What is it about?
Quantum computers are expensive resources, so running multiple programs at the same time can help improve their efficiency. However, preparing and scheduling these programs can introduce significant delays. This paper presents FLAMENCO, a system that enables multiple quantum programs to share a quantum computer with minimal scheduling overhead. By preparing programs in advance and quickly adapting to incoming requests, FLAMENCO makes quantum computing faster and more efficient without compromising the reliability of computation.
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Why is it important?
As quantum computers become more accessible through cloud services, efficiently sharing their limited hardware resources among multiple users is increasingly important. Existing approaches often introduce substantial scheduling delays, limiting their ability to respond quickly to incoming requests. FLAMENCO addresses this challenge through a system architecture that combines advance program preparation with low-latency runtime scheduling. Our results demonstrate that efficient multiprogramming can significantly reduce execution delays while maintaining computational reliability, paving the way for more responsive and scalable quantum computing services.
Perspectives
In classical computing, applications are typically distributed as precompiled executables. Users can run these applications repeatedly without triggering recompilation at runtime. Although this seems like a natural and fundamental feature of modern computing, achieving a similar programming and execution model remains challenging in quantum computing. In this work, we explore a possible approach to bridging this gap, with the long-term goal of enabling quantum applications to be easily invoked and repeatedly executed by different users, much like conventional software today.
Yilun Zhao
Institute of Computing Technology, Chinese Academy of Sciences
Read the Original
This page is a summary of: A System Architecture for Low Latency Multiprogramming Quantum Computing, ACM Transactions on Architecture and Code Optimization, October 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3845611.
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