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arXiv:2309.06005

Distributed Scheduling of Quantum Circuits with Noise and Time Optimization

Debasmita Bhoumik, Ritajit Majumdar, Amit Saha, Susmita Sur‐Kolay

Published Sep 12, 2023• 5 citations
arXivHTMLPDF

Abstract

Quantum computers are currently noisy, particularly without error correction and fault tolerance. Methods like error suppression and mitigation are widely used to improve performance. Circuit cutting, which partitions a circuit into smaller subcircuits, can also reduce noise. In this paper, we propose an Integer Linear Program (ILP) based scheduler for optimizing subcircuit schedules on available hardware. The goal is to maximize overall fidelity and ensure each hardware does not exceed its predefined execution time. For 10-qubit circuits, our method achieves an average fidelity improvement of ~12.3% and ~21% with and without measurement error mitigation, respectively, even with minimal execution time. Additionally, we introduce a polynomial-time graph-theoretic scheduling method that matches the ILP scheduler's results when the number of subcircuits does not exceed the number of hardware units, each with minimal execution time. This noise and time-optimized scheduler represents a crucial step towards optimal quantum computing performance, especially with limited hardware access.

DOI
10.48550/arxiv.2309.06005

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•Sep 5, 2026•0 citations
Quantum computers are currently noisy, particularly without error correction and fault tolerance. Methods like error suppression and mitigation are widely used to improve performance. Circuit cutting, which partitions a circuit into smaller subcircuits, can also reduce noise. In this paper, we propose an Integer Linear Program (ILP) based scheduler for optimizing subcircuit schedules on available hardware. The goal is to maximize overall fidelity and ensure each hardware does not exceed its predefined execution time. For 10-qubit circuits, our method achieves an average fidelity improvement of ~12.3% and ~21% with and without measurement error mitigation, respectively, even with minimal execution time. Additionally, we introduce a polynomial-time graph-theoretic scheduling method that matches the ILP scheduler's results when the number of subcircuits does not exceed the number of hardware units, each with minimal execution time. This noise and time-optimized scheduler represents a crucial step towards optimal quantum computing performance, especially with limited hardware access.
Despite advancements, current quantum hardware faces significant challenges, including limited qubit counts and high susceptibility to noise, which hinder the execution of large, complex algorithms. To address these limitations, multi-node quantum systems and quantum circuit cutting techniques partition large circuits into smaller subcircuits that can be executed on individual quantum machines and then reconstructed using classical resources. However, these methods introduce new challenges, such as the large overhead from subcircuit reconstruction and additional noise from entangled EPR pairs, especially in multi-node quantum systems. In this paper, we propose the Efficient Circuit Cutting and Scheduling (EC2S) system, which integrates EPR pairs with circuit cutting to address these issues. EC2S improves system performance by transitioning from logical to physical EPR pairs and further reduces computational overhead by minimizing the number of subcircuits during the reconstruction phase. \sol~ is implemented using Qiskit and evaluated on both real quantum hardware and various emulators. Compared to the state-of-the-art Qiskit-Addon-Cut, EC2S achieves significant improvements in fidelity, up to 16.7\%, and reduces system-wide expenditure by up to 99.5\%.
Near-term hardware is constrained by high error rates, small qubit counts, and relatively low output fidelity, making the execution of large, high performance quantum circuits difficult. Circuit partitioning (or circuit cutting) has emerged as a promising approach to circumvent these limitations by decomposing circuits into smaller subcircuits at two-qubit interaction points. However, it remains unclear which classes of circuits benefit the most from partitioning and under what hardware conditions it is most effective. In this work, we evaluate the suitability of quantum circuits for partitioning from three perspectives: improving fidelity, enabling distributed execution, and scaling to larger circuit sizes. Specifically, we compare uncut circuit execution against two circuit partitioning approaches: Qiskit's automatic cut finding technique and a custom performance optimized circuit cutting method. We also measure these across GHZ, QFT, brickwork, and random quantum circuits ranging from 4 to 14 qubits, using mean absolute error of expectation values and overall output fidelity. Our results show that partitioning benefits larger, highly interconnected circuits, with our custom method reducing error by up to 55\% and improve fidelity for GHZ circuits, but degrading performance for brickwork circuits at larger scales.
Recent quantum technologies and quantum error-correcting codes emphasize the requirement for arranging interacting qubits in a nearest-neighbor (NN) configuration while mapping a quantum circuit onto a given hardware device, in order to avoid undesirable noise. It is equally important to minimize the wastage of qubits in a quantum hardware device with m qubits while running circuits of n qubits in total, with n < m. In order to prevent cross-talk between two circuits, a buffer distance between their layouts is needed. Furthermore, not all the qubits and all the two-qubit interactions are at the same noise-level. Scheduling multiple circuits on the same hardware may create a possibility that some circuits are executed on a noisier layout than the others. In this paper, we consider an optimization problem which schedules as many circuits as possible for execution in parallel on the hardware, while maintaining a pre-defined layout quality for each. An integer linear programming formulation to ensure maximum fidelity while preserving the nearest neighbor arrangement among interacting qubits is presented. Our assertion is supported by comprehensive investigations involving various well-known quantum circuit benchmarks. As this scheduling problem is shown to be NP Hard, we also propose a greedy heuristic method which provides 2x and 3x better utilization for 27-qubit and 127-qubit hardware devices respectively in terms of qubits and time.
The current execution model of quantum computers, based on serialized task processing, leads to inefficient resource utilization and increased waiting times. This inefficiency persists even as the demand for quantum computing grows. To address this, an automated scheduling approach is proposed through the AutoScheduler library, which replicates quantum circuits to fully occupy the available qubits in a quantum processor. This strategy reduces the number of required shots per execution while optimizing hardware usage. Experimental validation on IBM Quantum and Amazon Braket platforms demonstrates that autoscheduling achieves substantial reductions in execution time and cost, with minimal impact on result fidelity despite inherent quantum noise. These findings suggest that circuit autoscheduling is a viable method for improving the scalability and efficiency of quantum computing in the NISQ era.
Noisy intermediate-scale quantum (NISQ) devices with limited qubit count and connectivity limit the scale of quantum circuits that can be executed. Circuit cutting methods simulate larger quantum computers by decomposing large circuits into small subcircuits that can be executed on NISQ hardware. Existing circuit cutting methods only consider the qubit count of the target hardware for finding the locations of cuts on the circuits. This can result in deeper subcircuits after mapping on the hardware. Fidelity decays exponentially with circuit depth and diminishes the quality of execution. We propose a circuit-cutting framework that reduces variance in the depth of subcircuits after mapping to the hardware. Our method formulates the initial circuit cutting as a multi-objective optimization problem based on an evolutionary search method with the number of cuts and the maximum depth of individual routed subcircuits as fitness objectives. This method is implemented as an extension to the existing Qiskit Circuit Cutting Addon, which allows for integration in existing flows. Our approach achieves reductions in routed subcircuit depth of up to 9.9% across benchmark circuits compared with baseline methods.
This paper presents a novel approach to quantum circuit optimization – self-correcting quantum circuits – that leverages adaptive gate scheduling to mitigate the detrimental effects of noise and decoherence. The core concept involves establishing a closed-loop feedback system where real-time performance measurements of the circuit are utilized to dynamically adjust the execution order of quantum gates. This contrasts with traditional static circuit design or gate scheduling methods which often rely on pre-determined strategies and fail to adapt to unforeseen variations in quantum system behavior. The algorithm incorporates a scheduler that continuously monitors circuit output probabilities and adjusts gate timings and sequences to minimize error propagation. The effectiveness of this approach is demonstrated through a theoretical analysis and a conceptual framework, outlining the key components and operational principles. The primary goal is to create a system capable of actively correcting for errors during execution, enhancing the fidelity and reliability of quantum computations. We propose a mathematical framework for describing the adaptive gate scheduling process, utilizing concepts from Markov Chains and Bayesian Optimization to model the uncertainty and optimize the scheduling strategy. The potential impact of this research lies in enabling the design of more robust and reliable quantum algorithms, particularly for complex computations where error mitigation is crucial.