Undoubtedly, quantum computing offers valuable acceleration for solving intricate problems. One of the primary hurdles lies in executing large-scale quantum applications on backend machines. Qubit noise, among other factors, dramatically influences the execution process. Implementing effective scheduling techniques for quantum circuits is crucial for practical quantum computing and preventing excessive waiting times. The quantum realm is distinct from classical computing in terms of optimization, performance, utilization, and waiting periods. Consequently, the parameters and components of quantum circuit scheduling diverge from those of classical computing. This paper presents Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput, a straightforward yet effective scheduling framework and policy that enhances noise resilience, throughput, and the fidelity of quantum circuits. Drawing inspiration from classical methods, our scheduling approach incorporates additional constraints tailored for quantum logic. The outcome demonstrates a substantial improvement in fidelity and resource management, which is vital for real-world quantum applications.
Undoubtedly, quantum computing offers valuable acceleration for solving intricate problems. One of the primary hurdles lies in executing large-scale quantum applications on backend machines. Qubit noise, among other factors, dramatically influences the execution process. Implementing effective scheduling techniques for quantum circuits is crucial for practical quantum computing and preventing excessive waiting times. The quantum realm is distinct from classical computing in terms of optimization, performance, utilization, and waiting periods. Consequently, the parameters and components of quantum circuit scheduling diverge from those of classical computing. This paper presents Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput, a straightforward yet effective scheduling framework and policy that enhances noise resilience, throughput, and the fidelity of quantum circuits. Drawing inspiration from classical methods, our scheduling approach incorporates additional constraints tailored for quantum logic. The outcome demonstrates a substantial improvement in fidelity and resource management, which is vital for real-world quantum applications.
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.
The susceptibility to decoherence is significantly high in quantum computing systems, and the latter effect significantly compromises the computational reliability, particularly in Noisy Intermediate-Scale Quantum (NISQ) computers. Even though the existing mitigation strategies are currently focused on hardware-level error correction and pulselevel noise mitigation, system-level execution variables (by far, the most notable of them being waiting times caused by queues) can also contribute to the worsening of fidelity degradation, but this is hardly addressed. In its turn, this manuscript suggests an AI-based decoherence mitigation framework, in which the time spent in the queue is incorporated into fidelity prediction and scheduling. The framework elucidates the interrelations between quantum-state fidelity and waiting time through a physics-informed decoherence formulation, which is followed by the use of machine-learning to predict degradation of fidelity prior to circuits execution. Based on the estimated measures of fidelity, decoherence-aware scheduling algorithms are presented to either give priority or re-scheduling to quantum jobs that become highly disturbed by the coherence loss. The viability of integrating the AI-based predictive mechanisms into the system-level quantum computing processes is shown through simulation-based assessment, which indicates that the suggested methodology can improve the effective fidelity of the implemented quantum jobs as compared to the traditional scheduling approaches. The findings indicate that AI-supported software-level alleviation mechanisms can be used as an effective and efficient enhancement of the dependability of nearterm quantum computing systems due to their scalability and use of hardware as the basis of the error reduction approach.
Quantum systems have potential to demonstrate significant computational advantage, but current quantum devices suffer from the rapid accumulation of error that prevents the storage of quantum information over extended periods. The unintentional coupling of qubits to their environment and each other adds significant noise to computation, and improved methods to combat decoherence are required to boost the performance of quantum algorithms on real machines. While many existing techniques for mitigating error rely on adding extra gates to the circuit, calibrating new gates, or extending a circuit's runtime, this paper's primary contribution leverages the gates already present in a quantum program without extending circuit duration. We exploit circuit slack for single-qubit gates that occur in idle windows, scheduling the gates such that their timing can counteract some errors. Spin-echo corrections that mitigate decoherence on idling qubits act as inspiration for this work. Theoretical models, however, fail to capture all sources of noise in NISQ devices, making practical solutions necessary that better minimize the impact of unpredictable errors in quantum machines. This paper presents TimeStitch: a novel framework that pinpoints the optimum execution schedules for single-qubit gates within quantum circuits. TimeStitch, implemented as a compilation pass, leverages the reversible nature of quantum computation to boost the success of circuits on real quantum machines.
The advent of quantum computing promises transformative advancements across various scientific and technological domains. However, realizing this potential hinges critically on the efficient execution of quantum algorithms. A significant hurdle lies in the intricate orchestration of quantum resources – qubits, coherence times, and gate fidelity – to meet the demanding requirements of complex quantum algorithms. Traditional scheduling methods often fail to adequately account for these constraints, leading to suboptimal performance and increased execution times. This paper proposes a novel approach to quantum algorithm scheduling by formulating the problem as a Constraint Programming (CP) model. This approach leverages the power of CP solvers to systematically explore the solution space, identifying schedules that maximize resource utilization while adhering to stringent hardware limitations. We introduce a framework for representing quantum algorithms and their associated constraints within a CP environment, enabling automated optimization. The core of the method involves defining constraints related to qubit connectivity, coherence time limitations, and gate fidelity errors. The resulting CP model is then solved using state-of-the-art CP solvers, producing optimized schedules that demonstrate significant improvements in resource efficiency compared to naive scheduling strategies. This work represents a substantial advance in quantum algorithm scheduling, offering a robust and scalable solution for harnessing the full power of quantum hardware.
Achieving high-precision quantum computation requires effective suppression of idling errors that occur when qubits remain inactive during waiting periods within a quantum circuit. Conventional mitigation techniques, such as dynamical decoupling, suppress decoherence by periodically refreshing quantum states through the insertion of additional control gates. In this paper, we propose an alternative approach that suppresses idling errors through quantum circuit scheduling without introducing any additional gate operations. By appropriately adjusting the execution timing of quantum gates with scheduling flexibility, we demonstrate through both numerical simulations and hardware experiments that the overall computational accuracy can be significantly influenced and, in many cases, improved. In addition, we analytically derive the density-matrix evolution under idling noise and provide a theoretical framework that explains the observed behavior.
Quantum systems have the potential to demonstrate significant computational advantage, but current quantum devices suffer from the rapid accumulation of error that prevents the storage of quantum information over extended periods. The unintentional coupling of qubits to their environment and each other adds significant noise to computation, and improved methods to combat decoherence are required to boost the performance of quantum algorithms on real machines. While many existing techniques for mitigating error rely on adding extra gates to the circuit [ 13 , 20 , 56 ], calibrating new gates [ 50 ], or extending a circuit’s runtime [ 32 ], this article’s primary contribution leverages the gates already present in a quantum program without extending circuit duration. We exploit circuit slack for single-qubit gates that occur in idle windows, scheduling the gates such that their timing can counteract some errors. Spin-echo corrections that mitigate decoherence on idling qubits act as inspiration for this work. Theoretical models, however, fail to capture all sources of noise in Noisy Intermediate Scale Quantum devices, making practical solutions necessary that better minimize the impact of unpredictable errors in quantum machines. This article presents TimeStitch: a novel framework that pinpoints the optimum execution schedules for single-qubit gates within quantum circuits. TimeStitch, implemented as a compilation pass, leverages the reversible nature of quantum computation to boost the success of circuits on real quantum machines. Unlike past approaches that apply reversibility properties to improve quantum circuit execution [ 35 ], TimeStitch amplifies fidelity without violating critical path frontiers in either the slack tuning procedures or the final rescheduled circuit. On average, compared to a state-of-the-art baseline, a practically constrained TimeStitch achieves a mean 38% relative improvement in success rates, with a maximum of 106%, while observing bounds on circuit depth. When unconstrained by depth criteria, TimeStitch produces a mean relative fidelity increase of 50% with a maximum of 256%. Finally, when TimeStitch intelligently leverages periodic dynamical decoupling within its scheduling framework, a mean 64% improvement is observed over the baseline, relatively outperforming stand-alone dynamical decoupling by 19%, with a maximum of 287%.