Speakers

Achieving quantum advantage within the next two years is a milestone that will only be possible through close collaboration between the quantum compute resources and high-performance computing (HPC) —especially given the substantial resource demands of scaling quantum circuits beyond the 100-qubit regime. Now that we’ve entered the era of quantum utility, marked by quantum systems capable of outperforming classical simulators in meaningful tasks, the path to reliable quantum advantage requires continued innovation across hardware, software, and algorithm design.

In this talk, I will highlight recent progress along the IBM Quantum development roadmap, including advances in utility-scale processors, quantum-centric supercomputing, and the Qiskit software stack. I will take a deeper look at Sample-based Quantum Diagonalization (SQD) and its variants, which not only demonstrate the growing capabilities of quantum systems but also underscore the essential role of HPC in realizing scalable, noise-resilient quantum algorithms.

The ability to perform ab initio molecular dynamics simulations using potential energies calculated on quantum computers would allow virtually exact dynamics for chemical and biochemical systems, with substantial impacts on the fields of catalysis and biophysics. However, noisy hardware, the costs of computing gradients, and the number of qubits required to simulate large systems present major challenges to realizing the potential of dynamical simulations using quantum hardware. Here, we demonstrate that some of these issues can be mitigated by recent advances in machine learning. By combining transfer learning with techniques for building machine-learned potential energy surfaces, we propose a new path forward for molecular dynamics simulations on quantum hardware.

Over the last 20 years, quantum information science (QIS) at ORNL his grown from a niche effort to a major priority involving many dozens of researchers across the laboratory. In this talk I will survey the ORNL approach to QIS with an emphasis on recent research activities in the Quantum Computational Science group. 

Phil Emer has spent nearly 35 years working at the intersections of network technology, research, academia and business – splitting time between the public and private sectors. In the private sector Phil has worked as a technical business development manager with Amazon Web Services (AWS), as an engineer with IBM, as a technology executive with venture-backed Carolina Broadband, and as a member of the leadership team with not-for-profit MCNC.  In the public sector Phil directed voice, video, and data communications at NC State University and served as Senior Director of Technology Planning and Policy with the Friday Institute for Educational Innovation.

Phil currently serves as Vice President Business Development at MCNC, a North Carolina not-for-profit corporation that functions as the single State education service agency supporting K-20 with connectivity, cybersecurity, hosting, and related technology services.

Phil has served on the NC eLearning Commission, the advisory board of the Institute for
Next Generation IT Systems, the architecture review board of the Smarter Balanced
Assessment Consortium, the NC Innovation Council, and has served as adjunct faculty in the department of computer science at NC State University. Currently, he serves on the Board of the Wireless Research Center of North Carolina.

Phil earned a B.S. in electrical engineering at Virginia Tech and an M.S. in computer engineering at NC State University.

This talk will address the role of MCNC and the North Carolina Research and Education Network (NCREN) in supporting quantum computing and communications related research and development. The talk also describes the deployment of a timing and synchronization infrastructure overlay network that will be used to synchronize quantum network nodes.

Sertalp B. Çay is a Principal Operations Research Specialist at SAS Institute, where he develops optimization-based solutions for complex business problems. He specializes in working with state-of-the-art tools and algorithms, building fast prototypes, and modeling real-life challenges. As the technical expert for quantum optimization efforts at SAS, he has worked on leveraging quantum optimization tools to solve real-world business problems, bridging the gap between emerging technologies and practical applications.

He holds a PhD in Industrial Engineering from Lehigh University, where his research focused on developing efficient methods for solving second-order cone optimization problems. A strong advocate for open-source solutions, Sertalp is the lead developer of SAS’s Python optimization modeling package, sasoptpy, which provides an interface for SAS Viya optimization solvers.

Beyond his professional work, Sertalp is deeply involved in sports analytics. He has played a key role in expanding the use of analytics in the Fantasy Premier League (FPL) community, developing optimization tools, publishing tutorials and blog posts, and managing an active online community. He also maintains FPL Optimized, an award-winning website dedicated to data-driven fantasy football decision-making.

Outside of work and analytics, Sertalp is a dedicated husband and father, always seeking ways to optimize not just fantasy teams and algorithms, but also time with his family.

Prescriptive analytics represents the final phase of the analytics lifecycle, offering optimal courses of action based on data. As a global leader in data and AI, SAS provides a range of optimization solvers to address prescriptive analytics applications. With advancements in quantum computing, there is potential to leverage this technology for solving complex optimization problems. This research collaboration between SAS and a Consumer-Packaged Goods company aims to explore the opportunities and limitations of both classical and quantum solvers in addressing a particularly challenging business use case. Ultimately, SAS developed a hybrid approach that integrates a quantum solution with our classical solver for further improvement.

