Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2405.00863 · IEEE International Conference on Quantum Computing and Engineering (QCE)

SHARE: Secure Hardware Allocation and Resource Efficiency in Quantum Systems

Suryansh Upadhyay, Swaroop Ghosh

Published Sep 15, 2024• 10 citations
arXivHTMLPDF

Abstract

Quantum computing (QC) is poised to revolutionize problem-solving across various fields, with research suggesting that systems with over 50 qubits may achieve quantum advantage-surpassing supercomputers in certain optimization tasks. As the hardware size of Noisy Intermediate-Scale Quantum (NISQ) computers continues to grow, Multi-tenant computing (MTC) has emerged as a viable approach to enhance hardware utilization by allowing shared resource access across multiple quantum programs. However, MTC can also bring challenges and security concerns. This paper focuses on optimizing quantum hardware utilization in shared environments by implementing multi-programming strategies that not only enhance hardware utilization but also effectively manage associated risks like crosstalk and fault injection. We propose a novel partitioning and allocation method called Community-Based Dynamic Allocation Partitioning (COMDAP) and Secure COMDAP to refine and secure multi-programming capabilities in quantum systems. COMDAP ensures equitable and efficient resource distribution, addresses the issues of suboptimal partitioning, and significantly improves hardware utilization. We report a 23% average improvement in hardware utilization rate compared to existing greedy heuristics, with rates averaging 92%. COMDAP introduces an average increase of approximately 0.05X in$\Delta \mathbf{CX}$, alongside a 3.5% average reduction in PST across benchmarks.

DOI
10.1109/qce60285.2024.00130

Related papers

Impact of Error Rate Misreporting on Resource Allocation in Multi-tenant Quantum Computing and Defense

Subrata Das, Swaroop Ghosh

Cloud-based quantum service providers allow multiple users to run programs on shared hardware concurrently to maximize resource utilization and minimize operational costs. This multi-tenant computing (MTC) model relies on the error parameters of the hardware for fair qubit allocation and scheduling, as error-prone qubits can degrade computational accuracy asymmetrically for users sharing the hardware. To maintain low error rates, quantum providers perform periodic hardware calibration, often relying on third-party calibration services. If an adversary within this calibration service misreports error rates, the allocator can be misled into making suboptimal decisions even when the physical hardware remains unchanged. We demonstrate such an attack model in which an adversary strategically misreports qubit error rates to reduce hardware throughput, and probability of successful trial (PST) for two previously proposed allocation frameworks, i.e. Greedy and Community-Based Dynamic Allocation Partitioning (COMDAP). Experimental results show that adversarial misreporting increases execution latency by 24% and reduces PST by 7.8%. We also propose to identify inconsistencies in reported error rates by analyzing statistical deviations in error rates across calibration cycles.

arXiv:2504.04285•ACM Great Lakes Symposium on VLSI (GLSVLSI)•Jun 27, 2025•1 citation

Stealthy SWAPs: Adversarial SWAP Injection in Multi-Tenant Quantum Computing

Suryansh Upadhyay, Swaroop Ghosh

Quantum computing (QC) holds tremendous promise in revolutionizing problem-solving across various domains. It has been suggested in literature that 50+ qubits are sufficient to achieve quantum advantage (i.e., to surpass supercomputers in solving certain class of optimization problems). The hardware size of existing Noisy Intermediate-Scale Quantum (NISQ) computers have been ever increasing over the years. Therefore, Multi-tenant computing (MTC) has emerged as a potential solution for efficient hardware utilization, enabling shared resource access among multiple quantum programs. However, MTC can also bring new security concerns. This paper proposes one such threat for MTC in superconducting quantum hardware i.e., adversarial SWAP gate injection in victim’s program during compilation for MTC. We present a representative scheduler designed for optimal resource allocation. To demonstrate the impact of this attack model, we conduct a detailed case study using a sample scheduler. Exhaustive experiments on circuits with varying depths and qubits offer valuable insights into the repercussions of these attacks. We report a max of $\approx 55$% and a median increase of $\approx 25$% in SWAP overhead. As a countermeasure, we also propose a sample machine learning model for detecting any abnormal user behavior and priority adjustment.

arXiv:2310.17426•International Conference on VLSI Design•Jan 6, 2024•10 citations

Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Era

Siyuan Niu, Aida Todri‐Sanial

NISQ devices have several physical limitations and unavoidable noisy quantum operations, and only small circuits can be executed on a quantum machine to get reliable results. This leads to the quantum hardware under-utilization issue. Here, we address this problem and improve the quantum hardware throughput by proposing a Quantum Multi-programming Compiler (QuMC) to execute multiple quantum circuits on quantum hardware simultaneously. This approach can also reduce the total runtime of circuits. We first introduce a parallelism manager to select an appropriate number of circuits to be executed at the same time. Second, we present two different qubit partitioning algorithms to allocate reliable partitions to multiple circuits – a greedy and a heuristic. Third, we use the Simultaneous Randomized Benchmarking protocol to characterize the crosstalk properties and consider them in the qubit partition process to avoid the crosstalk effect during simultaneous executions. Finally, we enhance the mapping transition algorithm to make circuits executable on hardware using a decreased number of inserted gates. We demonstrate the performance of our QuMC approach by executing circuits of different sizes on IBM quantum hardware simultaneously. We also investigate this method on VQE algorithm to reduce its overhead.

arXiv:2102.05321•Quantum•Feb 16, 2023•52 citations

A System Aware Resource Allocation for Distributed Workflows in Quantum Computing Environments

Abhishek Sawaika, Udaya Parampalli, Rajkumar Buyya

Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and costly devices. Although most of the current systems use a priority based access protocol, they are unable to fully support reliable, efficient, and scalable execution of larger-scale applications. To overcome this limitation, we propose a comprehensive solution for efficient allocation of quantum programs to appropriate quantum devices, considering all the relevant cost metrics into account including, fidelity, execution time and communication overhead. We also formulate use-cases for distributed quantum workflow and propose modified graph based algorithms to solve for allocation of such use-cases, assuming a hybrid classical-quantum network. Since hardware advancements in large standalone devices is an ongoing process, it is critical to investigate such distributed workflows to maximize the best utilization of current NISQ devices. Our empirical study shows that the proposed techniques perform better than state-of-the-art methods for almost all evaluation parameters, with average improvements of approximately $5\%$ in execution time, $30\%$ in communication overhead, $40\%$ in wait time and $2\%$ in fidelity, providing better solutions to efficient allocation strategies.

quant-pharXiv:2605.17944•May 19, 2026•0 citations

Exploring multi-programming for quantum algorithms

Siyuan Niu, Aida Todri‐Sanial

Today's quantum computing is performed on Noisy Intermediate-Scale Quantum (NISQ) hardware. The NISQ era allows for a limited number of qubits with several physical limitations. As NISQ devices have unavoidable noisy quantum operations, we can execute only small circuits on a quantum machine to get reliable results. But, this leads to the quantum hardware under-utilization issue. With the increase of hardware qubit number and the improvement of error rates, it becomes possible to execute multiple circuits on a quantum chip simultaneously, enhancing the throughput and the utilization of NISQ hardware. Thus, we address a timely problem on how to execute reliably multiple quantum programs on given quantum hardware. This is the first attempt to propose a complete multi-programming process flow for executing an optimal number of workloads in parallel, ensuring output fidelity by analyzing the hardware limitations.

•Jan 1, 2021•0 citations

An Optimized Quantum Computing Integration Approach for Multi-Cloud Environments: Architecture Design for Efficiency, Security, and Intelligent Workload Management

