Neural-network quantum state tomography

Abstract

We revisit the application of neural networks techniques to quantum state tomography. We confirm that the positivity constraint can be successfully implemented with trained networks that convert outputs from standard feed-forward neural networks to valid descriptions of quantum states. Any standard neural-network architecture can be adapted with our method. Our results open possibilities to use state-of-the-art deep-learning methods for quantum state reconstruction under various types of noise.

arXiv comment8 pages, 4 color figures. Comments are most welcome

References in corpus (49)

• An overview of gradient descent optimization algorithms
• An Introduction to Variational Autoencoders
• On the Measurement of Qubits
• QuTiP: An open-source Python framework for the dynamics of open quantum systems
• Quantum state tomography via compressed sensing
• A high-bias, low-variance introduction to Machine Learning for physicists
• Experimental Quantum State Tomography of Optical Fields and Ultrafast Statistical Sampling
• Unknown Quantum States and Operations, a Bayesian View
• Many-body quantum state tomography with neural networks
• Efficient quantum state tomography
• Quantum generative adversarial learning
• Quantum state estimation
• Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
• The Bloch Vector for N-Level Systems
• Optimal, reliable estimation of quantum states
• Bloch vectors for qudits
• Reconstructing quantum states with generative models
• Active learning machine learns to create new quantum experiments
• Efficient tomography of a quantum many-body system
• Automated Search for new Quantum Experiments
• Identifying Quantum Phase Transitions using Artificial Neural Networks on Experimental Data
• Approximating quantum many-body wave-functions using artificial neural networks
• A Neural Decoder for Topological Codes
• Diluted maximum-likelihood algorithm for quantum tomography
• Generating random density matrices
• Experimental neural network enhanced quantum tomography
• Quantum State Tomography via Linear Regression Estimation
• Constructing exact representations of quantum many-body systems with deep neural networks
• Machine-learning-assisted correction of correlated qubit errors in a topological code
• Speedup for quantum optimal control from automatic differentiation based on graphics processing units
• Deep Neural Network Probabilistic Decoder for Stabilizer Codes
• Practical Bayesian Tomography
• Using a Recurrent Neural Network to Reconstruct Quantum Dynamics of a Superconducting Qubit from Physical Observations
• Machine learning assisted quantum state estimation
• Integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits
• Least-squares inversion for density-matrix reconstruction
• Random Bures mixed states and the distribution of their purity
• Learning hard quantum distributions with variational autoencoders
• Local-measurement-based quantum state tomography via neural networks
• Entanglement Classification via Neural Network Quantum States
• Attention-based Quantum Tomography
• Experimental quantum homodyne tomography via machine learning
• On the Optimality of Square Root Measurements in Quantum State Discrimination
• Variational Quantum Circuits for Quantum State Tomography
• Adaptive compressive tomography with no a priori information
• Parametrizations of density matrices
• Eigenstate extraction with neural-network tomography
• Deep Q-learning decoder for depolarizing noise on the toric code
• Neural-network approach for identifying nonclassicality from click-counting data

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• Enhancing quantum state tomography via resource-efficient attention-based neural networks
• Transformer neural networks and quantum simulators: a hybrid approach for simulating strongly correlated systems
• Simulating noisy quantum channels via quantum state preparation algorithms
• Generation of scalable many-body Bell correlations in spin chains with short-range two-body interactions
• Efficient learning of mixed-state tomography for photonic quantum walk
• Machine learning the Kondo entanglement cloud from local measurements
• Efficient factored gradient descent algorithm for quantum state tomography
• Gradient-descent methods for fast quantum state tomography
• Lee-Yang theory of quantum phase transitions with neural network quantum states
• Non-Markovian Quantum Gate Set Tomography
• Multiparameter estimation of continuous-time Quantum Walk Hamiltonians through Machine Learning
• Neural network assisted quantum state and process tomography using limited data sets
• Observing Schrödinger's Cat with Artificial Intelligence: Emergent Classicality from Information Bottleneck
• Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model
• Recovery of Quantum Correlations using Machine Learning
• Deep Neural Network-assisted improvement of quantum compressed sensing tomography
• Robustness of random-control quantum-state tomography
• Adaptive, symmetry-informed Bayesian metrology for precise quantum technology measurements
• Reliable confidence regions for quantum tomography using distribution moments
• Neural quantum states for entanglement depth certification from randomized Pauli measurements
• Tensorization of neural networks for improved privacy and interpretability