Architecture-Aware Reinforcement Learning for Communication-Efficient Distributed Quantum Circuit Compilation

• URL: http://arxiv.org/abs/2608.06892v1
• Date: Fri, 07 Aug 2026 07:25:18 GMT
• Title: Architecture-Aware Reinforcement Learning for Communication-Efficient Distributed Quantum Circuit Compilation
• Abstract summary: Distributed quantum computing provides a scalable route for executing quantum circuits beyond the capacity limits of a single quantum processing unit.<n>This paper presents an architecture-aware reinforcement-learning framework that formulates distributed quantum compilation as a constrained Markov Decision Process.
• Score: 16.380589534840862
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distributed quantum computing provides a scalable route for executing quantum circuits beyond the capacity limits of a single quantum processing unit (QPU), but it introduces a communication-aware compilation problem involving strict hardware constraints and circuit dependencies. This paper presents an architecture-aware reinforcement-learning framework that formulates distributed quantum compilation as a constrained Markov Decision Process (MDP). The compiler-level communication actions dynamically update logical-qubit placement and enable subsequent gate execution. A heterogeneous graph model represents interactions among hardware, logical qubits, and circuit operations, while a policy trained via Proximal Policy Optimization optimizes EPR-pair consumption and communication makespan. Evaluation across benchmark circuits shows that our policy matches state-of-the-art heuristics on structured workloads, with lookahead reward shaping yielding modest improvements on unstructured circuits. These results demonstrate that reinforcement learning is a flexible alternative to manual heuristics, though scalability remains a key bottleneck for practical use.

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