Retrieving Quantum Information with Active Learning

Abstract

Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum information retrieval, which is a crucial task in the design of quantum experiments. Meanwhile, when dealing with large data output, we employ active learning for the sake of classification with minimal cost in fidelity loss. Indeed, labeling only 5% samples, we achieve almost 90% rate estimation. The introduction of active learning methods in the data analysis of quantum experiments will enhance applications of quantum technologies.

References in corpus (18)

• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• A survey of active learning algorithms for supervised remote sensing image classification
• Quantum reinforcement learning
• Active learning machine learns to create new quantum experiments
• Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
• Soundness and completeness of quantum root-mean-square errors
• Learning the quantum algorithm for state overlap
• Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems
• Variational neural network ansatz for steady states in open quantum systems
• Constructing neural stationary states for open quantum many-body systems
• Solving the Bose-Hubbard model with machine learning
• Quantum Averages of Weak Values
• Quantum Memristors
• Quantum Memristors with Superconducting Circuits
• Measurement-based adaptation protocol with quantum reinforcement learning
• Quantum Memristors in Quantum Photonics
• Adaptive quantum computation in changing environments using projective simulation
• Multiqubit and multilevel quantum reinforcement learning with quantum technologies

Cited by in corpus (8)

• Quantum Machine Learning: from physics to software engineering
• Breaking Adiabatic Quantum Control with Deep Learning
• Adaptive Quantum State Tomography with Active Learning
• Active Learning Approach to Optimization of Experimental Control
• Characterization of a driven two-level quantum system by Supervised Learning
• Quantum Active Learning
• Active Learning in Physics: From 101, to Progress, and Perspective
• Active Learning for Quantum Mechanical Measurements