Noise-Resilient Quantum Reinforcement Learning

Abstract

As a branch of quantum machine learning, quantum reinforcement learning (QRL) aims to solve complex sequential decision-making problems more efficiently and effectively than its classical counterpart by exploiting quantum resources. However, in the noisy intermediate-scale quantum (NISQ) era, its realization is challenged by the ubiquitous noise-induced decoherence. Here, we propose a noise-resilient QRL scheme for a quantum eigensolver with a two-level system as an agent. By investigating the non-Markovian decoherence effect on the QRL for solving the eigenstates of the agent-environment interaction Hamiltonian, we find that the formation of a bound state in the energy spectrum of the total agent-noise system restores the QRL performance to that in the noiseless case. Providing a universal physical mechanism to suppress the decoherence effect on quantum machine learning, our result lays the foundation for designing NISQ algorithms and offers a guideline for their practical implementation.

References in corpus (68)

• Quantum Computing in the NISQ era and beyond
• A variational eigenvalue solver on a quantum processor
• Supervised learning with quantum enhanced feature spaces
• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• Non-Markovian dynamics in open quantum systems
• Error mitigation for short-depth quantum circuits
• Circuit-centric quantum classifiers
• Power of data in quantum machine learning
• Challenges and Opportunities in Quantum Machine Learning
• Data re-uploading for a universal quantum classifier
• Entanglement of two qubits mediated by one-dimensional plasmonic waveguides
• Quantum advantage in learning from experiments
• A rigorous and robust quantum speed-up in supervised machine learning
• Hybrid Quantum-Classical Hierarchy for Mitigation of Decoherence and Determination of Excited States
• Quantum-enhanced machine learning
• Quantum reinforcement learning
• Decoherence-protected quantum gates for a hybrid solid-state spin register
• A generative modeling approach for benchmarking and training shallow quantum circuits
• Quantum Boltzmann Machine
• General non-Markovian dynamics of open quantum systems
• Reinforcement Learning in Different Phases of Quantum Control
• Dissipative charging of a quantum battery
• Quantum electrodynamics near a photonic band-gap
• Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
• Quantum speedup for active learning agents
• Generalization in Quantum Machine Learning: a Quantum Information Perspective
• Decoherence in adiabatic quantum computation
• Mesoscopic Spin-Boson Models of Trapped Ions
• Error Mitigation by Symmetry Verification on a Variational Quantum Eigensolver
• Spontaneous Emission in a Matter-Wave Open Quantum System
• Learning Robust and High-Precision Quantum Controls
• Machine learning for long-distance quantum communication
• Machine learning non-Markovian quantum dynamics
• Generalizable control for quantum parameter estimation through reinforcement learning
• Remote Charging and Degradation Suppression for the Quantum Battery
• Learning hard quantum distributions with variational autoencoders
• Automatic spin-chain learning to explore the quantum speed limit
• Retrieving ideal precision in noisy quantum optical metrology
• Reconstruction of a Photonic Qubit State with Reinforcement Learning
• Signatures of a Noise-Induced Quantum Phase Transition in a Mesoscopic Metal Ring
• Measurement-based adaptation protocol with quantum reinforcement learning
• Probe incompatibility in multiparameter noisy quantum metrology
• Quantum optimal control of multi-level dissipative quantum systems with Reinforcement Learning
• Employing Non-Markovian effects to improve the performance of a quantum Otto refrigerator
• Unsupervised Learning of Non-Hermitian Topological Phases
• Improving the dynamics of quantum sensors with reinforcement learning
• Anomalous decoherence in a dissipative two-level system
• Faster State Preparation across Quantum Phase Transition Assisted by Reinforcement Learning
• Non-Markovian Sensing of a Quantum Reservoir
• Floquet Engineering to Overcome No-Go Theorem of Noisy Quantum Metrology
• Canonical versus noncanonical equilibration dynamics of open quantum systems
• Why and when is pausing beneficial in quantum annealing?
• Pulse variational quantum eigensolver on cross-resonance based hardware
• Noisy intermediate-scale quantum algorithm for semidefinite programming
• Quantum metrology of noisy spreading channels
• Formation of Matter-Wave Polaritons in an Optical Lattice
• Supervised learning of time-independent Hamiltonians for gate design
• Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data
• Reinforcement learning for semi-autonomous approximate quantum eigensolver
• Characterization of decohering quantum systems: Machine learning approach
• Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer
• Decoherence induced deformation of the ground state in adiabatic quantum computation
• Decohering Tensor Network Quantum Machine Learning Models
• Quantum reinforcement learning in the presence of thermal dissipation
• The role of coherence theory in attractor quantum neural networks
• Experimental semi-autonomous eigensolver using reinforcement learning