Learning-based quantum error mitigation

Abstract

If NISQ-era quantum computers are to perform useful tasks, they will need to employ powerful error mitigation techniques. Quasi-probability methods can permit perfect error compensation at the cost of additional circuit executions, provided that the nature of the error model is fully understood and sufficiently local both spatially and temporally. Unfortunately these conditions are challenging to satisfy. Here we present a method by which the proper compensation strategy can instead be learned ab initio. Our training process uses multiple variants of the primary circuit where all non-Clifford gates are substituted with gates that are efficient to simulate classically. The process yields a configuration that is near-optimal versus noise in the real system with its non-Clifford gate set. Having presented a range of learning strategies, we demonstrate the power of the technique both with real quantum hardware (IBM devices) and exactly-emulated imperfect quantum computers. The systems suffer a range of noise severities and types, including spatially and temporally correlated variants. In all cases the protocol successfully adapts to the noise and mitigates it to a high degree.

28 pages, 19 figures

References in corpus (5)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Surface codes: Towards practical large-scale quantum computation
• Simple pulses for elimination of leakage in weakly nonlinear qubits
• High-fidelity preparation, gates, memory and readout of a trapped-ion quantum bit
• Fast simulation of stabilizer circuits using a graph state representation

Cited by in corpus (68)

• Variational Quantum Algorithms
• The Variational Quantum Eigensolver: a review of methods and best practices
• Quantum Error Mitigation
• Quantum Simulation for High Energy Physics
• Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors
• Virtual Distillation for Quantum Error Mitigation
• Quantum circuit architecture search for variational quantum algorithms
• QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
• Training Quantum Embedding Kernels on Near-Term Quantum Computers
• Quantum error mitigation as a universal error-minimization technique: applications from NISQ to FTQC eras
• Efficient measure for the expressivity of variational quantum algorithms
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
• Towards understanding the power of quantum kernels in the NISQ era
• Self-mitigating Trotter circuits for SU(2) lattice gauge theory on a quantum computer
• Variational Quantum Singular Value Decomposition
• Quasiprobability decompositions with reduced sampling overhead
• Toward Practical Quantum Embedding Simulation of Realistic Chemical Systems on Near-term Quantum Computers
• Optimal resource cost for error mitigation
• Error mitigation for variational quantum algorithms through mid-circuit measurements
• Shadow Distillation: Quantum Error Mitigation with Classical Shadows for Near-Term Quantum Processors
• Quantum Error Mitigation using Symmetry Expansion
• Universal Sampling Lower Bounds for Quantum Error Mitigation
• Dual-state purification for practical quantum error mitigation
• Universal cost bound of quantum error mitigation based on quantum estimation theory
• Quantum error mitigation via matrix product operators
• Overlapped grouping measurement: A unified framework for measuring quantum states
• Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
• Simulating the electronic structure of spin defects on quantum computers
• Computationally Efficient Zero Noise Extrapolation for Quantum Gate Error Mitigation
• Probing confinement in a ${\mathbb{Z}}_2$ lattice gauge theory on a quantum computer
• Unifying and benchmarking state-of-the-art quantum error mitigation techniques
• Quantifying the effect of gate errors on variational quantum eigensolvers for quantum chemistry
• Efficient quantum readout-error mitigation for sparse measurement outcomes of near-term quantum devices
• Error statistics and scalability of quantum error mitigation formulas
• Accelerated quantum Monte Carlo with mitigated error on noisy quantum computer
• Steering-enhanced quantum metrology using superpositions of noisy phase shifts
• Efficiently improving the performance of noisy quantum computers
• Error mitigation with Clifford quantum-circuit data
• Low-depth Hamiltonian Simulation by Adaptive Product Formula
• A deep learning model for noise prediction on near-term quantum devices
• Optimal quantum dataset for learning a unitary transformation
• Quantum Error Mitigation Relying on Permutation Filtering
• Mitigating algorithmic errors in quantum optimization through energy extrapolation
• Activation of metrologically useful genuine multipartite entanglement
• Deep learning enhanced noise spectroscopy of a spin qubit environment
• NISQ: Error Correction, Mitigation, and Noise Simulation
• Error Suppression for Arbitrary-Size Black Box Quantum Operations
• Maxwell's two-demon engine under pure dephasing noise
• Quantum error reduction with deep neural network applied at the post-processing stage
• Resource frugal optimizer for quantum machine learning
• Identification of topological phases using classically-optimized variational quantum eigensolver
• Mitigating Quantum Errors via Truncated Neumann Series
• Quantum Error Mitigation via Quantum-Noise-Effect Circuit Groups
• Scalable evaluation of quantum-circuit error loss using Clifford sampling
• Divide-and-conquer verification method for noisy intermediate-scale quantum computation
• The dilemma of quantum neural networks
• Quantum algorithm for evaluating operator size with Bell measurements
• Quantum algorithms for Schrieffer-Wolff transformation
• A Practical Framework for Quantum Error Mitigation
• Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses
• Qubit-efficient exponential suppression of errors
• Improving the efficiency of learning-based error mitigation
• Wildcard error: Quantifying unmodeled errors in quantum processors
• Method for Generating Randomly Perturbed Density Operators Subject to Different Sets of Constraints
• Accelerating variational quantum algorithms with multiple quantum processors
• Modelling for Quantum Error Mitigation
• Quantum Noise Sensing by generating Fake Noise
• Study of noise in virtual distillation circuits for quantum error mitigation