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

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.

17 pages, 13 figures

References in corpus (31)

• Charge insensitive qubit design derived from the Cooper pair box
• Entanglement of a Pair of Quantum Bits
• Characterizing Quantum Supremacy in Near-Term Devices
• Predicting Many Properties of a Quantum System from Very Few Measurements
• Quantum state tomography via compressed sensing
• Logical quantum processor based on reconfigurable atom arrays
• Quantum optimization using variational algorithms on near-term quantum devices
• Scalable Noise Estimation with Random Unitary Operators
• Procedure for systematically tuning up crosstalk in the cross resonance gate
• Characterization of addressability by simultaneous randomized benchmarking
• Rapid high-fidelity multiplexed readout of superconducting qubits
• Provably efficient machine learning for quantum many-body problems
• Detecting crosstalk errors in quantum information processors
• Sample-optimal tomography of quantum states
• Learning Quantum Systems
• Classical Shadows With Noise
• Frequency Division Multiplexing Readout and Simultaneous Manipulation of an Array of Flux Qubits
• Benchmarking a trapped-ion quantum computer with 30 qubits
• A trapped-ion based quantum byte with $10^{-5}$ next-neighbour cross-talk
• Probing context-dependent errors in quantum processors
• Quantum tomography as a tool for the characterization of optical devices
• Suppressing quantum circuit errors due to system variability
• Classical Shadows for Quantum Process Tomography on Near-term Quantum Computers
• Informational completeness in bounded-rank quantum-state tomography
• The power of being positive: Robust state estimation made possible by quantum mechanics
• Strictly-complete measurements for bounded-rank quantum-state tomography
• Quantum Error Mitigated Classical Shadows
• GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm
• Towards a Linear-Ramp QAOA protocol: Evidence of a scaling advantage in solving some combinatorial optimization problems
• Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators
• Method for Generating Randomly Perturbed Density Operators Subject to Different Sets of Constraints