Embedded Files
Morning
9:30–9:35 Opening Remarks (Ikko Hamamura, NVIDIA)
9:35–9:55 ABCI-Q: Quantum-AI Hybrid Computing Information (Ryousei Takano, AIST)
9:55–10:20 Enabling GPU-Accelerated Pulse-Level QPU Emulation in Qibolab via CUDA-Q Dynamics (Khoo Jun Yong, A*STAR)
10:20–10:45 Distributed Quantum Optimization and Machine Learning in Quantum-HPC Ecosystem (Kuan-Cheng Chen, Jij Europe/Imperial College London)
10:45–11:15 Break
11:15–11:40 Toward practical quantum-classical hybrid application (Shinji Kikuchi, Fujitsu)
11:40–12:05 Hybrid HPC–Quantum Workflows in Practice (Juan Pedersen, Quantinuum)
12:05–12:30 Lessons learned from real HPC+QC Integration (Eric Mansfield, IQM)
12:30–13:30 Lunch Break
Afternoon
13:30–14:45 Hands-on Session (Pascal Jahan Elahi, Pawsey & Tommaso Macrì, QuEra)
Pawsey Quantum Supercomputing Innovation Hub and Hybrid Quantum Computing
14:45–15:15 Break
15:15–15:40 Efficient Tensor Network Simulation of Large-Scale Quantum Circuits on HPC Systems (Tai-Yue Li, NCHC)
15:40–16:05 Recent progress in quantum algorithms for quantum chemistry (Masaya Kohda, QunaSys)
16:05–16:30 Qamomile and SQOA-QR for Sampling-Based Hybrid Quantum Optimization (Yu Yamashiro, Jij)
Program Detail
Enabling GPU-Accelerated Pulse-Level QPU Emulation in Qibolab via CUDA-Q Dynamics
Abstract:
Building on our previous work on pulse-level simulation integrated with Qibolab 0.1 using a QuTiP-based simulation engine, we present a refactored emulator architecture in Qibolab 0.2 that introduces an additional CUDA-Q Dynamics-based simulation engine. While the default QuTiP engine provides a robust baseline for pulse-level simulation, the CUDA-Q Dynamics engine offers enhanced efficiency for workloads that benefit from GPU acceleration. We demonstrate this through benchmarks across representative workloads, including simulations of selected QPU calibration and quantum algorithm routines, showing how GPU acceleration can significantly improve the performance and scalability of QPU emulation. The addition of the CUDA-Q Dynamics simulation engine positions the Qibolab 0.2 emulator as a versatile and practical tool for accelerating near-term quantum algorithm development and QPU hardware optimization.
Short bio:
Khoo Jun Yong is currently a senior research scientist at Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore. His research interests include quantum optimal control, quantum machine learning, as well as applications of classical machine learning to quantum systems. He received his Ph.D. in Physics from Massachusetts Institute of Technology, M.Sc. in Physics from University of Waterloo, and B.A. Physics from University of Oxford. Prior to joining A*STAR, he did his post-doctoral research in Max Planck Institute for the Physics of Complex Systems in Dresden, Germany.
Distributed Quantum Optimization and Machine Learning in Quantum-HPC Ecosystem (Kuan-Cheng Chen, Jij Europe and Imperial College London)
Abstract
Distributed integration of quantum computing with high-performance computing (HPC) is emerging as a promising approach for tackling large-scale optimization and machine learning (ML) problems. This talk presents a framework for distributed quantum optimization and quantum-enhanced ML designed for a quantum–HPC ecosystem, where NISQ-era quantum processors are tightly coupled with classical parallel systems. The framework supports the orchestration of quantum workloads across multiple HPC nodes, embedding variational quantum algorithms and quantum ML models into distributed workflows with scalable classical pre- and post-processing. Key design aspects include strategies for mitigating quantum hardware noise and communication latency, scheduling and load balancing across heterogeneous quantum–classical resources, and optimization of data movement between simulators, quantum devices, and HPC storage. Representative case studies on benchmark optimization and ML tasks demonstrate the potential for improved solution quality and scalability relative to purely classical baselines, while maintaining efficient utilization of limited quantum resources. The talk will also outline open challenges in software stacks, programming models, and performance modeling, and discuss future directions toward production-ready quantum–HPC integration for scientific and industrial applications.
