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.
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.
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.
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.
Early quantum computers are being offered by Cloud providers, with the ability to run Hybrid Quantum-Classical (HQC) applications that span Quantum and Classical (binary) computing resources. These HQC algorithms help benchmark and validate the performance of such near-term quantum hardware. Equally important is the software platform that allows a remote user to program the quantum computer on the cloud. The recent Qiskit Runtime platform from IBM allows a seamless and prioritized access to quantum backends and co-located classical hardware within their hybrid cloud for optimized execution of HQC workloads. In contrast, IBM’s Circuit API offers finer application definition but the classical resources are not co-located but placed farther away. Here, we study and contrast the execution flows of a Variational Quantum Eigensolver (VQE) application defined using Qiskit Runtime, and an equivalent implementation using the Circuit API. We quantify the latencies of the various components in these execution. This offers insights on how to better orchestrate the execution of quantum applications across such hybrid resources.
Abstract In order to support near-term applications of quantum computing, a new compute paradigm has emerged—the quantum-classical cloud—in which quantum computers (QPUs) work in tandem with classical computers (CPUs) via a shared cloud infrastructure. In this work, we enumerate the architectural requirements of a quantum-classical cloud platform, and present a framework for benchmarking its runtime performance. In addition, we walk through two platform-level enhancements, parametric compilation and active qubit reset, that specifically optimize a quantum-classical architecture to support variational hybrid algorithms, the most promising applications of near-term quantum hardware. Finally, we show that integrating these two features into the Rigetti Quantum Cloud Services platform results in considerable improvements to the latencies that govern algorithm runtime.
Abstract The practical application of quantum machine learning algorithms, such as Quantum Neural Networks (QNNs), is currently constrained by their dependence on nascent, error-prone, and resource-intensive quantum hardware. This research introduces and validates a novel hybrid quantum- classical (HQC) algorithm engineered to deliver robust classification performance on modest, commodity classical hardware, thereby circumventing the quantum hardware bottleneck. Our multi-stage architecture strategically integrates advanced classical statistical analysis (Mahalanobis distance), a quantum-inspired kernel method (a hardened Quantum Support Vector Machine), and a quantum-inspired anomaly detection model (a Quantum Autoencoder). This ensemble was rigorously benchmarked on a synthetic dataset specifically engineered for extreme classification dinjculty (85-90%), characterized by high dimensionality, low informative features, and complex, multi- cluster class structures. The HQC algorithm achieved a notable weighted average accuracy of 42.66% across multiple experimental runs. This result is particularly significant when contextualized: it was achieved on a legacy classical server (Dell PowerEdge T110 II), yet it competitively approaches the performance envelope of state-of-the-art QNNs (55-70%) that require genuine quantum processors to exploit phenomena like superposition and entanglement. This work presents a compelling case for the strategic value of hybrid models as a pragmatic, resource- enjcient, and immediately deployable solution. It effectively bridges the gap between the theoretical promise of quantum computing and the practical demands of current computational ecosystems, offering a viable and powerful pathway toward the democratization of complex problem- solving