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Dynamic Topology-Dependent Quantum Computing Compilation

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

Quantum computing relies heavily on efficient compilation techniques to translate high-level quantum algorithms into low-level gate sequences suitable for specific quantum hardware. Traditional quantum compilation methods often rely on fixed, pre-defined topologies, which can be inefficient and inflexible when dealing with the inherent physical constraints and algorithmic variations present in real quantum devices. This work introduces a novel approach to quantum compilation that dynamically adapts the topology of quantum circuits based on reinforcement learning. We propose a "topology optimizer" that learns to optimize circuit topologies in real-time, considering factors such as quantum decoherence, quantum error correction, and resource limitations. The core mechanism involves iteratively adjusting the circuit's topology to minimize resource usage and error propagation. This dynamic approach promises to significantly improve the performance and practicality of quantum computing by aligning compilation strategies with the nuances of quantum hardware. The key equation representing this process is: $T^* = argmin_T \left[ \sum_{i=1}^{K} \rho_i(T) \cdot C_i \right]$, where $T^*$ is the optimal topology, $T$ represents a candidate topology, $\rho_i(T)$ is the fidelity of the quantum circuit with topology $T$ for the $i$-th qubit, and $C_i$ is the cost associated with that qubit. Furthermore, the error rate $ε(T)$ is defined as $ε(T) = \sum_{i=1}^{K} \delta_i(T)$, where $\delta_i(T)$ represents the error introduced by the topology $T$ for the $i$-th qubit. This framework offers a fundamentally new paradigm for quantum compilation.

DOI
10.5281/zenodo.22194712

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