Fast Estimation of Sparse Quantum Noise

Abstract

As quantum computers approach the fault tolerance threshold, diagnosing and characterizing the noise on large scale quantum devices is increasingly important. One of the most important classes of noise channels is the class of Pauli channels, for reasons of both theoretical tractability and experimental relevance. Here we present a practical algorithm for estimating the $s$ nonzero Pauli error rates in an $s$-sparse, $n$-qubit Pauli noise channel, or more generally the $s$ largest Pauli error rates. The algorithm comes with rigorous recovery guarantees and uses only $O(n^2)$ measurements, $O(s{n}^2)$ classical processing time, and Clifford quantum circuits. We experimentally validate a heuristic version of the algorithm that uses simplified Clifford circuits on data from an IBM 14-qubit superconducting device and our open source implementation. These data show that accurate and precise estimation of the probability of arbitrary-weight Pauli errors is possible even when the signal is two orders of magnitude below the measurement noise floor.

25 pages, 3 figures. Open source implementation available at https://github.com/rharper2/sparsePauliReconstruction. Minor edits and format changes

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Cited by in corpus (33)

• Robust shadow estimation
• Exponential Error Suppression for Near-Term Quantum Devices
• Out-of-distribution generalization for learning quantum dynamics
• Quantum advantages for Pauli channel estimation
• The learnability of Pauli noise
• Robust and Efficient Hamiltonian Learning
• Classical shadows with Pauli-invariant unitary ensembles
• Pauli error estimation via Population Recovery
• Learning correlated noise in a 39-qubit quantum processor
• Pauli channels can be estimated from syndrome measurements in quantum error correction
• Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrix
• Entanglement-Free Parameter Estimation of Generalized Pauli Channels
• Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection
• Scalably learning quantum many-body Hamiltonians from dynamical data
• Learning logical Pauli noise in quantum error correction
• Leveraging Randomized Compiling for the QITE Algorithm
• Scalable noise characterization of syndrome-extraction circuits with averaged circuit eigenvalue sampling
• Pauli Decomposition via the Fast Walsh-Hadamard Transform
• Pauli Noise Learning for Mid-Circuit Measurements
• Linear Cross Entropy Benchmarking with Clifford Circuits
• Efficient diagnostics for quantum error correction
• Noise-Agnostic Quantum Error Mitigation with Data Augmented Neural Models
• Fidelity-dissipation relations in quantum gates
• Group twirling and noise tailoring for multi-qubit controlled phase gates
• Scalable fast benchmarking for individual quantum gates with local twirling
• Disti-Mator: an entanglement distillation-based state estimator
• Characterization of coherent errors in gate layers with robustness to Pauli noise
• Theory of quantum error mitigation for non-Clifford gates
• Detecting and Eliminating Quantum Noise of Quantum Measurements
• Trotterless Simulation of Open Quantum Systems for NISQ Quantum Devices
• Agnostic Process Tomography
• Corrupted sensing quantum state tomography
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