HALO: A Fine-Grained Resource Sharing Quantum Operating System

2026-02-06

AI Summary

This work addresses the significant disparity between queuing and execution times in quantum cloud platforms, where existing schedulers lack concurrency support, leading to underutilized qubits and low hardware efficiency. To overcome this, the authors propose HALO, the first quantum operating system enabling fine-grained resource sharing. HALO integrates hardware-aware qubit placement with ancilla-sharing strategies, crosstalk-resilient spatial partitioning, and an adaptive execution window scheduler driven by sampling requirements. Experiments on the IBM Torino quantum processor demonstrate that HALO achieves up to 2.44× higher hardware utilization and 4.44× greater throughput compared to state-of-the-art systems like HyperQ, while limiting fidelity degradation to within 33%. This represents the first practical demonstration of efficient concurrent execution for multi-user quantum workloads.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• quantum computing
• resource sharing
• qubit utilization
• quantum cloud
• hardware under-utilization

Innovation

• fine-grained resource sharing
• qubit-sharing algorithm
• shot-adaptive scheduler
• quantum operating system
• hardware utilization