Experimental neural network enhanced quantum tomography

Abstract

Quantum tomography is currently ubiquitous for testing any implementation of a quantum information processing device. Various sophisticated procedures for state and process reconstruction from measured data are well developed and benefit from precise knowledge of the model describing state preparation and the measurement apparatus. However, physical models suffer from intrinsic limitations as actual measurement operators and trial states cannot be known precisely. This scenario inevitably leads to state-preparation-and-measurement (SPAM) errors degrading reconstruction performance. Here we develop and experimentally implement a machine learning based protocol reducing SPAM errors. We trained a supervised neural network to filter the experimental data and hence uncovered salient patterns that characterize the measurement probabilities for the original state and the ideal experimental apparatus free from SPAM errors. We compared the neural network state reconstruction protocol with a protocol treating SPAM errors by process tomography, as well as to a SPAM-agnostic protocol with idealized measurements. The average reconstruction fidelity is shown to be enhanced by 10\% and 27\%, respectively. The presented methods apply to the vast range of quantum experiments which rely on tomography.

11 pages, 3+6 figures; All data and source code are available online; RevTeX

References in corpus (38)

• Entanglement of Orbital Angular Momentum States of Photons
• On the Measurement of Qubits
• Randomized Benchmarking of Quantum Gates
• Experimental Quantum State Tomography of Optical Fields and Ultrafast Statistical Sampling
• Prescription for experimental determination of the dynamics of a quantum black box
• Unknown Quantum States and Operations, a Bayesian View
• Many-body quantum state tomography with neural networks
• Robust randomized benchmarking of quantum processes
• Complete Characterization of a Quantum Process: the Two-Bit Quantum Gate
• Quantum state estimation
• Demonstration of qubit operations below a rigorous fault tolerance threshold with gate set tomography
• Measuring measurement
• Maximum-likelihood estimation of the density matrix
• Generation and Confirmation of a (100x100)-dimensional entangled Quantum System
• Reconstructing quantum states with generative models
• Self-Consistent Quantum Process Tomography
• Robust Online Hamiltonian Learning
• Adaptive Bayesian Quantum Tomography
• Maximum-likelihood estimation of quantum measurement
• Randomized Benchmarking with Confidence
• Constructing exact representations of quantum many-body systems with deep neural networks
• Latent Space Purification via Neural Density Operators
• Full-field quantum correlations of spatially entangled photons
• Imperfect measurements settings: implications on quantum state tomography and entanglement witnesses
• Measuring azimuthal and radial modes of photons
• Experimental Adaptive Bayesian Tomography
• Robust, self-consistent, closed-form tomography of quantum logic gates on a trapped ion qubit
• Learning hard quantum distributions with variational autoencoders
• Optimization of a solid-state electron spin qubit using Gate Set Tomography
• Recovering quantum gates from few average gate fidelities
• Post-selection free spatial Bell state generation
• Ancilla-Assisted Calibration of a Measuring Apparatus
• Practical adaptive quantum tomography
• Self-calibrating Quantum State Tomography
• Detecting correlated errors in SPAM tomography
• Experimental adaptive process tomography
• Self-calibrating Tomography for Angular Schmidt Modes in Spontaneous Parametric Down-Conversion
• Tomography of Spatial Mode Detectors

Cited by in corpus (88)

