Cloud-accessible quantum computing has made hardware comparison not only a physics benchmark but also a practical purchasing decision. Cost-aware comparison of quantum computers remains underexplored and is difficult to do under the heterogeneous billing models offered by various cloud-based quantum computing providers. This paper makes two main contributions to enable price-aware comparison of quantum computers. First, this work presents a cross-provider measurement study of quantum circuit execution fidelity spanning 14 cloud QPU access-path entries (12 distinct physical QPUs) across four cloud access paths: Amazon Web Services (AWS) cloud, IBM Quantum Runtime (IBM) cloud, IQM Resonance (IQM) cloud, and Oxford Quantum Circuits (OQC) cloud. Second, this work proposes and analyzes a cost-aware score, Quantum Fidelity-per-Cost (QFC), which combines Kullback--Leibler (KL) divergence from an ideal output distribution, shot count, and monetary cost into one possible metric under a documented billing model. The main empirical observation from this work is that cost-aware ranking can differ from purely fidelity-based evaluation of quantum computers, and that users may select different quantum computing backends when they consider price in their selection, as opposed to selection based on fidelity alone. This work shows that the ranking is stable under reweighting of the metric, and that a device's billing model, not its hardware, governs how its score scales with shot count. Reported QFC values change as new machines come online or as providers revise their prices.
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7 pages, 4 figures, 3 tables. Accepted at IEEE International Conference on Quantum Computing and Engineering (QCE) 2026
Cloud-accessible quantum computing has made hardware comparison not only a physics benchmark but also a practical purchasing decision. Cost-aware comparison of quantum computers remains underexplored and is difficult to do under the heterogeneous billing models offered by various cloud-based quantum computing providers. This paper makes two main contributions to enable price-aware comparison of quantum computers. First, this work presents a cross-provider measurement study of quantum circuit execution fidelity spanning 14 cloud QPU access-path entries (12 distinct physical QPUs) across four cloud access paths: Amazon Web Services (AWS) cloud, IBM Quantum Runtime (IBM) cloud, IQM Resonance (IQM) cloud, and Oxford Quantum Circuits (OQC) cloud. Second, this work proposes and analyzes a cost-aware score, Quantum Fidelity-per-Cost (QFC), which combines Kullback--Leibler (KL) divergence from an ideal output distribution, shot count, and monetary cost into one possible metric under a documented billing model. The main empirical observation from this work is that cost-aware ranking can differ from purely fidelity-based evaluation of quantum computers, and that users may select different quantum computing backends when they consider price in their selection, as opposed to selection based on fidelity alone. This work shows that the ranking is stable under reweighting of the metric, and that a device's billing model, not its hardware, governs how its score scales with shot count. Reported QFC values change as new machines come online or as providers revise their prices.
Cloud-accessible quantum computing has made heterogeneous Quantum Processing Units (QPUs) available through provider platforms that differ in qubit technology, calibration state, queueing behavior, and pricing model. This heterogeneity makes execution management a quantum-engineering concern: a quantum circuit may be executable on several backends, but the resulting cost, latency, fidelity, and reproducibility depend on backend selection and on how measurement shots are allocated. This paper presents the Quantum Broker, an open-source and provider-agnostic framework for Quality-of-Service-aware quantum execution management. The framework operationalizes shot-wise distribution by exploiting the statistical independence of quantum measurement shots to dispatch a single circuit across multiple heterogeneous QPUs according to user-defined constraints and optimization objectives. It combines a modular execution architecture, interchangeable backend-profiling strategies, and the Quantum QoS Specification Language (QQSpec), a declarative language for expressing QoS requirements. Dispatch planning is supported by two solver backends: a mixed-integer linear-programming (MILP) solver, and a nonlinear dual-annealing solver. Experiments on benchmark quantum circuits and noisy simulator backends show that the Quantum Broker can produce feasible QoS-aware dispatch plans under heterogeneous backend conditions and can improve QoS satisfaction with respect to single-backend and naive multi-backend baselines. The results also expose a practical trade-off between planning time and dispatch quality, with the nonlinear solver producing higher-quality allocations at increased computational cost. Overall, the Quantum Broker provides an extensible execution-management layer for engineering portable, reproducible, and QoS-aware quantum software over fragmented quantum cloud infrastructures.
