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arXiv:2511.18079

Noise-Adaptive Quantum Circuit Mapping for Multi-Chip NISQ Systems via Deep Reinforcement Learning

Atiye Zeynali, Zahra Bakhshi

quant-phphysics.comp-phSubmitted Nov 22, 2025• Announced Nov 25, 2025• 0 citations
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Abstract

The transition from monolithic to distributed multi-chip quantum architectures has fundamentally altered the circuit compilation landscape, introducing challenges in managing temporal noise variations and minimizing expensive inter-chip operations. We present DeepQMap, a deep reinforcement learning framework that integrates a bidirectional Long Short-Term Memory based Dynamic Noise Adaptation (DNA) network with multi-head attention mechanisms and Rainbow DQN architecture. Unlike conventional static optimization approaches such as QUBO formulations, our method continuously adapts to hardware dynamics through learned temporal representations of quantum system behavior. Comprehensive evaluation across 270 benchmark circuits spanning Quantum Fourier Transform, Grover's algorithm, and Variational Quantum Eigensolver demonstrates that DeepQMap achieves mean circuit fidelity of $0.920 \pm 0.023$, representing a statistically significant 49.3\% improvement over state-of-the-art QUBO methods ($0.618 \pm 0.031$, $t_{98} = 4.87$, $p = 0.0023$, Cohen's $d = 2.34$). Inter-chip communication overhead reduces by 79.8\%, decreasing from 2.34 operations per circuit to 0.47. The DNA network maintains noise prediction accuracy with coefficient of determination $R^2 = 0.912$ and mean absolute error of 0.87\%, enabling proactive compensation for hardware fluctuations. Scalability analysis confirms sustained performance across 20-100 qubit systems, with fidelity remaining above 0.87 even at maximum scale where competing methods degrade below 0.60. Training convergence occurs 8.2$\times$ faster than baseline approaches, completing in 45 minutes versus 370 minutes for QUBO optimization. Very large effect sizes validate practical significance for near-term noisy intermediate-scale quantum computing applications.

