Framework for Learning and Control in the Classical and Quantum Domains
arXiv:2307.04256 · doi:10.1016/j.aop.2023.169471
Abstract
Control and learning are key to technological advancement, both in the classical and quantum domains, yet their interrelationship is insufficiently clear in the literature, especially between classical and quantum definitions of control and learning. We construct a framework that formally relates learning and control, both classical and quantum, to each other, with this formalism showing how learning can aid control. Furthermore, our framework helps to identify interesting unsolved problems in the nexus of classical and quantum control and learning and help in choosing tools to solve problems. As a use case, we cast the well-studied problem of adaptive quantum-enhanced interferometric-phase estimation as a supervised learning problem for devising feasible control policies. Our unification of these fields relies on diagrammatically representing the state of knowledge, which elegantly summarizes existing knowledge and exposes knowledge gaps.
arXiv comment28 pages, 11 figures, 1 table
References in corpus (87)
- Quantum Computing in the NISQ era and beyond
- Quantum Machine Learning
- Variational Quantum Algorithms
- Quantum-enhanced measurements: beating the standard quantum limit
- Supervised learning with quantum enhanced feature spaces
- Machine learning and the physical sciences
- The theory of variational hybrid quantum-classical algorithms
- Quantum support vector machine for big data classification
- Knowledge Graphs
- Noisy intermediate-scale quantum (NISQ) algorithms
- Quantum machine learning in feature Hilbert spaces
- An introduction to quantum machine learning
- Quantum fingerprinting
- The Variational Quantum Eigensolver: a review of methods and best practices
- A high-bias, low-variance introduction to Machine Learning for physicists
- Control of quantum phenomena: Past, present, and future
- Quantum Computational Supremacy
- Power of data in quantum machine learning
- Challenges and Opportunities in Quantum Machine Learning
- Entropic Uncertainty Relations and their Applications
- Quantum control theory and applications: A survey
- A rigorous and robust quantum speed-up in supervised machine learning
- Quantum Data Fitting
- Quantum-enhanced machine learning
- Geometric Quantum Mechanics
- A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
- Towards Quantum Machine Learning with Tensor Networks
- Optimal states and almost optimal adaptive measurements for quantum interferometry
- Quantum feedback: theory, experiments, and applications
- Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
- Quantization Methods: A Guide for Physicists and Analysts
- Modeling and Control of Quantum Systems: An Introduction
- Diagnosing Barren Plateaus with Tools from Quantum Optimal Control
- Fast machine-learning online optimization of ultra-cold-atom experiments
- Quantum machine learning beyond kernel methods
- Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
- The role of the rigged Hilbert space in Quantum Mechanics
- How to perform the most accurate possible phase measurements
- Quantum circuit architecture search for variational quantum algorithms
- Exploiting symmetry in variational quantum machine learning
- A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks
- Incorporating Domain Knowledge into Deep Neural Networks
- Experimental quantum speed-up in reinforcement learning agents
- Theory of overparametrization in quantum neural networks
- Artificial Intelligence and Machine Learning for Quantum Technologies
- Group-Invariant Quantum Machine Learning
- Learning Quantum Systems
- A quantum Fredkin gate
- Quantum-inspired algorithms in practice
- Machine learning for discriminating quantum measurement trajectories and improving readout
- High-Fidelity Single-Shot Toffoli Gate via Quantum Control
- Deep Reinforcement Learning for Quantum Gate Control
- From pulses to circuits and back again: A quantum optimal control perspective on variational quantum algorithms
- An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes
- Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule
- Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics
- Generalizable control for quantum parameter estimation through reinforcement learning
- Evolutionary Algorithms for Hard Quantum Control
- Quantum-optimal-control-inspired ansatz for variational quantum algorithms
- Quantum gate learning in engineered qubit networks: Toffoli gate with always-on interactions
- A Mean-Field Optimal Control Formulation of Deep Learning
- Differential Evolution for Many-Particle Adaptive Quantum Metrology
- Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory
- Demonstration of algorithmic quantum speedup
- Adaptive quantum computation in changing environments using projective simulation
- Deep reinforcement learning for quantum multiparameter estimation
- Improving the dynamics of quantum sensors with reinforcement learning
- Quantum learning: optimal classification of qubit states
- QInfer: Statistical inference software for quantum applications
- ResQNets: A Residual Approach for Mitigating Barren Plateaus in Quantum Neural Networks
- Adaptive Measurements in the Optical Quantum Information Laboratory
- Probe optimization for quantum metrology via closed-loop learning control
- Variational Quantum Classifiers Through the Lens of the Hessian
- New Trends in Quantum Machine Learning
- Pulse based Variational Quantum Optimal Control for hybrid quantum computing
- Robustness of Quantum-Enhanced Adaptive Phase Estimation
- Unified analysis of terminal-time control in classical and quantum systems
- Evaluation of Parameterized Quantum Circuits with Cross-Resonance Pulse-Driven Entanglers
- Quantum Geometric Machine Learning for Quantum Circuits and Control
- Feedback Ansatz for Adaptive-Feedback Quantum Metrology Training with Machine Learning
- Assessing data postprocessing for quantum estimation
- Short Quantum Circuits in Reinforcement Learning Policies for the Vehicle Routing Problem
- Benchmarking Machine Learning Algorithms for Adaptive Quantum Phase Estimation with Noisy Intermediate-Scale Quantum Sensors
- Optimizing Counterdiabaticity by Variational Quantum Circuits
- Learning Control of Quantum Systems
- Two new results about quantum exact learning
- Ordered Measurements of Permutationally-Symmetric Qubit Strings