Short Bio
Kuan-Cheng (Louis) Chen received his MSc and PhD from Imperial College London, where he focused on distributed quantum computing and quantum information processing. He is currently the lead quantum researcher at Jij, a Japanese quantum software startup, while also serving in a supporting PhD supervisor role at Imperial College London. Previously, he worked as an R&D device engineer at TSMC, specializing in 3DIC and memory devices. Dr. Chen won first prize in the 2024 Deloitte Quantum Challenge and received the 2025 IEEE QTC Distinguished QCE25 Technical Paper Award for Best Paper in the Quantum Application and Photonic tracks. His research interests include distributed quantum computing, quantum machine learning/AI, and quantum optimization. He has presented at major venues including ICLR, NeurIPS, INFOCOM, ICASSP, ISCAS, and IEEE QCE.
Efficient Tensor Network Simulation of Large-Scale Quantum Circuits on HPC Systems (Tai-Yue Li, NCHC)
Abstract
Large-scale quantum circuit simulation is essential as current quantum processing units (QPUs) remain constrained by noise, limited qubit counts, and short coherence times. While state-vector simulation becomes infeasible due to exponential resource growth, tensor network (TN) methods exploit circuit structure and entanglement to achieve greater efficiency and scalability. This talk presents efficient TN simulation techniques on high-performance computing (HPC) systems, highlighting core algorithms, parallelization strategies, and their value for quantum algorithm validation, QPU design, and applications in quantum machine learning, molecular generation, and image compression.
Short Bio
Dr. Tai-Yue Li is an Assistant Researcher at the National Center for High-performance Computing (NCHC), specializing in large-scale quantum circuit simulation, quantum machine learning, tensor-network algorithms, and Quantum–HPC integration. He develops scalable tensor-network simulation frameworks that run efficiently on multi-node, multi-GPU supercomputers, enabling quantum circuit and QSVM simulations at tens of thousands of qubits. His research also includes chemistry-inspired quantum circuit design, quantum data encoding, quantum kernels, and hybrid quantum–classical machine learning models.
Lessons learned from real HPC+QC Integration
Abstract: The first HPC+QC co-located systems have been available for users since 2024. In this talk, we will discuss three key challenges to realizing quantum computers as accelerators within heterogenous HPC architectures. The first is adapting the design and operations of quantum computers from experimental devices to production ready accelerators. The second is integrating the quantum computer into existing HPC orchestration management systems with minimal disruption. The third is identifying the most promising heterogenous workflows that can create new term scientific utility.
Short bio: Eric Mansfield is a senior product manager at IQM Quantum Computers.
Hybrid HPC–Quantum Workflows in Practice (Juan Pedersen, Quantinuum)
Abstract
This presentation focuses on quantum chemistry calculations conducted as joint research with RIKEN and introduces several concrete application case studies that leverage the Fugaku–Reimei hybrid platform.
In addition, we describe the design principles and practical operation of the workflow management system Tierkreis, which provides integrated control of these computations, and discuss future prospects for quantum–HPC hybrid computing.
Short bio
Juan Pedersen is a research scientist at Quantinuum. He works on Quantum-HPC hybrid applications using the hybrid system that combines the Quantinuum H1 device “Reimei” at RIKEN with the supercomputer “Fugaku.” His background is high-energy physics, with experience in quantum computation of gauge theories, and ongoing work on quantum algorithms for partial differential equations. After receiving his PhD in physics from the University of Tokyo, he worked as a postdoctoral researcher at RIKEN RQC before joining Quantinuum in 2025.
Pawsey Quantum Supercomputing Innovation Hub and Hybrid Quantum Computing (Pascal Jahan Elahi, Pawsey & Tommaso Macrì, QuEra)
Abstract
Pawsey's Quantum Supercomputing Innovation Hub is a collaborative platform aimed at accelerating quantum computing development and integrating it with classical supercomputing infrastructure. I will discuss Australia's efforts to democratise access using Setonix-Q Pilot, which provides open access to Australian researchers to quantum computing hardware along with our efforts to simulate quantum algorithms and integrate Quantum Computers with Supercomputers using QBitBridge, which will allow rapid development of hybrid quantum-classical workflows. I will close with early efforts running quantum algorithms on currently available quantum computers, focusing on co-design projects with QuERA.
