State Similarity in Modular Superconducting Quantum Processors with Classical Communications

TL;DR

This paper introduces a fidelity estimation algorithm for modular quantum processors that reduces sample complexity, enabling scalable verification and privacy-preserving quantum machine learning across distributed superconducting quantum modules.

Contribution

The authors develop a cross-platform fidelity estimation method tailored for modular architectures, demonstrating its effectiveness on superconducting quantum processors and in quantum machine learning tasks.

Findings

Successfully verified GHZ states on modular processors

Reduced sample complexity compared to previous methods

Enabled privacy-preserving quantum kernel learning

Abstract

As quantum devices continue to scale, distributed quantum computing emerges as a promising strategy for executing large-scale tasks across modular quantum processors. A central challenge in this paradigm is verifying the correctness of computational outcomes when subcircuits are executed independently following circuit cutting. Here we propose a cross-platform fidelity estimation algorithm tailored for modular architectures. Our method achieves substantial reductions in sample complexity compared to previous approaches designed for single-processor systems. We experimentally implement the protocol on modular superconducting quantum processors with up to 6 qubits to verify the similarity of two 11-qubit GHZ states. Beyond verification, we show that our algorithm enables a federated quantum kernel method that preserves data privacy. As a proof of concept, we apply it to a 5-qubit quantum phase learning task using six 3-qubit modules, successfully extracting phase information with just eight training samples. These results establish a practical path for scalable verification and trustworthy quantum machine learning of modular quantum processors.