Diagnosing crosstalk in large-scale QPUs using zero-entropy classical shadows

TL;DR

This paper introduces Zero-Entropy Classical Shadows (ZECS), a scalable, measurement-efficient method for diagnosing crosstalk in large-scale quantum processing units, improving qubit subset selection for quantum algorithms.

Contribution

The paper presents ZECS, a novel diagnostic tool that uses classical shadows of quantum states to identify crosstalk, enabling better qubit subset selection in large QPUs.

Findings

ZECS effectively characterizes crosstalk in large QPUs.

Applying ZECS improves solution quality by 10%.

ZECS increases algorithmic coherence by 33%.

Abstract

As quantum processing units (QPUs) scale toward hundreds of qubits, diagnosing noise-induced correlations (crosstalk) becomes critical for reliable quantum computation. In this work, we introduce Zero-Entropy Classical Shadows (ZECS), a diagnostic tool that uses information of a rank-one quantum state tomography (QST) reconstruction from classical shadow (CS) information to make a crosstalk diagnosis. We use ZECS on trapped ion and superconductive QPUs, including ionq_forte (36 qubits), ibm_brisbane (127 qubits), and ibm_fez (156 qubits), using from 1,000 to 6,000 samples. With these samples, we use the ZECS to characterize crosstalk among disjoint qubit subsets across the full hardware. This information is then used to select low-crosstalk qubit subsets on ibm_fez for executing the Quantum Approximate Optimization Algorithm (QAOA) on a 20-qubit problem. Compared to the best qubit selection via Qiskit transpilation, our method improves solution quality by 10% and increases algorithmic coherence by 33%. ZECS offers a scalable and measurement-efficient approach to diagnosing crosstalk in large-scale QPUs.