Tensor network noise characterization for near-term quantum computers

Abstract

Characterization of noise in current near-term quantum devices is of paramount importance to fully use their computational power. However, direct quantum process tomography becomes unfeasible for systems composed of tens of qubits. A promising alternative method based on tensor networks was recently proposed [Nat. Commun. 14, 2858 (2023)]. In this paper, we adapt it for the characterization of noise channels on near-term quantum computers and investigate its performance thoroughly. In particular, we show how experimentally feasible tomographic samples are sufficient to accurately characterize realistic correlated noise models affecting individual layers of quantum circuits, and study its performance on systems composed of up to 20 qubits. Furthermore, we combine this noise characterization method with a recently proposed noise-aware tensor network error mitigation protocol for correcting outcomes in noisy circuits, resulting accurate estimations even on deep circuit instances. This positions the tensor-network-based noise characterization protocol as a valuable tool for practical error characterization and mitigation in the near-term quantum computing era.

Updated version matching publication (minor modifications, added section on SPAM errors)

References in corpus (22)

• The density-matrix renormalization group in the age of matrix product states
• Predicting Many Properties of a Quantum System from Very Few Measurements
• Matrix Product Density Operators: Simulation of finite-T and dissipative systems
• Logical quantum processor based on reconfigurable atom arrays
• Efficient quantum state tomography
• Quantum Error Mitigation
• Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity, and Efficient Estimators
• A Race Track Trapped-Ion Quantum Processor
• Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors
• Gate Set Tomography
• Non-Markovian Quantum Process Tomography
• Compressed sensing quantum process tomography for superconducting quantum gates
• Evidence of Kardar-Parisi-Zhang scaling on a digital quantum simulator
• Shadow process tomography of quantum channels
• Quantum error mitigation via matrix product operators
• Classical Shadows for Quantum Process Tomography on Near-term Quantum Computers
• The learnability of Pauli noise
• Projected Least-Squares Quantum Process Tomography
• Gradient-descent quantum process tomography by learning Kraus operators
• Estimating gate-set properties from random sequences
• Compressive gate set tomography
• Self-consistent quantum measurement tomography based on semidefinite programming

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• A Quantum-Inspired Algorithm for Wave Simulation Using Tensor Networks
• Gauge-Fixing Quantum Density Operators At Scale
• Sublinear Classical-to-Quantum Data Encoding using $n$-Toffoli Gates
• The perfect entangler spectrum as a tool to analyze crosstalk
• Learning mixed quantum states in large-scale experiments