Modular, distributed, and multi-core architectures are considered a promising solution for scaling quantum computing systems. Optimising communication is crucial to preserve quantum coherence. The compilation and mapping of quantum circuits should minimise state transfers while adhering to architec-tural constraints. To address this problem efficiently, we propose a novel approach using Reinforcement Learning (RL) to learn heuristics for a specific multi-core architecture. Our RL agent uses a Transformer encoder and Graph Neural Networks, encoding quantum circuits with self-attention and producing outputs via an attention-based pointer mechanism to match logical qubits with physical cores efficiently. Experimental results show our method outperform the baseline reducing by 28% inter-core communications for random circuits while minimising time-to-solution.
Modular, distributed, and multi-core architectures are considered a promising solution for scaling quantum computing systems. Optimising communication is crucial to preserve quantum coherence. The compilation and mapping of quantum circuits should minimise state transfers while adhering to architec-tural constraints. To address this problem efficiently, we propose a novel approach using Reinforcement Learning (RL) to learn heuristics for a specific multi-core architecture. Our RL agent uses a Transformer encoder and Graph Neural Networks, encoding quantum circuits with self-attention and producing outputs via an attention-based pointer mechanism to match logical qubits with physical cores efficiently. Experimental results show our method outperform the baseline reducing by 28% inter-core communications for random circuits while minimising time-to-solution.
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.
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.
Quantum circuit optimization is a foundational challenge in realizing practical quantum computation, requiring the reduction of gate counts and circuit depth while preserving logical equivalence across noisy intermediate-scale quantum (NISQ) devices. Conventional rule-based and heuristic compilation approaches demonstrate diminishing effectiveness as circuit complexity grows, motivating the search for adaptive, data-driven alternatives. This paper reformulates quantum circuit optimization as a Markov decision process (MDP), enabling deep reinforcement learning (RL) agents to autonomously discover efficient transformation strategies. A graph neural network (GNN)-based actor-critic architecture is developed to encode circuit topology and guide transformation selection. Drawing on empirical evidence from phase-structured quantum control tasks, neural feedback architectures, and convergence behavior in active learning agents, we demonstrate that the proposed framework achieves an average two-qubit gate count reduction of 31.4% and a circuit depth reduction of 27.8% across standard benchmark circuits, outperforming established compilation baselines. This work provides a principled theoretical foundation for the RL-as-optimizer paradigm and contributes an extensible experimental platform suited to NISQ-era constraints.
This paper presents a novel approach to quantum circuit compilation leveraging reinforcement learning (RL). The core idea is to train an RL agent to dynamically optimize quantum circuit structures, aiming to minimize gate count and enhance overall circuit performance. Traditional quantum circuit compilation methods often rely on static heuristics and pre-defined optimization strategies, which may not be optimal for all quantum algorithms or hardware platforms. Our approach addresses this limitation by allowing the RL agent to learn a policy that adapts to the specific characteristics of the algorithm and the target quantum hardware. The agent learns by interacting with a simulated quantum circuit environment, receiving rewards based on circuit fidelity, depth, and gate count. The results demonstrate the potential of RL to significantly improve the efficiency and robustness of quantum computations. The key components of the system are: a high-level quantum algorithm representation, an RL agent, a quantum circuit simulator, and a reward function. The agent's policy is represented as a neural network, enabling it to generalize across different algorithms and hardware. We demonstrate the effectiveness of this approach through simulations, showcasing its ability to outperform conventional compilation methods in terms of reducing circuit depth and gate count while maintaining high fidelity.
Quantumcomputers exhibit significant potential for addressing problems that are intractable by classical computing. Nevertheless, the scalability of quantum architectures remains a pivotal challenge within the field. Current efforts for increasing the number of qubits. However, using these architectures implies dealing with new hardware and algorithmic challenges. The primary focus of this thesis is the partitioning of quantum circuits across multiple cores; a task classified as an NP-hard problem. While classical algorithms often struggle with execution time and scalability, Machine Learning (ML) has emerged as a promising, faster yet powerful alternative. This thesis presents a novel approach for circuit partitioning in multi-core quantum architectures using Deep Reinforcement Learning (DRL) with graph neural networks (GNN). This methodology provides a flexible and efficient framework, enabling the partitioning of circuits with varying numbers of qubits while maintaining high performance and scalability.