This work presents the first thorough exploration of how reset operations in cloud-based quantum computers could be exploited to run quantum circuits for free. This forms a new type of attack on the economics of cloud-based quantum computers. All major quantum computing companies today offer access to their hardware through some type of cloud-based service. Due to the noisy nature of quantum computers, a quantum circuit is run many times to collect the output statistics, and each run is called a shot. The fees users pay for access to the machines typically depend on the number of these shots of a quantum circuit that are executed. Per-shot pricing is a clean and straightforward approach as users are charged a small fee for each shot of their circuit. This work demonstrates that per-shot pricing can be exploited to get circuits to run for free when users abuse recently implemented mid-circuit qubit measurement and reset operations. Through evaluation on real, cloud-based quantum computers this work shows how multiple circuits can be executed together within a shot, by separating each user circuit by set of reset operations and submitting all the circuits, and reset operations, as one larger circuit. As a result, the user is charged per-shot pricing, even though inside each shot are multiple circuits. Total per-shot cost to run certain circuits could be reduced by up to 900\% using methods proposed in this work, leading to significant financial losses to quantum computing companies. To address this novel finding, this work proposes a clear approach for how users should be charged for their execution, while maintaining the flexibility and usability of the mid-circuit measurement and reset~operations.
Cloud-accessible quantum processors enable direct execution of quantum algorithms on heterogeneous hardware platforms. Unlike classical systems, however, identical quantum circuits may exhibit substantially different behavior across devices due to architectural variations in qubit connectivity, gate fidelity, and coherence times.
In this work, we systematically benchmark five representative quantum algorithms - Bell state preparation, GHZ state generation, Quantum Fourier Transform (QFT), Grover's Search, and the Quantum Approximate Optimization Algorithm (QAOA) - across trapped-ion, superconducting, and simulator backends using Amazon Braket. Performance metrics including fidelity, CHSH violation, success probability, circuit depth, and gate counts are evaluated.
Our results demonstrate a strong dependence of algorithmic performance on hardware topology and noise characteristics. For example, 10-qubit GHZ states achieved fidelities above 0.8 on trapped-ion hardware, while superconducting platforms dropped below 0.15 due to routing overhead and accumulated two-qubit gate errors. These findings highlight the importance of hardware-aware algorithm selection and provide practical guidance for benchmarking in the NISQ era.
Context: Quantum computing is rapidly evolving, offering new opportunities for solving problems in optimization, cryptography, and simulation. However, the limited availability of quantum resources makes efficient utilization of quantum hardware a current challenge. Today’s paradigms often lead to under-utilization of qubits, increased costs, and execution delays, especially in the NISQ era. Objective: This work aims to improve the utilization of quantum hardware by introducing an execution model that integrates multiprogramming at circuit level with quantum shot-wise distribution in a single policy-driven pipeline. Methods: An architecture has been implemented that combines circuit scheduling and shot distribution techniques to aggregate multiple circuits and distribute their shots across heterogeneous QPUs. The approach was empirically validated on actual IBM Quantum devices using a diverse set of reference circuits. Results: The proposal achieved a reduction in cost of 95% and a reduction in tasks 92%. Moreover, the fidelity analysis of the results showed an increase in noise, with an average increase of approximately 20% using different statistical distances. Conclusions: This research provides a usable and extensible solution to increase the efficiency, cost effectiveness, and resilience of quantum workload execution in heterogeneous and dynamic cloud environments. These results obtained suggest that users should weigh the implications of fidelity versus cost (and time) savings based on the application requirements and their goals.
Quantum Computing (QC) has evolved from a few custom quantum computers, which were only accessible to their creators, to an array of commercial quantum computers that can be accessed on the cloud by anyone. Accessing these cloud quantum computers requires a complex chain of tools that facilitate connecting, programming, simulating algorithms, estimating resources, submitting quantum computing jobs, retrieving results, and more. Some steps in the chain are hardware dependent and subject to change as both hardware and software tools, such as available gate sets and optimizing compilers, evolve. Understanding the trade-offs inherent in this process is essential for evaluating the power and utility of quantum computers. ARLIS has been systematically investigating these environments to understand these complexities. The work presented here is a detailed summary of three months of using such quantum programming environments. We show metadata obtained from these environments, including the connection metrics to the different services, the execution of algorithms, the testing of the effects of varying the number of qubits, comparisons to simulations, execution times, and cost. Our objective is to provide concrete data and insights for those who are exploring the potential of quantum computing. It is not our objective to present any new algorithms or optimize performance on any particular machine or cloud platform; rather, this work is focused on providing a consistent view of a single algorithm executed using out-of-the-box settings and tools across machines, cloud platforms, and time. We present insights only available from these carefully curated data.
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%of the qubit links in IBM Sherbrooke and80%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%)accuracy for single qubit (2 qubit) gate error rates.