Three Months in the Life of Cloud Quantum Computing
Darrell Teegarden, Allison Casey, F. Gino Serpa, Patrick Becker, Asmita Brahme, Saanvi Kataria, Paul Lopata
TL;DR
The paper investigates the practical usability and resource requirements of cloud quantum computers by benchmarking Azure Quantum and AWS Braket over three months using a QFT-based circuit benchmark. It establishes an automated, Python-based data collection framework that runs default configurations across multiple vendors and devices, storing results in a normalized database for cross-provider comparison and reproducibility. Key findings show wide variability in queue wait times, machine availability, and fidelity across machines and time, as well as stark cost–fidelity trade-offs driven by transpiler choices and pricing models. The work highlights operational challenges in the current cloud QC ecosystem and provides actionable guidance for users and providers, including a reusable benchmarking framework that can be extended to additional services and future platforms. Overall, the study advances practical understanding of cloud QC adoption and offers a data-driven basis for optimizing workload placement and budgeting in real-world quantum computing deployments.
Abstract
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.