• Quantum Error Mitigation
• Deep learning in nano-photonics: inverse design and beyond
• Learning Quantum Systems
• Quantum Machine Learning for Chemistry and Physics
• Quantum Machine Learning: from physics to software engineering
• Experimental Estimation of Quantum State Properties from Classical Shadows
• Time Series Quantum Reservoir Computing with Weak and Projective Measurements
• Computer-inspired Quantum Experiments
• Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential
• Neural network quantum state tomography in a two-qubit experiment
• Efficient quantum state tomography with convolutional neural networks
• Experimental quantum homodyne tomography via machine learning
• Super-resolution linear optical imaging in the far field
• Calibration of quantum sensors by neural networks
• Neural-network quantum state tomography
• Machine learning recognition of light orbital-angular-momentum superpositions
• Rapid classification of quantum sources enabled by machine learning
• Reservoir Computing Approach to Quantum State Measurement
• Deep learning of quantum entanglement from incomplete measurements
• Robust calibration of multiparameter sensors via machine learning at the single-photon level
• Scalability of all-optical neural networks based on spatial light modulators
• Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics
• Neural networks for detecting multimode Wigner-negativity
• Flexible learning of quantum states with generative query neural networks
• Bilinear dynamic mode decomposition for quantum control
• Production and applications of non-Gaussian quantum states of light
• On the experimental feasibility of quantum state reconstruction via machine learning
• Objective Compressive Quantum Process Tomography
• Deep reinforcement learning for universal quantum state preparation via dynamic pulse control
• Gradient-descent quantum process tomography by learning Kraus operators
• Regression of high dimensional angular momentum states of light
• The effect of quantum noise on algorithmic perfect quantum state transfer on NISQ processors
• Neural-network approach for identifying nonclassicality from click-counting data
• Synthetic gauge field and chiral physics on two-leg superconducting circuits
• Neural networks for on-the-fly single-shot state classification
• Spectral Density Classification For Environment Spectroscopy
• Quantum State Tomography with Conditional Generative Adversarial Networks
• Convergence of reconstructed density matrix to a pure state using maximal entropy approach
• Variational Approach to Quantum State Tomography based on Maximal Entropy Formalism
• Active Learning Approach to Optimization of Experimental Control
• Quantum tomography benchmarking
• Enhancing quantum state tomography via resource-efficient attention-based neural networks
• Quantum-machine-learning channel discrimination
• Neural networks for quantum state tomography with constrained measurements
• Neural Network-Based Design of Approximate Gottesman-Kitaev-Preskill Code
• Estimating the Euclidean quantum propagator with deep generative modeling of Feynman paths
• Classification and reconstruction of optical quantum states with deep neural networks
• Generation of scalable many-body Bell correlations in spin chains with short-range two-body interactions
• Neural networks in quantum many-body physics: a hands-on tutorial
• Quantum error reduction with deep neural network applied at the post-processing stage
• Machine Learning for Estimation and Control of Quantum Systems
• Quantum tomography of noisy ion-based qudits
• Experimental Learning of Pure Quantum States using Sequential Single-Shot Measurement Outcomes
• Improved Quantum State Tomography for the Systems with XX+YY Couplings and Z Readouts
• Entanglement Structure Detection via Computer Vision
• Measurement-Induced Randomness and Structure in Controlled Qubit Processes
• Exponentially improved efficient machine learning for quantum many-body states with provable guarantees
• Enhanced quantum state preparation via stochastic prediction of neural network
• Machine learning the Kondo entanglement cloud from local measurements
• Quantum reservoir computing for photonic entanglement witnessing
• Probing Criticality in Quantum Spin Chains with Neural Networks
• Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
• Applications of model-aware reinforcement learning in Bayesian quantum metrology
• Noise Classification in Three-Level Quantum Networks by Machine Learning
• Neural network assisted quantum state and process tomography using limited data sets
• Quantum error mitigation in the regime of high noise using deep neural network: Trotterized dynamics
• Entanglement transitions from restricted Boltzmann machines
• Entanglement Detection with Quantum-inspired Kernels and SVMs
• Spatial Quantum State Tomography with A Deformable Mirror
• Retrieving space-dependent polarization transformations via near-optimal quantum process tomography
• Quantum state tomography with disentanglement algorithm
• Preparing Quantum States by Measurement-feedback Control with Bayesian Optimization
• Echo-evolution data generation for quantum error mitigation via neural networks
• Data-Driven Forecasting of Non-Equilibrium Solid-State Dynamics
• Deep Neural Network-assisted improvement of quantum compressed sensing tomography
• Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
• Improved Tomographic Estimates by Specialised Neural Networks
• Benchmarking quantum tomography completeness and fidelity with machine learning
• Experimental demonstration of enhanced quantum tomography via quantum reservoir processing
• Corrupted sensing quantum state tomography
• All-optical neural network quantum state tomography
• Quantum process matrices as images: new tools to design novel denoising methods
• Quantum Pattern Recognition in Photonic Circuits
• Multiclass classification of dephasing channels
• Neural Network Based Qubit Environment Characterization
• Quantum process tomography of adiabatic and superadiabatic stimulated Raman passage
• Mean propagation velocity of multiphoton wave-packet states with nonzero Lorentz-invariant mass
• A context-aware gate set tomography characterization of superconducting qubits