Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation

Abstract

Topological error correcting codes, and particularly the surface code, currently provide the most feasible roadmap towards large-scale fault-tolerant quantum computation. As such, obtaining fast and flexible decoding algorithms for these codes, within the experimentally relevant context of faulty syndrome measurements, is of critical importance. In this work, we show that the problem of decoding such codes, in the full fault-tolerant setting, can be naturally reformulated as a process of repeated interactions between a decoding agent and a code environment, to which the machinery of reinforcement learning can be applied to obtain decoding agents. As a demonstration, by using deepQ learning, we obtain fast decoding agents for the surface code, for a variety of noise-models.

arXiv comment15 pages, 11 figures, associated code repository available at https://github.com/R-Sweke/DeepQ-Decoding

References in corpus (43)

• Fault-tolerant quantum computation by anyons
• Non-Abelian Anyons and Topological Quantum Computation
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Logic gates at the surface code threshold: Superconducting qubits poised for fault-tolerant quantum computing
• Topological quantum memory
• Universal Quantum Computation with ideal Clifford gates and noisy ancillas
• Machine learning phases of matter
• Quantum Error Correction for Quantum Memories
• Exponential Protection of Zero Modes in Majorana Islands
• Learning phase transitions by confusion
• The European Quantum Technologies Roadmap
• Roads towards fault-tolerant universal quantum computation
• Realization of Three-Qubit Quantum Error Correction with Superconducting Circuits
• Surface code quantum computing by lattice surgery
• Detecting arbitrary quantum errors via stabilizer measurements on a sublattice of the surface code
• Experimental Quantum Computations on a Topologically Encoded Qubit
• Efficient Representation of Quantum Many-body States with Deep Neural Networks
• Active learning machine learns to create new quantum experiments
• Low-distance Surface Codes under Realistic Quantum Noise
• Fast Decoders for Topological Quantum Codes
• Reinforcement Learning with Neural Networks for Quantum Feedback
• Quantum memories at finite temperature
• Demonstration of weight-four parity measurements in the surface code architecture
• A Neural Decoder for Topological Codes
• Fault-tolerant quantum error detection
• Constructing exact representations of quantum many-body systems with deep neural networks
• Probing many-body localization with neural networks
• Machine-learning-assisted correction of correlated qubit errors in a topological code
• Fault-tolerant thresholds for quantum error correction with the surface code
• Identifying Quantum Phase Transitions with Adversarial Neural Networks
• Poking holes and cutting corners to achieve Clifford gates with the surface code
• Decoding Small Surface Codes with Feedforward Neural Networks
• Deep Neural Network Probabilistic Decoder for Stabilizer Codes
• Quantum error correction for the toric code using deep reinforcement learning
• Lattice Surgery with a Twist: Simplifying Clifford Gates of Surface Codes
• Deep neural decoders for near term fault-tolerant experiments
• Efficient Decoders for Qudit Topological Codes
• Comparing neural network based decoders for the surface code
• Cellular-automaton decoders for topological quantum memories
• Scalable Neural Network Decoders for Higher Dimensional Quantum Codes
• Cellular automaton decoders of topological quantum memories in the fault tolerant setting
• Reinforcement learning for optimal error correction of toric codes
• Deep Q-learning decoder for depolarizing noise on the toric code

Cited by in corpus (39)

• Artificial Intelligence and Machine Learning for Quantum Technologies
• Identifying optimal cycles in quantum thermal machines with reinforcement-learning
• Quantum Machine Learning: from physics to software engineering
• Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network
• Decoding algorithms for surface codes
• Scalable Neural Decoder for Topological Surface Codes
• Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system
• Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks
• Reinforcement learning optimization of the charging of a Dicke quantum battery
• A scalable and fast artificial neural network syndrome decoder for surface codes
• Artificial Intelligence for Quantum Computing
• Quantum Error Correction with Quantum Autoencoders
• Pareto-optimal cycles for power, efficiency and fluctuations of quantum heat engines using reinforcement learning
• Expressive power of complex-valued restricted Boltzmann machines for solving non-stoquastic Hamiltonians
• Techniques for combining fast local decoders with global decoders under circuit-level noise
• Deep reinforcement learning for quantum Hamiltonian engineering
• Quantum variational learning for quantum error-correcting codes
• Simultaneous Discovery of Quantum Error Correction Codes and Encoders with a Noise-Aware Reinforcement Learning Agent
• Data-driven decoding of quantum error correcting codes using graph neural networks
• Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning
• Neural network decoder for near-term surface-code experiments
• Preparing Schrödinger cat states in a microwave cavity using a neural network
• Machine-learning based noise characterization and correction on neutral atoms NISQ devices
• Optimizing ZX-Diagrams with Deep Reinforcement Learning
• Exploring the Optimal Cycle for Quantum Heat Engine using Reinforcement Learning
• Error-rate-agnostic decoding of topological stabilizer codes
• Surface Code Design for Asymmetric Error Channels
• Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning
• Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
• Artificially intelligent Maxwell's demon for optimal control of open quantum systems
• A NEAT Quantum Error Decoder
• Enhancing Quantum Memory Lifetime with Measurement-Free Local Error Correction and Reinforcement Learning
• Quantum Compiling by Deep Reinforcement Learning
• Efficient Syndrome Decoder for Heavy Hexagonal QECC via Machine Learning
• Neural network enhanced cross entropy benchmark for monitored circuits
• The Quantum Cartpole: A benchmark environment for non-linear reinforcement learning
• Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation
• Neural Decoders for Universal Quantum Algorithms
• Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing