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Dy-Part: A Dynamic, Noise-Aware Scheduler for Optimizing Hybrid Quantum-Classical Algorithms

Mufakir Qamar Ansari

Published Nov 18, 2025• 0 citations
Publisher

Abstract

Abstract Hybrid quantum-classical algorithms are a leading approach for applying noisy intermediate-scale quantum (NISQ) devices to practical problems. A key challenge in this paradigm is determining how to partition large computational problems into smaller sub-tasks that can be executed on size-limited quantum hardware. Current methods often rely on static, predetermined partitioning strategies that primarily consider the problem's structure, often failing to adapt to the quantum processor's dynamic noise characteristics. In this work, we introduce and analyze a dynamic scheduling framework, named Dy-Part, designed to address this challenge. Dy-Part automates the partitioning decision by using a heuristic cost model that balances two competing factors: the expected infidelity of the quantum computation, which increases with circuit size, and the classical post-processing overhead, which increases as the problem is broken into more pieces. To find the optimal partition size that minimizes this cost, our framework employs an efficient ternary search, whose output guides a fast, greedy graph partitioning heuristic. We demonstrate and validate this approach using the Variational Quantum Eigensolver (VQE) to solve the Max-Cut problem. Our results, averaged over 30 random graph instances, show that while a static strategy is effective in low-noise regimes, Dy-Part provides a more robust solution as noise increases. For instance, on 12-node graphs with a high gate error rate (\((\epsilon_{\text{gate}} > 10^{-2})\)), Dy-Part's dynamic strategy yields a mean approximation ratio more than double that of the static baseline. These results show that a dynamic, noise-aware scheduling approach can provide a robust method for configuring hybrid workflows, offering a practical pathway to maximizing the performance of near-term quantum computers.

DOI
10.21203/rs.3.rs-8041248/v1

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Quantum algorithms offer an exponential speedup over classical algorithms for a range of computational problems. The fundamental mechanisms underlying quantum computation required the development and construction of quantum computers. These devices are referred to as NISQ (Noisy Intermediate-Scale Quantum) devices. Not only are NISQ devices extremely limited in their qubit count but they also suffer from noise during computation and this problem only gets worse as the size of the circuit increases which limits the practical use of quantum computers for modern day applications. This paper will focus on utilizing quantum circuit partitioning to overcome the inherent issues of NISQ devices. Partitioning a quantum circuit into smaller subcircuits has allowed for the execution of quantum circuits that are too large to fit on one quantum device. There have been many previous approaches to quantum circuit partitioning and each of these approaches differ in how they work with some focusing on hardware-aware partitioning, optimal graph-based partitioning, multi-processor architectures and many more. These approaches achieve success in their objective but they often fail to scale well which impacts cost and noise. The ultimate goal of this paper is to mitigate these issues by minimizing 3 important metrics; noise, time and cost. To achieve this we use dynamic partitioning for practical circuit cutting and we take advantage of the benefits of hybrid execution where classical computation will be used alongside quantum hardware. This approach has proved to be beneficial with respect to noise with classical execution enabling a 42.30% reduction in noise and a 40% reduction in the number of qubits required in cases where a mixture of classical and quantum computation were required.
The development of quantum computing relies heavily on the effective execution of quantum algorithms. However, current Noisy Intermediate-Scale Quantum (NISQ) computers are plagued by limitations such as short coherence times and significant error rates, posing substantial challenges to scheduling and execution. This paper introduces a novel dynamic resource-aware scheduling algorithm designed to mitigate these challenges. The core of the algorithm lies in its ability to adapt to the fluctuating noise levels and coherence times of the quantum hardware, intelligently prioritizing tasks based on their estimated computational cost and error probability. We demonstrate that this adaptive approach significantly improves the potential for successful quantum algorithm execution on NISQ devices, offering a pathway towards more efficient and reliable quantum computation. The algorithm's effectiveness is evaluated through theoretical analysis and conceptual modeling, focusing on the optimization of resource allocation and task prioritization within the constraints of NISQ technology. This research contributes to the growing field of quantum resource management and scheduling, providing a framework for tackling the complexities inherent in operating quantum computers.
The rapid advancement of Noisy Intermediate-Scale Quantum (NISQ) devices has driven the need for efficient design automation tools that can translate complex engineering models into executable quantum circuits. This study explores the development of automated quantum circuit design frameworks focused on gate optimization and noise-aware compilation, enabling practical quantum computation for engineering applications such as optimization, simulation, and control systems. Traditional circuit design methods often fail to account for hardware constraints, decoherence, and gate infidelity, which significantly affect computational accuracy on NISQ devices. To address these challenges, this research introduces a hybrid automation approach combining quantum transpilation, variational circuit pruning, and error-aware synthesis algorithms to enhance circuit reliability and scalability. The framework integrates quantum-classical co-design principles to optimize both logical and physical circuit layers, leveraging hardware-specific calibration data to adapt compilation strategies dynamically. Through simulations and benchmark analysis, the proposed system demonstrates significant improvements in quantum resource utilization, error mitigation, and gate depth reduction compared to conventional compilation methods. This work underscores the growing importance of design automation in quantum engineering, where algorithmic efficiency must be harmonized with physical constraints to realize functional and scalable quantum workflows. By establishing a methodology for gate-level optimization and noise-adaptive design, the study contributes to bridging the gap between theoretical quantum algorithms and real-world engineering implementations within the NISQ computing era.
The hardware noise and drift, combined with the fragility of stationary hybrid algorithms like the variational quantum eigensolver (VQE), limit the usefulness of noise quantum advantage accessed through Noisy intermediate-scale quantum (NISQ) processors, but allow near-term quantum advantage. In this paper, a Hybrid Quantum-Classical Adaptive Intelligence Framework is suggested, which bridges the gap between quantum hardware, a parameterized quantum circuit, explicit error-mitigation routines, and a controller trained through learning. The quantum subsystem prepares variational states and calculates the expectation values of the problem Hamiltonian, and an AI/ML model approximates an online noise probability signal and moves an optimizer based on reinforcement learning (RL). The RL policy chooses the parameter updates and enables mitigation measures depending on noise-sensitive state functions, such as energy, gradient norm, and estimated noise level. It was demonstrated experimentally using a simulated NISQ-style molecular Hamiltonian that the adaptive controller can reduce the error in the absolute ground state of the system by about 52.8 percent versus a standard static VQE baseline using the same ansatz. The integrated classifier that drives the process of mitigation is accurate with 97 percent accuracy, 100 percent precision, 92 percent recall, 95.9 F1-score, and 99.5 AUC, which shows reliable decision-making with minimal false alarms. All these findings indicate that the close-knit quantum-classical adaptivity can significantly contribute to the robustness and accuracy of VQE-like workloads on NISQ and serve as a scalable template to design noise-aware hybrid algorithms in the future.
In this paper, we describe the simulation of Ising minimization on a classical machine by executing variational quantum algorithms on our density-matrix simulator. We outline the Ising formulation of the Graph Partitioning problem and the Hamiltonian Cycle problem, and solve the Max-Cut variant of graph partitioning for a weighted square graph Sq2using the Quantum Approximate Optimization Algorithm. We finally study the effect of errors present in Noisy Intermediate-Scale Quantum processors on the obtained solutions. This paper illustrates the approach to approximately solving hard combinatorial optimization problems using a hybrid quantum-classical scheme and describes the issues in hardware implementation of such schemes. The simulations of NISQ noise models will be useful in understanding the performance and capabilities of such approaches.
Abstract Noisy Intermediate-Scale Quantum (NISQ) devices are near-term quantum computing devices that inherently suffer from imperfect gate implementation, decoherence, and measurement noise. The main type of variational quantum algorithms(VQAs) is based on Parameterized Quantum Circuits (PQCs), which, however, face practical challenges due to optimization instability of optimization caused by noise and reduced performance. In this paper, we suggest a comprehensive noise-resilient optimization framework for PQCs, which explicitly incorporates noise-resilient circuit architecture design, incorporating noise-aware training strategies into the variational optimization loop. The framework uses circuit structures that are efficient in depth and are hardware compatible in addition to adaptive optimizer choice, stochastic optimization, and averaging as a part of repeated measurement to reduce the impacts of realistic noise. Large-scale experiments are performed with different amounts of qubits, depths of the circuit and noise strengths with realistic noise models. The findings indicate that the convergence rate is faster, the stability is increased, the variability of run-to-run variability is minimized, and other robustness is better than the noise-unaware optimization techniques. Moreover, systematic studies determine optimal operating regions that optimally trade-off expressibility and noise tolerance to offer useful design advice on trusted implementation of VQAs with near-term quantum hardware.