Dr. Larry M. Deschaine serves as Team Lead for Data Sciences,
specializing in Quantum AI/ML and Optimization at the U.S. Department
of Energy’s Savannah River National Laboratory (SRNL). He leads
cutting-edge research at the intersection of quantum computing,
artificial intelligence, and complex systems optimization. He has
written over 100 quantum computing programs for a wide variety of
applications and is the workforce development instructor at SRNL for
both quantum and classical AI using generative methods.

Dr. Deschaine brings over four decades of experience as an
internationally recognized expert in complex adaptive systems
simulation and optimization. His academic credentials span multiple
disciplines, holding four university degrees: a Bachelor’s degree in
Civil Engineering from the Massachusetts Institute of Technology
(1984), a Master’s degree from the University of Connecticut (1992), a
PhD from the Department of Space, Earth and the Environment at
Chalmers University of Technology in Sweden (2014), and a Master of
Theology from Saint Leo University (2016). He first learned quantum
computation from a White Sands Missile Base physicist in 1978.
He attended the 1981 MIT Endicott meeting and heard Professor
Richard Feynman’s now-famous speech that helped launch the drive to build quantum
computing.

Dr. Deschaine received the prestigious U.S. Vice-Presidential Hammer
Award from Nobel Laureate Albert A. Gore Jr. for excellence in
process optimization. He was awarded Grand Prize distinction
in the 2017 American Academy of Environmental Engineers and Scientists
competition in the Research Category for his groundbreaking
optimization research and applications.

This presentation showcases eight practical quantum AI computing
applications developed at Savannah River National Laboratory (SRNL),
demonstrating real quantum advantages in diverse problem domains. All
implementations represent working code rather than theoretical
frameworks, emphasizing the practical readiness of quantum-enhanced
solutions, thereby putting science to work.

Our hybrid classical-quantum approach spans critical applications
including weather-informed sensing for plume dispersion modeling,
image-based spot detection and identification, materials discovery
frameworks, medicine discovery for protein structure prediction,
supply chain optimization, parameter optimization for complex systems,
optimal state estimation from sensor networks, and quantum sensing
digital twins.

Key achievements include a 7% accuracy improvement in spot detection
using quantum algorithms with 75% fewer input features compared to
classical approaches, demonstrating clear quantum advantage through
both improved performance and reduced computational complexity. The
materials and medicine discovery frameworks bridge theoretical
predictions with experimental validation, enabling quantum-accelerated
screening across multiple scales. Supply chain optimization
demonstrates exponential scaling advantages of quantum approaches over
classical methods for complex optimization problems.

The optimal state estimation toolkit successfully extracts signals
from noisy, correlated sensor data and provides digital twin
replacement capabilities for failed sensors. Our quantum sensing
framework creates digital twins of experimental setups, optimizing
calibration procedures and focusing experimental resources.

This work represents part of SRNL’s coordinated 5-year program to
build quantum skill sets in the emerging workforce and demonstrates
the transition from quantum AI research to practical
implementation. The presentation will discuss
specific quantum algorithms employed, performance comparisons with
classical methods, and provide a toolkit framework for assessing
quantum feasibility and advantage across diverse application domains.

Kanav Setia is a co-founder and CEO of qBraid. He earned a PhD in Physics from Dartmouth College in 2020, where he worked on quantum algorithms for quantum chemistry, with a particular focus on fermion-to-qubit encodings. Dr. Setia’s work on the Bravyi-Kitaev Superfast (BKSF) algorithm was the first to apply BKSF to quantum chemistry simulation. Collaborating with IBM, he developed the Generalized Superfast Encoding (GSE) for quantum simulation. This work was the first to show the presence of inherent error-correcting properties within the fermion-to-qubit encodings. Among many responsibilities at qBraid, he is the PI for the Q4Bio contract, for which qBraid is building a quantum software pipeline to understand protein drug interactions. Dr. Setia also holds a B.Tech in Aerospace Engineering with a minor in Astronomy and Planetary Sciences from the Indian Institute of Space Science and Technology. After graduation, he worked for four years at the Indian Space Research Organization (ISRO) in the MEMS division of the Semi-Conductor Laboratory, focusing on the design and development of accelerometers and gyroscopes.

Strongly correlated biomolecular systems such metalloproteins are critical to understanding the important cellular functioning and the pathogenesis of neurodegenerative diseases. Examples include iron-sulfur clusters, believed to be responsible for correct cellular functioning, as well as the amyloid-beta protein aggregating via metal ions such as zinc, iron, or copper and leading to dysfunction. The mechanisms of coordination, dynamics, and other physiological factors in these metalloproteins aren’t fully understood. Quanta-Bind is an algorithmic pipeline combining quantum computing and chemistry techniques to model these interactions better. The pipeline combines the Fragment Molecular Orbital (FMO) method to break down proteins, the Localized Active Space (LAS) and Quantum Bootstrap Embedding (QBE) method to treat correlation, alongside the Generalized Superfast Encoding (GSE) to map fermions to qubits. GSE is a graph-based fermion-to-qubit encoding, providing robustness against errors.

Dr. Yan Li received her Ph.D. degree from the University of Connecticut, Storrs, CT, U.S., in 2019. She also received a Ph.D. degree from Tianjin University, Tianjin, China, in 2013. Both are in electrical engineering. She is currently a Charles H. Fetter Endowed Fellow Assistant Professor at the School of Electrical Engineering and Computer Science at Pennsylvania State University, University Park, PA, U.S. Her research interests include cyber-physical power systems, quantum computing, data-driven modeling and control, stability, resilience analysis, cybersecurity, etc. Her team is currently funded by the Office of Naval Research and the National Science Foundation to develop quantum techniques for energy systems. She also serves as an IEEE representative for the Quantum Economic Development Consortium and chairs the IEEE Power and Energy Society Quantum Task Force.

Meter infrastructure is critical for real‑time monitoring and control of complex engineering systems, particularly modern power grids, but optimally locating meters is an NP‑hard combinatorial problem that becomes computationally prohibitive as network size and renewable integration grow. In this talk, a hybrid quantum‑classical method based on the Quantum Approximate Optimization Algorithm (QAOA) is presented to tackle meter placement under both unconstrained and channel‑limited scenarios. To analyze the QAOA-generated solution distributions, recursion-based Depth-First Search and Breadth-First Search algorithms are developed, which reduces the complexity from quadratic to linear time. Additionally, quantum parameter studies are conducted to identify key factors influencing QAOA performance and provide insights into tuning these critical parameters. Benchmarks on standard IEEE systems demonstrate better placement and faster runtimes than leading classical methods, paving the way for improved quantum‑enhanced grid monitoring in the Noisy Intermediate Scale Quantum era.

The Quantum Scientific Computing Open User Testbed (QSCOUT) located at Sandia National Laboratories is a “white box” quantum processor based on trapped ions available to the scientific community as of 2021 (https://qscout.sandia.gov).  During the process of interacting with users, we encountered numerous unforeseen challenges, resulting in hardware improvements to the system for the next round of experiments.  I will present how working with user teams – and the testbed model in general – has accelerated progress in gate implementation and calibration, software, and classical control hardware.  I will also speak to our upcoming features, including accessing the bosonic motional mode as a quantum resource. 

Sandia National Laboratories is managed and operated by NTESS, LLC, a subsidiary of Honeywell International, Inc. for the US DOE NNSA under contract DE-NA0003525.

One problem where quantum computers are hoped to demonstrate an advantage in the near term is the electronic structure problem in quantum chemistry, where one attempts to find the ground state energy of a molecular system. In recent years, quantum computers have scaled up to hundreds of physical qubits. Simultaneously, the development of sample-based diagonalization methods in recent years has brought down the runtime of simulating such systems drastically as compared to the variational quantum eigensolver. The combination of these developments has opened up an opportunity to begin to explore system sizes that are intractable to both classical diagonalization and quantum variational methods. In this work we combine sample-based diagonalization with quantum bootstrap embedding, a method wherein one breaks a molecule into non-disjoint fragments and solves for the energy self-consistently by matching the reduced density matrices of neighboring fragments on their overlapping regions. Furthermore, we show that the sample-based diagonalization approach can be used to reconstruct the global wavefunction of the system from its constituent fragment wavefunctions.

Variational quantum algorithms (VQAs) are a promising approach to solve relevant quantum chemistry and optimization problems algorithmically. However, making use of these approaches takes two-fold knowledge: the problem to be solved, and the quantum technology to be used. Our NchooseK programming model allows users to focus on only the variable relationships of their problem and automatically maps the problem appropriately to a user selected solver. Building on our past work, we contribute two novel solution approaches. The first synthesizes approximate circuits for the ansatz of the VQA to closely approximate the original circuit in such a way that on-line compilation is not necessary at every iteration of the VQA. These approximate circuits can be used both in simulation and on hardware and yield solutions of comparable fidelity to the baseline in both simulation and hardware experiments while reducing the average number of CNOTs required by 32\%. The second is in-progress work to add an additional solver to the framework to leverage qumode (bosonic) devices rather than exclusively qubit based ones. This approach has the potential to provide superior solution fidelity due to richer bosonic encodings. We leverage bosonic-qisikit to create and compare hybrid (qumode-qubit) VQA circuits with native gate representations. We intend to use this as a comprehensive comparison between qubit- and qumode-based devices as well as begin to consider various potential native qumode-based gate mixers for QAOA.