Short Bio
Dr. Pascal Jahan Elahi is a high-performance and quantum computing expert, leading the quantum supercomputing research group at the Pawsey Supercomputing Research Centre. Pascal received his PhD in computational astrophysics after which he held several postdoctoral research positions. During his PhD and academic career developed an extensive track record of developing astronomical software for high-performance computing (HPC) systems. Now part of the HPC community, Pascal uses his expertise to help not only astronomers but researchers across various scientific fields scale their workflows to supercomputing systems and accelerate their science. He works closely with the Australian Astronomical community running precursor radio telescopes to the upcoming SKA-Low radio telescope, the world's largest low frequency telescope. Pascal’s current focus is at the intersection of Quantum Computing and Supercomputing, looking to integrate these technologies and grow the Quantum Computing community in Australia. His team are assisting the Australian research quantum computing community by providing access to quantum computers, training in the use of HPC systems and are heavily involved in growing this ecosystem by bridging the gap between quantum computing experts and researchers in other scientific domains. Pascal is a strong advocate for diversifying the HPC community and a passionate STEM educator. He has developed and delivered training material for researchers using HPC, introductory quantum computing for researchers and primary and high school students to get them excited about STEM.
Tommaso Macrì is the Sr. Director of Business Development at QuEra Computing, where he works to advance the development and commercialization of neutral-atom quantum computing technology. With a strong background in theoretical physics, Tommaso specializes in quantum simulation and quantum computing, particularly focusing on neutral-atom platforms. Before joining QuEra, Tommaso held a professorship in physics and has contributed extensively to research in quantum simulation and many-body physics. His expertise spans both analog and digital quantum computing modalities. Tommaso has a PhD in Physics and has worked at prominent academic institutions. At QuEra, he closely works with academic and industry partners, ensuring the effective integration of quantum technologies into a variety of scientific and industrial applications.
Recent progress in quantum algorithms for quantum chemistry (Masaya Kohda, QunaSys)
Abstract:
In this talk, based on the current status and future outlook of quantum computing hardware development, I present an overview of quantum chemistry applications focusing on the efforts of QunaSys. In particular, the talk highlights the Quantum-Selected Configuration Interaction (QSCI) method, a quantum-classical hybrid algorithm developed by our group. Recent large-scale demonstrations using quantum-HPC environments suggest that QSCI may enable practical quantum chemistry calculations even before the fault-tolerant quantum computing (FTQC) era. In addition, potential applications in the field of computer-aided engineering (CAE) are briefly discussed.
Short Bio:
Dr. Masaya Kohda is a senior researcher at QunaSys. He conducts research and development on quantum algorithms, mainly for applications in chemistry and physics.
Qamomile and SQOA-QR for Sampling-Based Hybrid Quantum Optimization (Yu Yamashiro, Jij)
Abstract:
Hybrid quantum–classical computing is increasingly viewed as a practical path toward deploying quantum techniques for real-world optimization, particularly when quantum processing units (QPUs) are integrated into high-performance computing (HPC) environments. However, fragmented software stacks and heterogeneous execution environments make building end-to-end workflows challenging.
In this talk, we introduce Qamomile, an open-source software layer that bridges classical mathematical modeling and quantum execution by converting optimization formulations into quantum-ready representations such as Hamiltonians and circuits. By providing a unified interface, Qamomile enables rapid prototyping and benchmarking across multiple quantum SDKs without rewriting problem definitions.
Building on this foundation, we present our Sampling-based Quantum Optimization Algorithm with Quantum Relaxation (SQOA-QR), a non-variational approach that combines quantum relaxation encoding with efficient input-state preparation for sampling-based hybrid optimization. We also discuss interoperability with NVIDIA CUDA-Q as part of a heterogeneous quantum–classical software stack. Finally, we highlight platform characteristics that support production workflows, including seamless solver integration and scalable execution across heterogeneous environments, emphasizing how a software ecosystem approach can bridge research prototypes and operational deployment.
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