Machine Learning for Precise Quantum Measurement

Abstract

Adaptive feedback schemes are promising for quantum-enhanced measurements yet are complicated to design. Machine learning can autonomously generate algorithms in a classical setting. Here we adapt machine learning for quantum information and use our framework to generate autonomous adaptive feedback schemes for quantum measurement. In particular our approach replaces guesswork in quantum measurement by a logical, fully-automatic, programmable routine. We show that our method yields schemes that outperform the best known adaptive scheme for interferometric phase estimation.

7 pages, 3 figures, 2 tables

References in corpus (2)

• Entanglement-free Heisenberg-limited phase estimation
• A Quantum-Enhanced Prototype Gravitational-Wave Detector

Cited by in corpus (113)

• Quantum Machine Learning
• General framework for estimating the ultimate precision limit in noisy quantum-enhanced metrology
• Elucidating Reaction Mechanisms on Quantum Computers
• Quantum Fisher information matrix and multiparameter estimation
• Reinforcement Learning with Neural Networks for Quantum Feedback
• Quantum-enhanced optical phase tracking
• Hamiltonian Learning and Certification Using Quantum Resources
• Robust Online Hamiltonian Learning
• Efficient Bayesian Phase Estimation
• Optimized quantum sensing with a single electron spin using real-time adaptive measurements
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Machine Learning for Quantum Matter
• Machine learning for discriminating quantum measurement trajectories and improving readout
• Machine Learning Assisted Many-Body Entanglement Measurement
• Machine learning method for state preparation and gate synthesis on photonic quantum computers
• When does reinforcement learning stand out in quantum control? A comparative study on state preparation
• Phase estimation without a priori knowledge in the presence of loss
• An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes
• Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics
• Time Series Quantum Reservoir Computing with Weak and Projective Measurements
• Experimental Phase Estimation Enhanced By Machine Learning
• Quantum-enhanced metrology for multiple phase estimation with noise
• Improved Quantum Magnetometry beyond the Standard Quantum Limit
• Quantum Hamiltonian Learning Using Imperfect Quantum Resources
• Variational Quantum Algorithm for Estimating the Quantum Fisher Information
• Optimal Scheme for Quantum Metrology
• Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach
• Entanglement-assisted quantum metrology
• Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements
• The minimum error probability of quantum illumination
• Differential Evolution for Many-Particle Adaptive Quantum Metrology
• Detecting quantum entanglement with unsupervised learning
• A Tutorial on Optimal Control and Reinforcement Learning methods for Quantum Technologies
• Calibration of quantum sensors by neural networks
• QuanEstimation: An open-source toolkit for quantum parameter estimation
• Adaptive quantum computation in changing environments using projective simulation
• Benchmarking neural networks for quantum computation
• Deep reinforcement learning for quantum multiparameter estimation
• Rapid classification of quantum sources enabled by machine learning
• Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation
• Magnetic-field-learning using a single electronic spin in diamond with one-photon-readout at room temperature
• Feedback control in quantum optics: an overview of experimental breakthroughs and areas of application
• Critical quantum metrology assisted by real-time feedback control
• Machine learning \& artificial intelligence in the quantum domain
• Measurement-assisted Landau-Zener transitions
• Robust calibration of multiparameter sensors via machine learning at the single-photon level
• Steerability detection of arbitrary 2-qubit state via machine learning
• Achieving Heisenberg scaling with maximally entangled states: an analytic upper bound for the attainable root mean square error
• Probe optimization for quantum metrology via closed-loop learning control
• Advances in quantum machine learning
• Neural networks for detecting multimode Wigner-negativity
• Efficient estimation of resonant coupling between quantum systems
• Strategy for quantum algorithm design assisted by machine learning
• Quantum learning of coherent states
• Quantum Relief Algorithm
• Multi-copy programmable discrimination of general qubit states
• Practical Black Box Hamiltonian Learning
• Machine Learning and Quantum Devices
• Adaptive quantum tomography
• Robustness of Quantum-Enhanced Adaptive Phase Estimation
• Reinforcement learning for semi-autonomous approximate quantum eigensolver
• Characterization of decohering quantum systems: Machine learning approach
• Experimental realization of a quantum image classifier via tensor-network-based machine learning
• Learning unknown pure quantum states
• Feedback Ansatz for Adaptive-Feedback Quantum Metrology Training with Machine Learning
• Neural-network approach for identifying nonclassicality from click-counting data
• Assessing data postprocessing for quantum estimation
• Anti-crosstalk high-fidelity state discrimination for superconducting qubits
• Benchmarking Machine Learning Algorithms for Adaptive Quantum Phase Estimation with Noisy Intermediate-Scale Quantum Sensors
• Reconstructing dynamical networks via feature ranking
• Randomized gap and amplitude estimation
• Quantum-enhanced microscopy with binary-outcome photon counting
• Swarm optimization for adaptive phase measurements with low visibility
• Machine classification for probe based quantum thermometry
• Protocol for secure quantum machine learning at a distant place
• Frequentist Parameter Estimation with Supervised Learning
• Generation and storage of spin squeezing via learning-assisted optimal control
• Loss-resistant unambiguous phase measurement
• Machine learning applied to quantum synchronization-assisted probing
• Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner
• Single-shot non-Gaussian Measurements for Optical Phase Estimation
• Volterra filters for quantum estimation and detection
• Quantum Reliability
• Determination of the asymptotic limits of adaptive photon counting measurements for coherent-state optical phase estimation
• Identifying nonclassicality from experimental data using artificial neural networks
• Ordered Measurements of Permutationally-Symmetric Qubit Strings
• Optimizing qubit Hamiltonian parameter estimation algorithms using PSO
• Experimental investigation of Bayesian bounds in multiparameter estimation
• Optimizing quantum-enhanced Bayesian multiparameter estimation of phase and noise in practical sensors
• High-speed photon correlation monitoring of amplified quantum noise by chaos using deep-learning balanced homodyne detection
• Adaptive identification of coherent states
• Noise prediction and reduction of single electron spin by deep-learning-enhanced feedforward control
• Decoupling approximation robustly reconstructs directed dynamical networks
• Deterministic quantum state transfer of atoms in a random magnetic field
• Ancilla-assisted schemes are beneficial for Gaussian state phase estimation
• Quantum information spreading in a disordered quantum walk
• QuanEstimation.jl: An open-source Julia framework for quantum parameter estimation
• Achieving Heisenberg limit under General Markov Noise without Ancilla
• Machine Learning for Quantum-Enhanced Gravitational-Wave Observatories
• Algebraic and algorithmic frameworks for optimized quantum measurements
• Combinatorial optimization through variational quantum power method
• Beating standard quantum limit via two-axis magnetic susceptibility measurement
• Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network
• Improved Tomographic Estimates by Specialised Neural Networks
• Research progress of artificial intelligence empowered quantum communication and quantum sensing systems
• Limitations of strong coupling in non-Markovian quantum thermometry
• Single-shot Adaptive Measurement for Quantum-enhanced Metrology
• Channel-noise tracking for sub-shot-noise-limited receivers with neural networks
• Optimal finite-dimensional probe states for quantum phase estimation
• Quantum process matrices as images: new tools to design novel denoising methods
• Dealing with ignorance: universal discrimination, learning and quantum correlations
• Adaptive Bayesian algorithm for achieving desired quantum transition
• Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing