to keep chain breaks below a given tolerance, and examined how it scales with average chain length, the data told a different story than the square-root law. The fitted power-law exponent ranged from 0.805 at a permissive 5 percent chain-break tolerance to 1.006 at a strict 1 percent tolerance, with all 95 percent bootstrap confidence intervals decisively excluding the independent-noise prediction of 0.5. In other words, real hardware accumulates errors faster than independent noise would allow, requiring chain strength to grow substantially more steeply, approaching linear growth under strict tolerance requirements, than the idealized baseline suggests.

To explain this deviation, the researchers proposed a correlated-variance extension, adding a term to the variance that scales superlinearly with chain length. When fitted directly to the critical chain strength data, this extension recovered a correlation exponent of approximately 1.8, in excellent agreement with the prediction that the exponent should equal twice the empirical chain-strength exponent. Model comparison using the Akaike information criterion favored the correlated extension over the independent baseline at all three tolerance thresholds. The physical origin of this correlated component remains an open question, with candidates including calibration drift, low-frequency flux noise, crosstalk between neighboring qubits, systematic offsets in programmed Hamiltonians, or non-Gaussian error tails, all of which are known to plague analog quantum processors.

The practical implications are significant for anyone deploying quantum annealing at scale. The framework provides quantitative guidance for embedding-aware chain-strength selection, replacing heuristic sweet-spot searches with principled scaling predictions. It also enables more realistic embedding-aware simulation of quantum annealers, allowing researchers to anticipate how reliability will degrade as problems grow before ever touching the hardware. Under time-matched budget comparisons, the quantum annealer delivered solution quality competitive with simulated annealing, the open-source PuLP solver, and the commercial Gurobi optimizer on the tested dense instances, matching certified Gurobi optima for problem sizes up to 60 logical variables, though the authors are careful to frame this as a controlled benchmark rather than a claim of quantum speedup.

Looking forward, the authors emphasize that their model is a first-order baseline, not a complete microscopic theory of annealing hardware. The fitted parameters should be interpreted as effective, schedule-dependent quantities rather than universal hardware constants, and broader validation across independently generated instances, alternative embeddings, gauge transformations, and future device generations remains essential work. Yet the message is clear and compelling: the noise that limits quantum annealing is not a collection of independent random insults but carries a correlated structure that grows with problem size, and any serious effort to scale quantum annealing toward commercially relevant optimization must confront that correlation head-on. By turning embedding overhead from an empirical nuisance into a quantifiable, predictable quantity, this work provides the analytical foundation on which the next generation of noise-aware quantum annealing algorithms and hardware-conscious mitigation strategies can be built.

Subject of Research: Embedding-aware noise modeling of quantum annealing on superconducting quantum annealers

Article Title: Embedding-aware noise modeling of quantum annealing

Article References: Jeong, S.-G., Cong Mai, D., Noh, D.-I., Pham, Q.-V., & Hwang, W.-J. (2026). Embedding-aware noise modeling of quantum annealing. Quantum Information Processing, 25(10), Article 315. https://doi.org/10.1007/s11128-026-05293-z

Image Credits: AI Generated

DOI: 10.1007/s11128-026-05293-z

Keywords: quantum annealing, quantum computing, D-Wave, chain breaks, minor embedding, integrated control error, noise modeling, combinatorial optimization, Zephyr topology, quantum information, Embedding-aware, noise