The growth of quantum hardware as accelerators within cloud data centers is giving rise to hybrid quantum-classical applications. Such applications can be composed and deployed as Functions as a Service (FaaS) workflows, coupled with automated circuit optimizations to efficiently use the scarce quantum hardware with low cost. This poster explores the use of Qubit Reuse that can reduce the workload resource footprint, and potentially leverage the low-noise regions of qubit topologies with uneven noise distributions. We profile the possibility of qubit reuse in simulated noisy qubit topologies and in real hardware, and explore its efficiency in exploiting low-noise sub-topologies in quantum hardware, and the resulting trade-offs. Our initial results are promising for efficient use of scarce quantum hardware.
The growth of quantum hardware as accelerators within cloud data centers is giving rise to hybrid quantum-classical applications. Such applications can be composed and deployed as Functions as a Service (FaaS) workflows, coupled with automated circuit optimizations to efficiently use the scarce quantum hardware with low cost. This poster explores the use of Qubit Reuse that can reduce the workload resource footprint, and potentially leverage the low-noise regions of qubit topologies with uneven noise distributions. We profile the possibility of qubit reuse in simulated noisy qubit topologies and in real hardware, and explore its efficiency in exploiting low-noise sub-topologies in quantum hardware, and the resulting trade-offs. Our initial results are promising for efficient use of scarce quantum hardware.
Quantum computing is entering the mainstream as part of cloud offerings, where it serves as a special-purpose accelerator in larger applications. However, it is still challenging for developers and researchers to design, build and manage the resources for Hybrid Quantum-Classical (HQC) applications that include both classical logic (x86, ARM) and quantum circuit blocks, and run across both traditional and quantum processors. Further, quantum hardware is available on public cloud and even on-premise as private clouds, with varying capabilities, costs and queue times. Further, such quantum circuits also expose optimization methods that offer cost, time, accuracy and parallelism trade-offs. So, there is a compelling for easy composition of HQC applications that can be effortlessly and efficiently deployed on hybrid clouds. In this paper, we propose a framework to intuitively compose and deploy “zero-touch” quantum-classical Function-as-a-Service (FaaS) workflows through various workflow patterns that leverage diverse cloud system (workflow partitioning, adaptive polling) and quantum (circuit cutting, qubit reuse) optimizations. These utilize our XFaaS FaaS workflow framework for hybrid cloud deployments on AWS and Azure, and IBM Qiskit SDK for the quantum circuit toolchain. We also offer detailed experimental profiling of these optimizations for realistic and synthetic HQC applications on real clouds, and on real and simulated quantum hardware, and analyze the benefits of cloud system and quantum circuit optimizations. Our results demonstrate up to 53 % improvement in time and 80 % in cost when quantum circuit optimization on hardware is used in conjunction with dynamic fan-out.
Quantum computers are the next evolution of computing hardware. Quantum devices are being exposed through the same familiar cloud platforms used for classical computers, and enabling seamless execution of hybrid applications that combine quantum and classical components. Quantum devices vary in features, e.g., number of qubits, quantum volume, CLOPS, noise profile, queuing delays and resource cost. So, it may be useful to split hybrid workloads with either large quantum circuits or a large number of quantum circuits, into smaller units. In this paper, we profile two workload splitting techniques on IBM's Quantum Cloud: (1) Circuit parallelization, to split one large circuit into multiple smaller ones, and (2) Data parallelization to split a large number of circuits that run on one device to smaller batches of circuits that run on concurrently on multiple devices. These can improve the utilization of heterogeneous quantum hardware, but involve trade-offs. We evaluate these techniques on two key algorithmic classes: Variational Quantum Eigensolver (VQE) and Quantum Support Vector Machine (QSVM), and measure the impact on quality of the result, circuit execution times, quantum resource usage, and processing overheads, relative to a baseline without such parallelization. The results are obtained on real hardware and complemented by simulations. We see that (1) VQE with circuit cutting is ~ 39% better in ground state estimation than the uncut version, and (2) QSVM that combines data parallelization with a reduced feature set yields upto 3 ×improvement in quantum workload execution time and reduces quantum resource use by 3 ×, while providing comparable accuracy. Error mitigation can improve the accuracy by ≈ 7% and reduce resource footprint by ≈4% compared to the best case among the considered scenarios.
