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| Hauptseite > Publikationsdatenbank > Diagnosing crosstalk in large-scale QPUs using zero-entropy classical shadows |
| Hauptseite > Publikationsdatenbank > Diagnosing crosstalk in large-scale QPUs using zero-entropy classical shadows |
| Journal Article | FZJ-2025-04791 |
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2026
IOP Publishing
Philadelphia, PA
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Please use a persistent id in citations: doi:10.1088/2058-9565/ae1e99 doi:10.34734/FZJ-2025-04791
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 reconstruction from classical shadow 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 1000 to 6000 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 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.
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