Resource-efficient quantum correlation measurements via multicopy neural network methods

TL;DR

This paper introduces a neural network-based method that reduces resource requirements for measuring quantum correlations, demonstrating improved efficiency and noise robustness on real quantum hardware compared to traditional tomography.

Contribution

It presents a novel multicopy measurement approach combined with neural networks that significantly decreases measurement resources and enhances noise resilience in quantum correlation assessments.

Findings

Achieved 67% reduction in measurement resources compared to QST.

Successfully measured quantum correlations on IBMQ hardware.

Enhanced noise robustness using trained neural networks.

Abstract

Measuring complex properties in quantum systems, such as measures of quantum entanglement and Bell nonlocality, is inherently challenging. Traditional methods, like quantum state tomography (QST), necessitate a full reconstruction of the density matrix for a given system and demand resources that scale exponentially with system size. We propose an alternative strategy that reduces the required information by combining multicopy measurements with artificial neural networks (ANNs), resulting in a 67\% reduction in measurement requirements compared to QST. We have successfully measured two-qubit quantum correlations of Bell states subjected to a depolarizing channel (resulting in Werner states) and an amplitude damping channel (leading to Horodecki states) using the multicopy approach on real quantum hardware. Our experiments, conducted with transmon qubits on IBMQ processors, quantified the violation of Bell's inequality and the negativity of two-qubit entangled states. We compared these results with those obtained from the standard QST approach and applied a maximum likelihood method to mitigate noise. We trained ANNs to estimate entanglement and nonlocality measures using optimized sets of projections identified through Shapley's (SHAP) analysis for the Werner and Horodecki states. The ANN output, based on this reduced set of projections, aligns well with expected values and enhances noise robustness. This approach simplifies and improves the error-robustness of multicopy measurements, eliminating the need for complex nonlinear equation analysis. It represents a significant advancement in AI-assisted quantum measurements, making practical implementation on current quantum hardware more feasible.