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arXiv:2507.23165 · IEEE International Conference on Quantum Computing and Engineering (QCE)

A Practical Open-Source Software Stack for a Cloud-Based Quantum Computing System

Norihiro Kakuko, Shun Gokita, Naoyuki Masumoto, Keita Matsumoto, Kosuke MIYAJI, Takafumi Miyanaga, Toshio Mōri, Haruki Nakayama, Keita Sasada, Yasuhito Takamiya, Satoyuki Tsukano, Ryo Uchida, Masaomi Yamaguchi

Published Aug 30, 2025• 4 citations
arXivHTMLPDF

Abstract

Since the late 2010s, quantum computers have become commercially available, and the number of services that users can run remotely via cloud servers is increasing. In Japan, several domestic superconducting quantum computing systems, including our own, began operation in 2023. However, the design of quantum computing systems, especially in the most critical areas near quantum computers, remains largely undisclosed, creating a significant barrier to entry into the quantum computing field. If this situation continues, progress toward standardization, which is essential for guiding quantum computer development, will stall, and it will be difficult to develop a practical quantum computing system that can perform calculations on a supercomputer scale. To address this issue, we propose Open Quantum Toolchain for OPerators and USers (OQTOPUS), a full-stack quantum computing system developed from research with real quantum computers. OQTOPUS is one of the world's largest open-source software projects, covering operational software from cloud-based execution environment construction to system operation. Furthermore, to perform quantum computing effectively and efficiently, it implements key features, such as transpilers, multi-programming, and error mitigation, in an area as close as possible to a quantum computer, an area that system vendors rarely disclose. Finally, this study presents experimental results of applying OQTOPUS to a real quantum computer. OQTOPUS is publicly available on GitHub and will notably lower the barrier to entry into the quantum computing field, contributing to the formation of a quantum computing developer community through open discussion.

DOI
10.1109/qce65121.2025.00092

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Since the late 2010s, quantum computers have become commercially available, and the number of services that users can run remotely via cloud servers is increasing. In Japan, several domestic superconducting quantum computing systems, including our own, began operation in 2023. However, the design of quantum computing systems, especially in the most critical areas near quantum computers, remains largely undisclosed, creating a significant barrier to entry into the quantum computing field. If this situation continues, progress toward standardization, which is essential for guiding quantum computer development, will stall, and it will be difficult to develop a practical quantum computing system that can perform calculations on a supercomputer scale. To address this issue, we propose Open Quantum Toolchain for OPerators and USers (OQTOPUS), a full-stack quantum computing system developed from research with real quantum computers. OQTOPUS is one of the world's largest open-source software projects, covering operational software from cloud-based execution environment construction to system operation. Furthermore, to perform quantum computing effectively and efficiently, it implements key features, such as transpilers, multi-programming, and error mitigation, in an area as close as possible to a quantum computer, an area that system vendors rarely disclose. Finally, this study presents experimental results of applying OQTOPUS to a real quantum computer. OQTOPUS is publicly available on GitHub and will notably lower the barrier to entry into the quantum computing field, contributing to the formation of a quantum computing developer community through open discussion.
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Abstract Quantum computing leverages fundamental quantum-mechanical phenomena—most notably superposition, entanglement, and quantum tunneling—to perform computations in ways that differ fundamentally from classical digital processing. Compared to conventional computing architectures, quantum systems promise significant reductions in computational time and potentially lower energy consumption for specific classes of problems. These advantages, combined with advances in nanoscale engineering and the slowing of traditional hardware scaling trends such as Moore’s Law, have led to substantial commercial and national-security-driven investments in quantum technologies over the past decade. A major milestone was reached when Google announced the experimental demonstration of “quantum supremacy,” showing that a quantum processor could execute a specific computational task infeasible for a classical supercomputer within practical time constraints. Although the demonstrated problem had no immediate practical application, this achievement marked a crucial step toward the development of quantum systems capable of delivering real-world computational advantages and provided a more precise technical framing of the concept of quantum advantage. Despite the fact that the mathematical foundations, programming models, and principal quantum algorithms were established in the 1990s, quantum computing remained largely confined to a specialized research community for decades. Today, the increasing accessibility of cloud-based quantum hardware calls for a broader dissemination of implementation-oriented knowledge among researchers and practitioners in computer science, software engineering, and related fields. The quantum programming paradigm differs fundamentally from classical programming models and often relies on physical and algebraic notations that may pose barriers to traditionally trained computer scientists. This series of articles provides a self-contained and concise introduction to quantum computing and its core algorithms, with a strong emphasis on practical implementation. Given that real quantum processors—such as those accessible via the IBM quantum ecosystem—are now available as cloud services, experimental validation can be conducted both on simulators and on actual hardware for small-scale inputs. While other platforms such as Rigetti and IonQ also offer cloud-based quantum computing services, this review focuses exclusively on the IBM quantum computing ecosystem.
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Despite rapid progress in quantum computing in the last decade, the limited usability of quantum computers remains a major roadblock toward its wider adoption. Current noisy intermediate-scale quantum (NISQ) computers produce highly erroneous program outputs for quantum-advantage-proven algorithms - that is, algorithms that are infeasible or orders of magnitude slower on classical supercomputing and high-performance computing (HPC) clusters. Unfortunately, currently, quantum computing programmers lack robust system software tools and methods to make meaningful use of erroneous program executions on quantum computers. This lack of capability is the core motivation behind the fundamental question this dissertation poses: "Can we build system software tools for programmers to make the quantum program execution and output meaningful on NISQ machines?" This dissertation answers this question in the affirmative - experimentally demonstrating on real-system quantum computers that it is possible to extract near-accurate program output from noisy executions on today's erroneous quantum computers, ironically using classical HPC resources and knowledge. This dissertation demonstrates how to achieve this goal without requiring user intervention, domain knowledge about quantum algorithms, or additional quantum hardware support. Unfortunately, as this dissertation uncovers, progressing toward making quantum computers usable is a double-edged sword. In the near future, only a few entities in the world may have access to powerful quantum computers, and these quantum computers will be used to solve previously-unsolved large-scale optimization problems, possibly without an explicit trust model between the service provider and the customer. Therefore, this dissertation envisions that the solutions to such large-scale optimization problems will be considered sensitive and will need to be protected. This dissertation takes the first few steps toward preparing us for that future - by developing a novel method that intelligently obfuscates near-accurate program output and quantum circuit structure to preserve a customer's privacy under a specified computation model and resource availability. The approaches introduced in this dissertation open up new research avenues for hybrid quantum-classical computing. This dissertation also aims to lower the barrier to entry for quantum computing research for the experimental computer systems and HPC community by open-sourcing multiple novel datasets and software frameworks implemented for real-system quantum computers.--Author's abstract
Abstract Quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. To realize this promise, these new capabilities need software solutions that are able to effectively harness its power. However, developers may face significant challenges when developing and executing quantum software due to the limited availability of quantum computer hardware, high computational demands of simulating quantum computers on classical systems, and complicated technology stack to enable currently available accelerators into development environments. These limitations make it difficult for the developer to create an efficient workflow for quantum software development. In this paper, we investigate the potential of using remote computational capabilities in an efficient manner to improve the workflow of quantum software developers, by lowering the barrier of moving between local execution and computationally more efficient remote hardware and offering speedup in execution with simulator surroundings. The goal is to allow the development of more complex circuits and to support an iterative software development approach. In our experiment, with the solution presented in this paper, we have obtained up to 5 times faster circuit execution runtime, and enabled qubit ranges from 21 to 29 qubits with a simple plug-and-play kernel for the Jupyter notebook.