As quantum computing enters the cloud era, thousands of users must share access to a small number of quantum processors. Users need to wait minutes to days to start their jobs, which only takes a few seconds for execution. Current quantum cloud platforms employ a fair-share scheduler, as there is no way to multiplex a quantum computer among multiple programs at the same time, leaving many qubits idle and significantly under-utilizing the hardware. This imbalance between high user demand and scarce quantum resources has become a key barrier to scalable and cost-effective quantum computing. We present HALO, the first quantum operating system design that supports fine-grained resource-sharing. HALO introduces two complementary mechanisms. First, a hardware-aware qubit-sharing algorithm that places shared helper qubits on regions of the quantum computer that minimize routing overhead and avoid cross-talk noise between different users' processes. Second, a shot-adaptive scheduler that allocates execution windows according to each job's sampling requirements, improving throughput and reducing latency. Together, these mechanisms transform the way quantum hardware is scheduled and achieve more fine-grained parallelism. We evaluate HALO on the IBM Torino quantum computer on helper qubit intense benchmarks. Compared to state-of-the-art systems such as HyperQ, HALO improves overall hardware utilization by up to 2.44x, increasing throughput by 4.44x, and maintains fidelity loss within 33%, demonstrating the practicality of resource-sharing in quantum computing.

翻译：随着量子计算进入云时代，数千名用户必须共享少量量子处理器的访问权限。用户需要等待数分钟至数天才能启动其作业，而这些作业的执行时间仅需数秒。当前量子云平台采用公平共享调度器，由于无法在多个程序间同时复用同一量子计算机，导致大量量子比特闲置，硬件利用率严重不足。这种高用户需求与稀缺量子资源之间的不平衡已成为可扩展且经济高效的量子计算的关键障碍。本文提出HALO，首个支持细粒度资源共享的量子操作系统设计。HALO引入两种互补机制：首先，一种硬件感知的量子比特共享算法，将共享辅助量子比特放置在量子计算机上能最小化路由开销并避免不同用户进程间串扰噪声的区域；其次，一种基于测量次数自适应的调度器，根据各作业的采样需求分配执行窗口，从而提升吞吐量并降低延迟。这些机制共同改变了量子硬件的调度方式，实现了更细粒度的并行化。我们在IBM Torino量子计算机上使用辅助量子比特密集型基准测试对HALO进行评估。与HyperQ等先进系统相比，HALO将整体硬件利用率提升最高达2.44倍，吞吐量提高4.44倍，并将保真度损失控制在33%以内，证明了资源共享在量子计算中的实用性。