Dr. Natsuki Ono, Dr. Riku Ando

The integration of quantum computing into multi-cloud environments represents a transformative paradigm in distributed computing, offering substantial improvements in computational efficiency, security resilience, and adaptive workload orchestration. However, the heterogeneous nature of cloud infrastructures, combined with the nascent and resource-sensitive characteristics of quantum systems, introduces significant architectural, operational, and security challenges. This paper proposes an optimized quantum computing integration approach designed specifically for multi-cloud ecosystems, emphasizing scalable architecture design, intelligent workload distribution, and enhanced cryptographic security mechanisms.The study synthesizes foundational quantum computing principles with contemporary multi-cloud orchestration strategies to develop a hybrid architectural framework. The proposed model leverages quantum-enhanced optimization techniques, including quantum approximate optimization principles, to dynamically allocate computational tasks across distributed cloud nodes. Additionally, it integrates quantum-safe security protocols to mitigate emerging cyber threats in cloud environments. Prior research highlights the relevance of such integration, particularly in solving complex optimization problems and enhancing system-level security (Brown & White, 2022; Cao et al., 2020).A structured literature synthesis reveals that while cloud computing and quantum computing have individually matured, their convergence remains underexplored in practical deployment contexts. This paper addresses this gap by introducing an architecture that supports adaptive workload balancing, quantum-classical hybrid execution layers, and secure inter-cloud communication channels. The findings indicate that quantum-enhanced orchestration can significantly reduce computational latency and improve resource utilization efficiency in multi-cloud systems.

