TY  - JOUR
AU  - Montañez-Barrera, J. A.
AU  - Beretta, G. P.
AU  - Michielsen, Kristel
AU  - von Spakovsky, Michael R
TI  - Diagnosing crosstalk in large-scale QPUs using zero-entropy classical shadows
JO  - Quantum science and technology
VL  - 11
IS  - 1
SN  - 2058-9565
CY  - Philadelphia, PA
PB  - IOP Publishing
M1  - FZJ-2025-04791
SP  - 17
PY  - 2026
AB  - 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.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1088/2058-9565/ae1e99
UR  - https://juser.fz-juelich.de/record/1048663
ER  -