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
This paper provides a comprehensive analysis of three months of experience with cloud quantum computing environments, offering detailed metadata, performance metrics, and insights into the trade-offs and complexities involved.
Contribution
It systematically documents the performance, connectivity, and resource utilization of cloud quantum computers over three months, providing valuable data for future research and development.
Findings
Connectivity metrics vary across platforms
Execution times depend on qubit count and hardware
Cost and resource usage insights are provided
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…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Cloud Computing and Resource Management · Big Data and Digital Economy