Quantum computing is widely regarded as a promising technology due to its potential to offer exponential computational advantages over classical systems. However, practical deployment and realization of most quantum algorithms remain constrained by the limitations of current quantum architectures– most notably, the limited number of physical qubits available in quantum processing units (QPUs). Therefore, efficient utilization of quantum resources has become a crucial concern in the field. Thus, in this paper, we explore the nature of qubit reuse. Based on our findings, we formulate an optimization problem and propose a novel algorithm to minimize the number of qubits for scheduled quantum circuits. Finally, extensive simulation results on various practical benchmarks manifest that our algorithm can significantly outperform the existing approach in qubit reuse.
Quantum computing is a new emerging technology which is capable of performing specific types of calculations more efficiently than classical computers. Yet quantum computers have their own limitations in terms of noise, decoherence, and qubits numbers, so the current research focus on hybrid quantum-classical computation where classical computers work in conjunction with quantum devices to perform computation. This chapter discusses the architecture of quantum-ready clouds and describes how to effectively run a hybrid quantum-classical workflows over cloud environment. It introduces the middleware and Application Programming Interface (API) in order to support communication between cloud environment and quantum hardware. Furthermore, the chapter shows how the orchestration mechanism to coordinate the classical cloud resources and quantum processors, and presents a simple workflow model for data routing, processing, and analysis utilizing classical/cloud and quantum hardware.
As quantum computing (QC) technologies mature, their integration into established high-performance computing (HPC) infrastructures is becoming a central objective for next-generation computing systems. However, unlocking the potential of hybrid platforms for computationally demanding workloads remains challenging. The mismatch between quantum and classical programming models, the limited maturity of quantum software stacks, and the scarcity of quantum processing units (QPUs) above all, necessitate scheduling strategies that go beyond standard HPC mechanisms to manage such heterogeneous and constrained resources.
To address this issue, we investigate three distinct methodologies for HPC-QC resource scheduling: time-based multiplexing, dynamic resource management, and workflow decomposition. Experimental validation on production HPC clusters and real quantum hardware demonstrates the effectiveness of these approaches under different workload scenarios. Malleability and workflow strategies significantly optimize classical resource utilization, reducing consumption by up to 45.7% and 64% respectively, proving to be best fitted for hybrid jobs where quantum and classical workloads are evenly balanced. Conversely, time-multiplexing enhances QPU utilization and reduces execution time at the cluster level, making it the optimal strategy for the opposite context, which is characterized by high classical-quantum workload imbalances. These findings underscore the practical viability of tailored scheduling strategies for hybrid HPC-QC environments and highlight their complementarity in building efficient, scalable software stacks for next-generation quantum-accelerated facilities.
The rapid advancement of quantum computing technology presents an opportunity to revolutionize cloud computing platforms, enabling the execution of complex workloads that are beyond the reach of classical systems. This paper explores the potential of quantum-enhanced cloud platforms, focusing on bridging classical and quantum computing to support future workloads. We examine the integration of quantum processors with classical cloud infrastructure, highlighting the challenges and benefits of hybrid architectures that combine the strengths of both paradigms. Key topics include quantum resource management, quantum programming models, and the development of algorithms that leverage quantum speedup for optimization, machine learning, and data analysis. Additionally, we address the scalability, security, and interoperability concerns that must be overcome for effective deployment in real-world cloud environments. By offering insights into the convergence of classical and quantum computing, this paper provides a roadmap for the evolution of cloud platforms capable of supporting next-generation applications and workloads.