QInfer: Statistical inference software for quantum applications

Abstract

Characterizing quantum systems through experimental data is critical to applications as diverse as metrology and quantum computing. Analyzing this experimental data in a robust and reproducible manner is made challenging, however, by the lack of readily-available software for performing principled statistical analysis. We improve the robustness and reproducibility of characterization by introducing an open-source library, QInfer, to address this need. Our library makes it easy to analyze data from tomography, randomized benchmarking, and Hamiltonian learning experiments either in post-processing, or online as data is acquired. QInfer also provides functionality for predicting the performance of proposed experimental protocols from simulated runs. By delivering easy-to-use characterization tools based on principled statistical analysis, QInfer helps address many outstanding challenges facing quantum technology.

19 pages, a full Users' Guide and illustrated examples describing the QInfer software library

References in corpus (14)

• The NumPy array: a structure for efficient numerical computation
• Robust randomized benchmarking of quantum processes
• Entanglement-free Heisenberg-limited phase estimation
• Characterization of addressability by simultaneous randomized benchmarking
• Robust Online Hamiltonian Learning
• Random Quantum Operations
• Single shot parameter estimation via continuous quantum measurement
• Practical adaptive quantum tomography
• Accelerated Randomized Benchmarking
• Quantum Bootstrapping via Compressed Quantum Hamiltonian Learning
• Estimating the fidelity of T gates using standard interleaved randomized benchmarking
• Robust and efficient in situ quantum control
• Magnetometry via a double-pass continuous quantum measurement of atomic spin
• Quantum Model Averaging

Cited by in corpus (33)

• Silicon qubit fidelities approaching incoherent noise limits via pulse engineering
• Gate Set Tomography
• Learning Quantum Systems
• Metropolitan-scale heralded entanglement of solid-state qubits
• Detecting and tracking drift in quantum information processors
• Experimental demonstration of entanglement delivery using a quantum network stack
• Multi-exponential Error Extrapolation and Combining Error Mitigation Techniques for NISQ Applications
• Learning models of quantum systems from experiments
• Real Randomized Benchmarking
• Deep reinforcement learning for quantum multiparameter estimation
• Practical adaptive quantum tomography
• Statistical analysis of randomized benchmarking
• Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation
• Bayesian Quantum Noise Spectroscopy
• Statistical Inference with Quantum Measurements: Methodologies for Nitrogen Vacancy Centers in Diamond
• Simplified algorithms for adaptive experiment design in parameter estimation
• Operational, gauge-free quantum tomography
• Active Learning of Quantum System Hamiltonians yields Query Advantage
• Variational certification of quantum devices
• Framework for Learning and Control in the Classical and Quantum Domains
• Applications of model-aware reinforcement learning in Bayesian quantum metrology
• Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
• Structured Filtering
• A comparison of three ways to measure time-dependent densities with quantum simulators
• Learning to Detect Entanglement
• Lindblad-like quantum tomography for non-Markovian quantum dynamical maps
• A de Finetti theorem for quantum causal structures
• Efficient inference of quantum system parameters by Approximate Bayesian Computation
• Unorthodox parallelization for Bayesian quantum state estimation
• Principles of quantum functional testing
• Adaptive quantum state tomography with iterative particle filtering
• Bayesian ACRONYM Tuning
• Sequential Bayesian experiment design for adaptive Ramsey sequence measurements