Hardware-aware Compilation for Chip-to-Chip Coupler-Connected Modular Quantum Systems

TL;DR

This paper presents CCMap, a system-level compiler framework for modular quantum systems that improves circuit fidelity and reduces compilation costs by leveraging calibration data and noise-aware metrics.

Contribution

CCMap introduces a novel co-design framework that integrates system-level considerations into quantum circuit compilation for chip-to-chip coupler-connected architectures.

Findings

Improves circuit fidelity by up to 21.9%.

Reduces compilation cost by up to 58.6%.

Enhances scalability of quantum processors.

Abstract

As quantum processors scale, monolithic architectures face growing challenges due to limited qubit density, heterogeneous error profiles, and restricted connectivity. Modular quantum systems, enabled by chip-to-chip coupler-connected modular architectures, provide a scalable alternative. However, existing quantum compilers fail to accommodate this new architecture. We introduce CCMap, a circuit-compiler co-design framework that enhances existing quantum compilers with system-level coordination across modular chips. It leverages calibration data and introduces a coupler-aligned and noise-aware cost metric to evaluate circuit compilation. CCMap integrates with existing compilers by partitioning circuits into subcircuits compiled on individual chips, followed by a global mapping step to minimize the total cost. We evaluated CCMap on IBM-Q noisy emulators using real hardware calibrations across various coupler-connected topologies. Results show that CCMap improves circuit fidelity by up to 21.9%, representing a 30% increase, and reduces compilation cost by up to 58.6% over state-of-the-art baselines. These findings highlight CCMap's potential to enable scalable, high-fidelity execution in coupler-connected modular quantum systems.