[论文解读] HALO: A Fine-Grained Resource Sharing Quantum Operating System

John Zhuoyang Ye, Jiyuan Wang+2 more|arXiv (Cornell University)|Feb 6, 2026

Quantum Computing Algorithms and Architecture被引用 0

一句话总结

HALO 通过共享辅助量子比特并采用拍数感知调度，实现对量子硬件的细粒度资源共享，在真实硬件上达到更高的利用率与吞吐量，同时带来中等的保真度损失。

ABSTRACT

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.

研究动机与目标

• 在云时代量子计算中，面临多用户、设备有限时，推动高效、可扩展的量子硬件利用需求的动机。
• 提出一种设计，使多进程之间实现量子资源在时空上的精细共享。
• 开发一种面向硬件的量子比特共享机制和一个拍数自适应调度器，以提升吞吐量和利用率。
• 在真实硬件上对 HALO 进行评估，并与现有最先进系统比较，以证明其实用性与收益。

提出的方法

• 开发一个考虑数据比特放置、辅助比特重用、路由和串扰的代价函数的硬件感知多进程映射模型。
• 引入一个拍数感知的批量调度器，基于数据比特占用与拍次数要求形成进程批次，以最大化时空利用率。
• 实现带有仿真退火布局优化的多进程共享数据比特空间管理器。
• 实现一个动态辅助比特调度器，采用轮询指令排序和强制比特复位以确保进程隔离。
• 提供 HALO 系统调用，用于分配数据比特与辅助比特并设置拍次数，以支持资源的虚拟化。

Figure 1 : Sharing helper qubits can in principle increase system throughput for applications need a lot of helper qubits.

实验结果

研究问题

• RQ1 HALO 是否能容忍对细粒度硬件控制的调度时间开销？
• RQ2 与 IBM Quantum 与 HyperQ 相比，HALO 是否提升了空间利用率与吞吐量？
• RQ3 共享辅助比特是否能显著提升利用率与保真度？
• RQ4 拍数感知调度是否相比非拍数感知调度提高了时空效用（η）？
• RQ5 在不同数据占用和路由成本下，吞吐量如何影响进程保真度？

主要发现

Method	Utilization	Throughput	Fidelity Loss
HALO	2.44×	4.44×	≤33%
IBM Quantum (baseline)	1×	1×	—
HyperQ (baseline)	—	—	—

• HALO 将硬件利用率提升至现有系统的最高 2.44×，吞吐量提升至最高 4.44×，同时将保真度损失控制在 33% 以内。
• 共享辅助比特并采用时空共享，使得并行性比独占模型更加细粒度。
• 拍数感知批量调度在评估场景中显著提高时空效用（η），提升因子在 2.87–4.04 之间。
• 数据比特占用参数 lambda 控制利用率与保真度之间的权衡；更高的共享增加吞吐量和路由成本。
• HALO 在所示基准测试中，在利用率和吞吐量方面优于 IBM Quantum 与 HyperQ。

Figure 2 : The work flow diagram of HALO scheduler in our resource sharing quantum operating system. It takes user’s processes as input and send scheduled quantum gate instructions to the quantum hardwares.