Comments
14 pages, 8 figures, 2 tables

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The transition from monolithic to distributed multi-chip quantum architectures has fundamentally altered the circuit compilation landscape, introducing challenges in managing temporal noise variations and minimizing expensive inter-chip operations. We present DeepQMap, a deep reinforcement learning framework that integrates a bidirectional Long Short-Term Memory based Dynamic Noise Adaptation (DNA) network with multi-head attention mechanisms and Rainbow DQN architecture. Unlike conventional static optimization approaches such as QUBO formulations, our method continuously adapts to hardware dynamics through learned temporal representations of quantum system behavior. Comprehensive evaluation across 270 benchmark circuits spanning Quantum Fourier Transform, Grover's algorithm, and Variational Quantum Eigensolver demonstrates that DeepQMap achieves mean circuit fidelity of 0.920±0.0230.920 \pm 0.0230.920±0.023, representing a statistically significant 49.3% improvement over state-of-the-art QUBO methods (0.618±0.0310.618 \pm 0.0310.618±0.031, t98=4.87t_{98} = 4.87t98​=4.87, p=0.0023p = 0.0023p=0.0023, Cohen's d=2.34d = 2.34d=2.34). Inter-chip communication overhead reduces by 79.8%, decreasing from 2.34 operations per circuit to 0.47. The DNA network maintains noise prediction accuracy with coefficient of determination R2=0.912R^2 = 0.912R2=0.912 and mean absolute error of 0.87%, enabling proactive compensation for hardware fluctuations. Scalability analysis confirms sustained performance across 20-100 qubit systems, with fidelity remaining above 0.87 even at maximum scale where competing methods degrade below 0.60. Training convergence occurs 8.2×\times× faster than baseline approaches, completing in 45 minutes versus 370 minutes for QUBO optimization. Very large effect sizes validate practical significance for near-term noisy intermediate-scale quantum computing applications.
Abstract Modular quantum architectures have emerged as a promising approach for scaling quantum computing systems by connecting multiple Quantum Processing Units (QPUs). However, this approach introduces significant challenges due to costly inter-core operations between chips and quantum state transfers, which contribute to noise and quantum decoherence. This paper presents QARMA, a novel Qubit mapping using Attention-based deep Reinforcement learning (DRL) for Modular quantum Architectures, along with its extension QARMA-R that incorporates dynamic qubit reuse capabilities. Our approach combines an attention-based mechanism with Graph Neural Networks (GNN) to learn optimal qubit allocation, routing, and reuse strategies that minimize inter-core communications.Our experimental results show significant improvements over state-of-the-art approaches. Compared to highly-optimized Qiskit with modular architecture configuration, QARMA-R reduces inter-core communications by up to 100% (on average 86%), while QARMA maintains 15-40% improvement for larger circuits without reuse. Against traditional modular qubit mapping, our approach achieves 97-100% reduction in inter-core operation.The proposed methods advance quantum circuit compilation techniques and enable the execution of more extensive quantum algorithms on resource-constrained modular quantum systems, contributing to the growing body of research on scalable quantum computing architectures.
Modular quantum architectures have emerged as a promising approach for scaling quantum computing systems by connecting multiple Quantum Processing Units (QPUs). However, this approach introduces significant challenges due to costly inter-core operations between chips and quantum state transfers, which contribute to noise and quantum decoherence. This paper presents QARMA, a novel Qubit mapping using Attention-based deep Reinforcement learning (DRL) for Modular quantum Architectures, along with its extension QARMA-R that incorporates dynamic qubit reuse capabilities. Our approach combines an attention-based mechanism with Graph Neural Networks (GNN) to learn optimal qubit allocation, routing, and reuse strategies that minimize inter-core communications. We introduce two key innovations: (1) a transformer-based encoder that captures both the global circuit structure and local qubit interactions and (2) a dynamic qubit reuse compilation mechanism that leverages mid-circuit measurement and reset operations to reduce inter-operation and qubit requirements. Our experimental results show significant improvements over state-of-the-art approaches. Compared to highly-optimized Qiskit with modular architecture configuration, QARMA-R reduces inter-core communications by up to 100% (on average 86%), while QARMA maintains 15-40% improvement for larger circuits without reuse. Against traditional modular qubit mapping, our approach achieves 97-100% reduction in inter-core operation. The proposed methods advance quantum circuit compilation techniques and enable the execution of more extensive quantum algorithms on resource-constrained modular quantum systems, contributing to the growing body of research on scalable quantum computing architectures.
Modular, distributed and multi-core architectures are currently considered a promising approach for scalability of quantum computing systems. The integration of multiple Quantum Processing Units necessitates classical and quantum-coherent communication, introducing challenges related to noise and quantum decoherence in quantum state transfers between cores. Optimizing communication becomes imperative, and the compilation and mapping of quantum circuits onto physical qubits must minimize state transfers while adhering to architectural constraints. The compilation process, inherently an NP-hard problem, demands extensive search times even with a small number of qubits to be solved to optimality. To address this challenge efficiently, we advocate for the utilization of heuristic mappers that can rapidly generate solutions. In this work, we propose a novel approach employing Deep Reinforcement Learning (DRL) methods to learn these heuristics for a specific multi-core architecture. Our DRL agent incorporates a Transformer encoder and Graph Neural Networks. It encodes quantum circuits using self-attention mechanisms and produce outputs through an attention-based pointer mechanism that directly signifies the probability of matching logical qubits with physical cores. This enables the selection of optimal cores for logical qubits efficiently. Experimental evaluations show that the proposed method can outperform baseline approaches in terms of reducing inter-core communications and minimizing online time-to-solution. This research contributes to the advancement of scalable quantum computing systems by introducing a novel learning-based heuristic approach for efficient quantum circuit compilation and mapping.
Although quantum computers will not necessarily transform ML in general, they hold immense potential for specific uses, such as dealing with high-dimensional data and complex correlations that cannot be effectively modeled using classical systems. It is, however, difficult to design efficient quantum circuits, especially with the existing Noisy Intermediate-Scale Quantum (NISQ) hardware that has constraints including decoherence, a limited number of qubits, and gate fidelity limitations. The authors propose an Adaptive Quantum Circuit Architecture Search (AQCAS) framework whose structure and parameters are optimized dynamically to improve the performance of the task-specific ML. The framework integrates quantum circuit encoding, a hybrid search strategy that is based on reinforcement learning and evolutionary algorithms, and a task-specific adaptivity mechanism. Benchmark datasets were used for experimental evaluations of both simulation using Qiskit Aer and classical optimization using the Python frameworks TensorFlow and PennyLane. Fixed and standard variational quantum circuits were compared with performance measurements like the classification accuracy, circuit depth, convergence speed, and computational efficiency. The results show that the accuracy of AQCAS can be raised to 94.1%, and the circuit depth is lowered at the same time. It converged rapidly and is computationally efficient. Ablation studies show that both hybrid search and adaptivity mechanisms help to enhance generalization and stability under NISQ constraints. The results demonstrate the potential of AQCAS as a practical approach to using adaptive quantum circuits in specialized ML applications, and lay the groundwork for future development of scaling to larger devices, implementing noise-aware optimization, and integrating with hybrid quantum-classical ML pipelines.
Abstract: Efficient quantum circuit compilation is crucial for maximizing the utility of Noisy Intermediate- Scale Quantum (NISQ) devices. This paper presents a novel adaptive quantum circuit compilation technique using dynamic programming, which significantly reduces circuit depth while maintaining high fidelity. Our approach, termed AdaQC (Adaptive Quantum Compiler), dynamically adjusts the compilation strategy based on the specific quantum hardware constraints and noise characteristics. We demonstrate up to 30% reduction in circuit depth compared to state-of-the-art compilers, with less than 1% loss in fidelity for benchmark circuits on superconducting qubit architectures. Furthermore, we provide a comprehensive analysis of AdaQC's performance across various quantum algorithms and hardware configurations, showcasing its adaptability and efficiency in real-world scenarios
Noisy Intermediate-Scale Quantum (NISQ) devices are promising platforms for practical quantum computing, but their performance is significantly affected by limited qubit availability, gate errors, decoherence, circuit depth, and hardware-dependent noise. Conventional quantum circuits are generally designed and optimized before execution and may not respond effectively to changing hardware conditions during computation. This research proposes a Self-Adaptive Quantum Circuit framework that dynamically modifies quantum circuits according to the current computational resources and noise characteristics of NISQ devices. The proposed framework monitors parameters such as qubit fidelity, gate error rates, circuit depth, execution time, and resource availability, and automatically selects suitable circuit configurations to improve computational performance. Adaptive mechanisms can be employed to reduce unnecessary gates, optimize qubit utilization, and mitigate the impact of hardware noise while maintaining the required computational accuracy. The framework will be evaluated using simulation platforms and available NISQ quantum hardware, comparing adaptive circuits with conventional static circuits using metrics such as accuracy, fidelity, circuit depth, gate count, execution time, and error rate. The expected outcome is a resource-efficient and noise-aware quantum circuit optimization approach capable of improving the reliability and performance of quantum applications on near-term quantum hardware. The proposed research aims to contribute toward more practical, flexible, and hardware-aware quantum computing systems for NISQ-era applications. Keywords: NISQ Computing, Self-Adaptive Quantum Circuits, Quantum Circuit Optimization, Quantum Error Mitigation, Quantum Noise, Quantum Computing.