In classical game theory, Kuhn’s Theorem shows that in games with perfect recall, behavioral and mixed strategies are equivalent. But this equivalence breaks down when recall is imperfect — behavioral strategies lose their coordination power. In this talk, I present a quantum analogue to Kuhn’s result. I show that a behavioral-style quantum strategy, using only local measurements on a separable (non-entangled) quantum state with nonzero discord, can replicate the performance of a classical mixed strategy in an imperfect recall game. This coordination is impossible with classical memoryless strategies. The result highlights quantum discord—not entanglement—as a minimal and robust resource for restoring strategic coordination without memory, suggesting new directions for modeling bounded rationality in quantum systems.

In the NISQ era, quantum error mitigation (QEM) is essential for producing reliable outputs from quantum circuits. We present a statistical signal processing approach to QEM that estimates the most likely noiseless outputs from noisy measurements. The noiseless outputs are estimated by optimizing over a probabilistic noise formulation. We demonstrate the effectiveness of this approach using simulated data and IBM QPU data, and compare its performance to state-of-the-art statistical QEM techniques, showing significant improvement in fidelity.

Entanglement distribution across remote distances is critical for many quantum applications. Currently, the de facto approach for remote entanglement distribution relies on optical fiber for on-the-ground entanglement distribution. However, the fiber-based approach is incapable of global-scale entanglement distribution due to intrinsic limitations. This paper investigates a new hybrid ground-satellite quantum network architecture (QuESat) for global-scale entanglement distribution, integrating an on-the-ground fiber network with a global-scale passive optical network built with low-Earth-orbit satellites. The satellite network provides dynamic construction of photon lightpaths based on near-vacuum beam guides constructed via adjustable arrays of lenses, forwarding photons from one ground station to another with very high efficiency over long distances compared to using fiber. To assess the feasibility and effectiveness of QuESat for global communication, we formulate lightpath provisioning and entanglement distribution problems, considering the orbital dynamics of satellites and the time-varying entanglement demands from ground users. A two-stage algorithm is developed to dynamically configure the beam guides and distribute entanglements, respectively. The algorithm combines randomized and deterministic rounding for lightpath provisioning to enable global connectivity, with optimal entanglement swapping for distributing entanglements to meet users’ demands. By developing a ground-satellite quantum network simulator, QuESat achieves multi-fold improvements compared to repeater networks.

The formation of coherent macroscopic states and the manipulation of their entanglement using external stimuli are essential for emerging quantum applications. However, the observation of collective quantum phenomena such as Bose–Einstein condensation, superconductivity, superfluidity and superradiance has been limited to extremely low temperatures to suppress dephasing due to random thermal agitations. In this presentation I will talk about room-temperature superfluorescence (SF) in hybrid perovskite thin films. In SF an optically excited population of incoherent dipoles develops collective coherence spontaneously. This emergent collective state forms a giant dipole and radiates a burst of photons. The discovery of room temperature SF in perovskites is very surprising and shows that in this material platform, there exists an extremely strong immunity to thermal dephasing. To explain this observation, I will introduce the quantum analogue of vibration isolation (QAVI) mechanism, which protects electronic excitations against dephasing even at room temperature. Understanding the origins of sustained quantum coherence at high temperatures can provide guidance to design systems for emerging quantum information technologies and to realize similar high-temperature macroscopic quantum phenomena in tailored materials.

Open systems with loss or gain, described by effective non-Hermitian Hamiltonians, have attracted significant attention in the last two decades. These systems generally possess complex eigenvalues and nonorthogonal eigenstates. Their degeneracies are known as exceptional points (EPs), where both the eigenvalues and the corresponding eigenstates become degenerate. This peculiar behavior has led to many nonintuitive phenomena and applications in classical platforms, including EP sensors and chiral state transfer via encircling an EP. Recently, there has been growing interest in harnessing non-Hermitian dynamics and EPs for quantum applications. Open quantum systems provide a natural platform for exploring the relevant physics. In this talk, I will discuss chiral state transfer via dynamically encircling an EP in a circuit QED setup and the approach to mitigate the decoherence effect. I will also introduce a Floquet picture to interpret the observations, which offers a way to engineer quantum dissipators. Finally, I will discuss a theoretical proposal leveraging the same idea for generating Bell states in a dissipative two-qubit system.

Spin-photon interfaces established by coupling between optically-active spin systems and photonic cavities with high cooperativity are vital for quantum information processing. Specifically, “Bullseye” cavities formed by periodic etching of rings offer an optically accessible spin-photon interface but lack the high cooperativity required for quantum applications due to the low quality factor of the cavities (<1000). Here, we use differentiable guided-mode expansion (GME) inverse-design method to design novel cavities with quality factors as high as ~1000 while maintaining the efficient optical accessibility of Bullseye cavities. Our work highlights the potential of inverse design for advancing quantum photonic technologies.