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arXiv:2406.19120

QOS: A Quantum Operating System

Emmanouil Giortamis, Francisco Romão, Nathaniel Tornow, Pramod Bhatotia

Published Jun 27, 2024• 1 citation
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Abstract

Quantum computers face challenges due to hardware constraints, noise errors, and heterogeneity, and face fundamental design tradeoffs between key performance metrics such as \textit{quantum fidelity} and system utilization. This substantially complicates managing quantum resources to scale the size and number of quantum algorithms that can be executed reliably in a given time. We introduce QOS, a cloud operating system for managing quantum resources while mitigating their inherent limitations and balancing the design tradeoffs of quantum computing. QOS exposes a hardware-agnostic API for transparent quantum job execution, mitigates hardware errors, and systematically multi-programs and schedules the jobs across space and time to achieve high quantum fidelity in a resource-efficient manner. To achieve this, it leverages two key insights: First, to maximize utilization and minimize fidelity loss, some jobs are more compatible than others for multi-programming on the same quantum computer. Second, sacrificing minimal fidelity can significantly reduce job waiting times. We evaluate QOS on real quantum devices hosted by IBM, using 7000 real quantum runs of more than 70.000 benchmark instances. We show that the QOS achieves 2.6--456.5$\times$ higher fidelity, increases resource utilization by up to 9.6$\times$, and reduces waiting times by up to 5$\times$ while sacrificing only 1--3\% fidelity, on average, compared to the baselines.

DOI
10.48550/arxiv.2406.19120

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Quantum computers face challenges due to hardware constraints, noise errors, and heterogeneity, and face fundamental design tradeoffs between key performance metrics such as \textit{quantum fidelity} and system utilization. This substantially complicates managing quantum resources to scale the size and number of quantum algorithms that can be executed reliably in a given time. We introduce QOS, a cloud operating system for managing quantum resources while mitigating their inherent limitations and balancing the design tradeoffs of quantum computing. QOS exposes a hardware-agnostic API for transparent quantum job execution, mitigates hardware errors, and systematically multi-programs and schedules the jobs across space and time to achieve high quantum fidelity in a resource-efficient manner. To achieve this, it leverages two key insights: First, to maximize utilization and minimize fidelity loss, some jobs are more compatible than others for multi-programming on the same quantum computer. Second, sacrificing minimal fidelity can significantly reduce job waiting times. We evaluate QOS on real quantum devices hosted by IBM, using 7000 real quantum runs of more than 70.000 benchmark instances. We show that the QOS achieves 2.6--456.5×\times× higher fidelity, increases resource utilization by up to 9.6×\times×, and reduces waiting times by up to 5×\times× while sacrificing only 1--3% fidelity, on average, compared to the baselines.
We describe Qonductor, a cloud orchestrator for hybrid quantum-classical applications that run on heterogeneous hybrid resources. Qonductor abstracts away the complexity of hybrid programming and resource management by exposing the Qonductor API, a high-level and hardware-agnostic API. The resource estimator strategically balances quantum and classical resources to mitigate resource contention and the effects of hardware noise. The hybrid scheduler automates job scheduling on hybrid resources and balances the tradeoff between users’ objectives of QoS and the cloud operator’s objective of resource efficiency.
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.
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Quantum cloud platforms need to dynamically orchestrate workloads across heterogeneous quantum computation backends whose noise profiles, qubit topologies, and queues vary over time. Existing orchestrators use noise-agnostic heuristics that ignore backend-specific errors, causing reduced execution fidelity, load imbalance, and frequent rescheduling. To address these challenges, we propose QCOEM - a Quantum Cloud Orchestration framework that leverages Evolutionary algorithms for Multi-objective optimization of quantum task scheduling. We compare NSGA-II and NSGA-III for jointly minimizing mean completion time, execution error rate, and load imbalance. To select schedules from a non-convex Pareto front, we apply an Augmented Achievement Scalarization Function (AASF) as a preference-based decision rule that maps the Pareto set to a single dispatchable schedule aligned with user priorities. Our extensive performance evaluation in a heterogeneous quantum cloud environment shows zero task rescheduling and about 30% higher mean fidelity than noise-agnostic heuristics, while maintaining bounded scheduling overhead. The experiment results indicate that our QCOEM framework can deliver stable, high-fidelity execution and lightweight resource management for quantum cloud computing.
Inspired by the ideas of quantum physics, quantum computing is a modern computational paradigm. It is one of the rapidly growing areas in computer science, mathematics, and physics. It has high speed and efficiency by making use of phenomena such as superposition, entanglement, and interference, allowing new possibilities in areas like cryptography, machine learning, and complex system simulations. Even though it has great potential, quantum computing also suffers from resource management and task scheduling issues, system design due to the differences in the foundational basis between classical and quantum computing. This study reviews the architecture and workflow of QOS (quantum operating system), emphasizing the integration of quantum and classical resources. In this study, elements such as quantum resource management, automatic qubit calibration, quantum program compilation, and hybrid task scheduling are investigated. The present study also looks at innovative architectures such as quantum FPGAs, x86-based quantum systems, and distributed quantum systems, which provide advanced quantum computations while being compatible with classical systems. The findings of this paper reveal how QOS improves fidelity, resource utilization, and task scheduling. We have also explored key quantum algorithms such as Grover’s and Shor’s algorithms, highlighting their potential in NP-hard problems[1], quantum machine learning, and optimization challenges. This research addresses the transformative power of QOS in solving real problems in computation, providing robust solutions in the management and programming of quantum resources. This research advances the development of future quantum computing systems by evaluating existing designs and suggesting improvements in task scheduling, qubit accuracy, and quantum algorithm performance. The findings in this paper seek to contribute to the advancement of understanding of QOS and inspire innovation in several fields.