European Journal of Emerging Cloud and Quantum Computing•Jul 1, 2026•0 citations
Quantum computing (QC) is poised to revolutionize problem-solving across various fields, with research suggesting that systems with over 50 qubits may achieve quantum advantage-surpassing supercomputers in certain optimization tasks. As the hardware size of Noisy Intermediate-Scale Quantum (NISQ) computers continues to grow, Multi-tenant computing (MTC) has emerged as a viable approach to enhance hardware utilization by allowing shared resource access across multiple quantum programs. However, MTC can also bring challenges and security concerns. This paper focuses on optimizing quantum hardware utilization in shared environments by implementing multi-programming strategies that not only enhance hardware utilization but also effectively manage associated risks like crosstalk and fault injection. We propose a novel partitioning and allocation method called Community-Based Dynamic Allocation Partitioning (COMDAP) and Secure COMDAP to refine and secure multi-programming capabilities in quantum systems. COMDAP ensures equitable and efficient resource distribution, addresses the issues of suboptimal partitioning, and significantly improves hardware utilization. We report a 23% average improvement in hardware utilization rate compared to existing greedy heuristics, with rates averaging 92%. COMDAP introduces an average increase of approximately 0.05X inΔCX\Delta \mathbf{CX}ΔCX, alongside a 3.5% average reduction in PST across benchmarks.
Cloud-based quantum service providers allow multiple users to run programs on shared hardware concurrently to maximize resource utilization and minimize operational costs. This multi-tenant computing (MTC) model relies on the error parameters of the hardware for fair qubit allocation and scheduling, as error-prone qubits can degrade computational accuracy asymmetrically for users sharing the hardware. To maintain low error rates, quantum providers perform periodic hardware calibration, often relying on third-party calibration services. If an adversary within this calibration service misreports error rates, the allocator can be misled into making suboptimal decisions even when the physical hardware remains unchanged. We demonstrate such an attack model in which an adversary strategically misreports qubit error rates to reduce hardware throughput, and probability of successful trial (PST) for two previously proposed allocation frameworks, i.e. Greedy and Community-Based Dynamic Allocation Partitioning (COMDAP). Experimental results show that adversarial misreporting increases execution latency by 24% and reduces PST by 7.8%. We also propose to identify inconsistencies in reported error rates by analyzing statistical deviations in error rates across calibration cycles.
Quantum computing (QC) holds tremendous promise in revolutionizing problem-solving across various domains. It has been suggested in literature that 50+ qubits are sufficient to achieve quantum advantage (i.e., to surpass supercomputers in solving certain class of optimization problems). The hardware size of existing Noisy Intermediate-Scale Quantum (NISQ) computers have been ever increasing over the years. Therefore, Multi-tenant computing (MTC) has emerged as a potential solution for efficient hardware utilization, enabling shared resource access among multiple quantum programs. However, MTC can also bring new security concerns. This paper proposes one such threat for MTC in superconducting quantum hardware i.e., adversarial SWAP gate injection in victim’s program during compilation for MTC. We present a representative scheduler designed for optimal resource allocation. To demonstrate the impact of this attack model, we conduct a detailed case study using a sample scheduler. Exhaustive experiments on circuits with varying depths and qubits offer valuable insights into the repercussions of these attacks. We report a max of ≈55\approx 55≈55% and a median increase of ≈25\approx 25≈25% in SWAP overhead. As a countermeasure, we also propose a sample machine learning model for detecting any abnormal user behavior and priority adjustment.
NISQ devices have several physical limitations and unavoidable noisy quantum operations, and only small circuits can be executed on a quantum machine to get reliable results. This leads to the quantum hardware under-utilization issue. Here, we address this problem and improve the quantum hardware throughput by proposing a Quantum Multi-programming Compiler (QuMC) to execute multiple quantum circuits on quantum hardware simultaneously. This approach can also reduce the total runtime of circuits. We first introduce a parallelism manager to select an appropriate number of circuits to be executed at the same time. Second, we present two different qubit partitioning algorithms to allocate reliable partitions to multiple circuits – a greedy and a heuristic. Third, we use the Simultaneous Randomized Benchmarking protocol to characterize the crosstalk properties and consider them in the qubit partition process to avoid the crosstalk effect during simultaneous executions. Finally, we enhance the mapping transition algorithm to make circuits executable on hardware using a decreased number of inserted gates. We demonstrate the performance of our QuMC approach by executing circuits of different sizes on IBM quantum hardware simultaneously. We also investigate this method on VQE algorithm to reduce its overhead.
Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and costly devices. Although most of the current systems use a priority based access protocol, they are unable to fully support reliable, efficient, and scalable execution of larger-scale applications. To overcome this limitation, we propose a comprehensive solution for efficient allocation of quantum programs to appropriate quantum devices, considering all the relevant cost metrics into account including, fidelity, execution time and communication overhead. We also formulate use-cases for distributed quantum workflow and propose modified graph based algorithms to solve for allocation of such use-cases, assuming a hybrid classical-quantum network. Since hardware advancements in large standalone devices is an ongoing process, it is critical to investigate such distributed workflows to maximize the best utilization of current NISQ devices. Our empirical study shows that the proposed techniques perform better than state-of-the-art methods for almost all evaluation parameters, with average improvements of approximately 5%5\%5% in execution time, 30%30\%30% in communication overhead, 40%40\%40% in wait time and 2%2\%2% in fidelity, providing better solutions to efficient allocation strategies.
Today's quantum computing is performed on Noisy Intermediate-Scale Quantum (NISQ) hardware. The NISQ era allows for a limited number of qubits with several physical limitations. As NISQ devices have unavoidable noisy quantum operations, we can execute only small circuits on a quantum machine to get reliable results. But, this leads to the quantum hardware under-utilization issue. With the increase of hardware qubit number and the improvement of error rates, it becomes possible to execute multiple circuits on a quantum chip simultaneously, enhancing the throughput and the utilization of NISQ hardware. Thus, we address a timely problem on how to execute reliably multiple quantum programs on given quantum hardware. This is the first attempt to propose a complete multi-programming process flow for executing an optimal number of workloads in parallel, ensuring output fidelity by analyzing the hardware limitations.
The integration of quantum computing into multi-cloud environments represents a transformative paradigm in distributed computing, offering substantial improvements in computational efficiency, security resilience, and adaptive workload orchestration. However, the heterogeneous nature of cloud infrastructures, combined with the nascent and resource-sensitive characteristics of quantum systems, introduces significant architectural, operational, and security challenges. This paper proposes an optimized quantum computing integration approach designed specifically for multi-cloud ecosystems, emphasizing scalable architecture design, intelligent workload distribution, and enhanced cryptographic security mechanisms.The study synthesizes foundational quantum computing principles with contemporary multi-cloud orchestration strategies to develop a hybrid architectural framework. The proposed model leverages quantum-enhanced optimization techniques, including quantum approximate optimization principles, to dynamically allocate computational tasks across distributed cloud nodes. Additionally, it integrates quantum-safe security protocols to mitigate emerging cyber threats in cloud environments. Prior research highlights the relevance of such integration, particularly in solving complex optimization problems and enhancing system-level security (Brown & White, 2022; Cao et al., 2020).A structured literature synthesis reveals that while cloud computing and quantum computing have individually matured, their convergence remains underexplored in practical deployment contexts. This paper addresses this gap by introducing an architecture that supports adaptive workload balancing, quantum-classical hybrid execution layers, and secure inter-cloud communication channels. The findings indicate that quantum-enhanced orchestration can significantly reduce computational latency and improve resource utilization efficiency in multi-cloud systems.