Adaptive Quantum Circuit Compilation via Tensor Networks

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum circuit compilation is a critical step in realizing quantum computation, aiming to translate high-level quantum algorithms into a sequence of gates executable on a specific quantum hardware. However, traditional compilation methods often struggle with the inherent complexities of quantum circuits, including their high dimensionality, susceptibility to noise, and the diverse characteristics of different quantum devices. This work proposes a novel approach to adaptive quantum circuit compilation that leverages the power of tensor networks (TNs) and reinforcement learning (RL). The core idea is to represent quantum circuits as TNs, dynamically adjusting the network's structure to capture the relevant circuit features while minimizing computational overhead and mitigating noise effects. A reinforcement learning agent is trained to iteratively construct and refine these TN representations, guided by feedback signals derived from circuit fidelity metrics and computational cost estimates. This dynamic adaptation allows for a more efficient and robust compilation process, potentially leading to improved performance and reduced error rates in quantum computations. The key contribution lies in the integration of TNs with RL for a self-optimizing compilation pipeline. This approach offers a promising direction for tackling the challenges inherent in quantum hardware limitations and diverse quantum device architectures.

DOI: 10.5281/zenodo.22315270

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