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arXiv:2504.04285 · ACM Great Lakes Symposium on VLSI (GLSVLSI)

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

Subrata Das, Swaroop Ghosh

Published Jun 27, 2025• 1 citation
arXivHTMLPDF

Abstract

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.

DOI
10.1145/3716368.3735191

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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.
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The qubit technologies, basis gate set, noise behavior, speed and coupling architecture are among the various factors that vary among various backends. Although third-party cloud providers offering quantum hardware as a service offer lower cost and flexibility to the users to choose from several qubit technologies, quantum hardware, and coupling maps; the actual execution of the program is not clearly visible to the customer. The success of the user program, in addition to various other metadata such as cost, performance, & number of iterations to converge, depends on the error rate of the backend used. Moreover, the third-party provider and/or tools (e.g., hardware allocator and mapper) may hold insider/outsider adversarial agents to conserve resources and maximize profit by running the quantum circuits on error-prone hardware. Thus it is important to gain visibility of the backend from various perspectives of the computing process e.g., execution, transpilation and outcomes. In this paper, we estimate the error rate of the backend from the original and transpiled circuit. Although many quantum services providers publish the error rates of their backends, we assume that such information may not be accurate and/or correspond to the actual hardware allocated to the program. For the forensics we propose two complementary approaches. First, we exploit the fact that qubit mapping and routing steps of the transpilation process select qubits and qubit pairs with low gate errors to minimize overall error accumulation. We leverage this to rank qubit links into bins and compare with publicly available data we are able to assign a bin rank within a difference of 2 with respect to the actual bin for upto83.5%\mathbf{8 3. 5 \%}83.5%of the qubit links in IBM Sherbrooke and80%\mathbf{8 0 \%}80%in IBM Brisbane, 127 qubit IBM backends. Second, we derive the error rates of the backends from a pool of programs by solving fidelity equations using numerical nonlinear optimizer. We achieve upto92.7%(97.3%)92.7 \%(97.3 \%)92.7%(97.3%)accuracy for single qubit (2 qubit) gate error rates.
Multiple layers of the quantum system full-stack require data on temporal variations in error rates of physical elements. The absence of this information, especially when users access cloud-based quantum systems with waiting queues between compilation and execution, can lead to unreliable computations due to outdated calibration data. One example is the compilation process that impacts the success rate of quantum circuits. This paper proposes using five machine learning and time-series models to predict error rate variability for two-qubit gates based on calibration data. We compare model accuracy and complexity in terms of execution time. The predicted information is applied to a noise-aware compiler to evaluate the impact on success rate. Experimental results on a real superconducting quantum processor demonstrate accurate predictions of temporal error rate variability, achieving performance comparable to that of real calibration data, reducing the impact of queue waiting times that can reach up to two days.
Quantum cloud computing will play a vital role in achieving quantum supremacy by providing flexible allocation of resources for applications that demand extensive computational power. Such a platform allows various clients to run their quantum jobs (quantum circuits) without managing the quantum hardware and to pay based on resource usage. Therefore, defining optimal quantum resource allocation is essential to prevent overcharging clients and to help quantum cloud providers maximize resource utilization. Our approach involves examining the problem from a game theory perspective. We introduce a quantum circuit partitioning resource allocation game model (QC-PRAGM) designed to minimize client costs and inter-node communication. This is achieved by solving a convex optimization problem that finds the system's optimal cost while maximizing the number of local gates within a partition through the selection of the best qubit combinations. We demonstrate analytically that clients are charged fairly. Additionally, our simulations show that our solutions outperform traditional methods.
Security and reliability are primary concerns in any computing paradigm including quantum computing. Currently users can access quantum computers through a cloud based platform where they can run their programs on a suite of quantum computers. As the quantum computing ecosystem grows in popularity and utility it is reasonable to expect that more companies including untrusted or less trusted or unreliable vendors will begin offering quantum computers as hardware as a service at varied price or performance points. Since computing time on quantum hardware is expensive and the access queue could be long the users will be motivated to use the cheaper and readily available but unreliable or less trusted hardware. The less trusted vendors can tamper with the results providing a suboptimal solution to the user. In this paper we model this adversarial tampering and simulate its impact on a number of pure quantum and hybrid quantum classical workloads. To guarantee trustworthy computing for a mixture of trusted and untrusted hardware we propose distributing the total number of shots equally among the various hardware options. On average we note approx 30X and approx 1.5X improvement across the pure quantum workloads and a maximum improvement of approx 5X for hybrid classical algorithm in the chosen quality metrics. We also propose an intelligent run adaptive split heuristic leveraging temporal variation in hardware quality to users advantage allowing them to identify tampered or untrustworthy hardware at runtime and allocate more number of shots to the reliable hardware which results in a maximum improvement of approx 190X and approx 9X across the pure quantum workloads and an improvement of up to approx 2.5X for hybrid classical algorithm.
Quantum computers are currently accessible through a cloud-based platform that allows users to run their programs on a suite of quantum hardware. As the quantum computing ecosystem grows in popularity and utility, it is reasonable to expect more companies, including untrustworthy/untrustworthy/unreliable vendors, to begin offering quantum computers as hardware-as-a-service at various price/performance points. Since computing time on quantum hardware is expensive and the access queue may be long, users will be enticed to use less expensive but less reliable/trustworthy hardware. Less-trusted vendors may tamper with the results and/or parameters of quantum circuits, providing the user with a sub-optimal solution or incurring a cost of higher iterations. In this paper, we model and simulate adversarial tampering of input parameters and measurement outcomes on an exemplary hybrid quantum classical algorithm namely, Quantum Approximate Optimization Algorithm (QAOA). We observe a maximum performance degradation of . To achieve comparable performance with minimal parameter tampering, the user incurs a minimum cost of 20X higher iteration. We propose distributing the computation (iterations) equally among the various hardware options to ensure trustworthy computing for a mix of trusted and untrusted hardware. In the chosen performance metrics, we observe a maximum improvement of ≈ 30%. In addition, we propose re-initialization of the parameters after a few initial iterations to fully recover the original program performance and an intelligent run adaptive split heuristic, which allows users to identify tampered/untrustworthy hardware at runtime and allocate more iterations to the reliable hardware, resulting in a maximum improvement of ≈ .