In hybrid quantum-classical (HQC) computing, the coherent execution of algorithms on quantum and classical resources is a critical challenge. Most HQC algorithms involve iterative exchanges of data between quantum and classical processors, causing system bottlenecks and leading to high latency in applications. In this paper, we propose a novel framework that unifies quantum and reconfigurable resources for HQC algorithms, to mitigate system bottlenecks and latency. The proposed framework integrates Field-Programmable Gate Arrays (FPGAs) with quantum processing units (QPUs) for deploying HQC algorithms. Classical subroutines of the algorithms are accelerated on FPGA fabric using a high-throughput processing pipeline, while quantum subroutines are executed on QPUs. High-level software is used to seamlessly facilitate workload distribution and data exchange between classical and quantum processors. To evaluate the proposed framework, an HQC algorithm, namely variational quantum classification and MNIST dataset are used as a test case. The results demonstrate that the FPGA pipeline achieves up to$8 \times$improvement in runtime compared to the baseline of a state-of-the-art quantum software framework running on a server-grade CPU.
In hybrid quantum-classical (HQC) computing, the coherent execution of algorithms on quantum and classical resources is a critical challenge. Most HQC algorithms involve iterative exchanges of data between quantum and classical processors, causing system bottlenecks and leading to high latency in applications. In this paper, we propose a novel framework that unifies quantum and reconfigurable resources for HQC algorithms, to mitigate system bottlenecks and latency. The proposed framework integrates Field-Programmable Gate Arrays (FPGAs) with quantum processing units (QPUs) for deploying HQC algorithms. Classical subroutines of the algorithms are accelerated on FPGA fabric using a high-throughput processing pipeline, while quantum subroutines are executed on QPUs. High-level software is used to seamlessly facilitate workload distribution and data exchange between classical and quantum processors. To evaluate the proposed framework, an HQC algorithm, namely variational quantum classification and MNIST dataset are used as a test case. The results demonstrate that the FPGA pipeline achieves up to8×improvement in runtime compared to the baseline of a state-of-the-art quantum software framework running on a server-grade CPU.
Hybrid quantum–classical (HQC) computing refers to the approach of executing algorithms coherently on both quantum and classical resources. This approach makes the best use of current or near-term quantum computers by sharing the workload with classical high-performance computing. However, HQC algorithms often require a back-and-forth exchange of data between quantum and classical processors, causing system bottlenecks and leading to high latency in applications. The objective of this study is to investigate novel frameworks that unify quantum and reconfigurable resources for HQC and mitigate system bottleneck and latency issues. In this paper, we propose a reconfigurable framework for hybrid quantum–classical computing. The proposed framework integrates field-programmable gate arrays (FPGAs) with quantum processing units (QPUs) for deploying HQC algorithms. The classical subroutines of the algorithms are accelerated on FPGA fabric using a high-throughput processing pipeline, while quantum subroutines are executed on the QPUs. High-level software is used to seamlessly facilitate data exchange between classical and quantum workloads through high-performance channels. To evaluate the proposed framework, an HQC algorithm, namely variational quantum classification, and the MNIST dataset are used as a test case. We present a quantitative comparison of the proposed framework with a state-of-the-art quantum software framework running on a server-grade CPU. The results demonstrate that the FPGA pipeline achieves up to 8× improvement in runtime compared to the CPU baseline.
Quantum algorithms can be efficiently emulated on classical hardware such as field programmable gate arrays (FPGAs), achieving significant speedup over software simulations. However, the increase in the required hardware resources for emulating quantum systems becomes a critical limitation as the number of qubits is increased. In this paper, we propose a scalable emulation framework for modeling quantum algorithms on FPGAs that employs efficient resource scheduling such as space and space-time scheduling. In addition, full floating point precision arithmetic and dataflow non-linear (dynamic) pipelining are also used to achieve higher accuracy and higher throughput. We propose scalable and optimized hardware architectures for Quantum Fourier Transform (QFT) and Grover's search algorithm and demonstrate the scalability of our framework by scaling the system up to 5 fully-entangled qubits. A multi-node (multi-FPGA), state-of-the-art high-performance reconfigurable computer (HPRC) was used for implementation of the proposed architectures. Our experimental results show that by employing efficient resource scheduling techniques, the hardware resource constraints could be mitigated and the proposed emulation framework could be made feasible for emulation of more complex, larger-scale quantum algorithms while maintaining higher accuracy and throughput than existing work.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum-classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum-classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum-classical HPC ecosystems.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum–classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum–classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum–classical HPC ecosystems.
Achieving high-performance computation on quantum systems presents a formidable challenge that necessitates bridging the capabilities between quantum hardware and classical computing resources. This study introduces an innovative distribution-aware Quantum-Classical-Quantum (QCQ) architecture, which integrates cutting-edge quantum software framework works with high-performance classical computing resources to address challenges in quantum simulation for materials and condensed matter physics. At the heart of this architecture is the seamless integration of VQE algorithms running on QPUs for efficient quantum state preparation, Tensor Network states, and QCNNs for classifying quantum states on classical hardware. For benchmarking quantum simulators, the QCQ architecture utilizes the cuQuantum SDK to leverage multi-GPU acceleration, integrated with PennyLane's Lightning plugin, demonstrating up to tenfold increases in computational speed for complex phase transition classification tasks compared to traditional CPU-based methods. This significant acceleration enables models such as the transverse field Ising and XXZ systems to accurately predict phase transitions with a 99.5% accuracy. The architecture's ability to distribute computation between QPUs and classical resources addresses critical bottlenecks in Quantum-HPC, paving the way for scalable quantum simulation. The QCQ framework embodies a synergistic combination of quantum algorithms, machine learning, and Quantum-HPC capabilities, enhancing its potential to provide transformative insights into the behavior of quantum systems across different scales. As quantum hardware continues to improve, this hybrid distribution-aware framework will play a crucial role in realizing the full potential of quantum computing by seamlessly integrating distributed quantum resources with the state-of-the-art classical computing infrastructure.
Achieving high-performance computation on quantum systems is challenging, requiring integration between quantum and classical computing resources. This study presents a distribution-aware Quantum-Classical-Quantum (QCQ) architecture that combines advanced quantum software frameworks with high-performance classical computing to improve quantum simulations for materials and condensed matter physics, including the prediction of quantum phase transitions. The architecture employs Variational Quantum Eigensolver (VQE) algorithms on Quantum Processing Units (QPUs) for efficient quantum state preparation, and Tensor Network states and Quantum Convolutional Neural Networks (QCNNs) on classical hardware for state classification. Utilizing the cuQuantum SDK and PennyLane's Lightning plugin, the QCQ architecture achieves up to tenfold increases in computational speed for complex phase transition classification tasks compared to traditional CPU-based methods, demonstrating 99.5% accuracy in predicting phase transitions in models like the transverse field Ising and XXZ systems. This framework integrates quantum algorithms, machine learning, and Quantum-HPC capabilities, offering transformative insights into the behavior of quantum systems across different scales. As quantum hardware continues to improve, the QCQ framework will play a crucial role in realizing the full potential of quantum computing by seamlessly integrating distributed quantum resources with state-of-the-art classical computing infrastructure.