Decoding Small Surface Codes with Feedforward Neural Networks

Abstract

Surface codes reach high error thresholds when decoded with known algorithms, but the decoding time will likely exceed the available time budget, especially for near-term implementations. To decrease the decoding time, we reduce the decoding problem to a classification problem that a feedforward neural network can solve. We investigate quantum error correction and fault tolerance at small code distances using neural network-based decoders, demonstrating that the neural network can generalize to inputs that were not provided during training and that they can reach similar or better decoding performance compared to previous algorithms. We conclude by discussing the time required by a feedforward neural network decoder in hardware.

References in corpus (27)

• Fault-tolerant quantum computation by anyons
• Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
• A variational eigenvalue solver on a quantum processor
• Quantum Mechanics helps in searching for a needle in a haystack
• Quantum Simulation
• Topological quantum memory
• Demonstration of Two-Qubit Algorithms with a Superconducting Quantum Processor
• Reliable Quantum Computers
• Simple pulses for elimination of leakage in weakly nonlinear qubits
• Stabilizer Codes and Quantum Error Correction
• Topological Quantum Distillation
• Realization of a scalable Shor algorithm
• High threshold universal quantum computation on the surface code
• Quantum codes on a lattice with boundary
• Low-distance Surface Codes under Realistic Quantum Noise
• Quantum LDPC codes with positive rate and minimum distance proportional to n^{1/2}
• Quantum Error Correction
• Scalable quantum circuit and control for a superconducting surface code
• A Neural Decoder for Topological Codes
• Optimal Resources for Topological 2D Stabilizer Codes: Comparative Study
• Arbitrarily Accurate Dynamical Control in Open Quantum Systems
• Optimal and Efficient Decoding of Concatenated Quantum Block Codes
• Density-matrix simulation of small surface codes under current and projected experimental noise
• Universal topological phase of 2D stabilizer codes
• Fault-tolerant quantum computing with color codes
• Fault-Tolerant Quantum Computing in the Pauli or Clifford Frame with Slow Error Diagnostics
• RNN Decoding of Linear Block Codes

Cited by in corpus (60)

• Machine learning and the physical sciences
• Interfacing spin qubits in quantum dots and donors - hot, dense and coherent
• Blueprint for a Scalable Photonic Fault-Tolerant Quantum Computer
• Almost-linear time decoding algorithm for topological codes
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Optimizing Quantum Error Correction Codes with Reinforcement Learning
• Machine-learning-assisted correction of correlated qubit errors in a topological code
• Deep Neural Network Probabilistic Decoder for Stabilizer Codes
• Neural Belief-Propagation Decoders for Quantum Error-Correcting Codes
• Quantum error correction for the toric code using deep reinforcement learning
• Deep neural decoders for near term fault-tolerant experiments
• Advantages of versatile neural-network decoding for topological codes
• Neural-Network Decoders for Quantum Error Correction using Surface Codes:A Space Exploration of the Hardware Cost-Performance Trade-Offs
• Comparing neural network based decoders for the surface code
• Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation
• Neural network decoder for topological color codes with circuit level noise
• Analysing correlated noise on the surface code using adaptive decoding algorithms
• Fault-Tolerant Weighted Union-Find Decoding on the Toric Code
• Scalable Neural Decoder for Topological Surface Codes
• Decoding algorithms for surface codes
• Scalable Neural Network Decoders for Higher Dimensional Quantum Codes
• Machine learning \& artificial intelligence in the quantum domain
• A scalable and fast artificial neural network syndrome decoder for surface codes
• Reinforcement learning for optimal error correction of toric codes
• Neural Network Decoders for Large-Distance 2D Toric Codes
• Fault-tolerant Quantum Error Correction on Near-term Quantum Processors using Flag and Bridge Qubits
• A local pre-decoder to reduce the bandwidth and latency of quantum error correction
• Deep Q-learning decoder for depolarizing noise on the toric code
• General framework for constructing fast and near-optimal machine-learning-based decoder of the topological stabilizer codes
• Recent Advances in Deep Learning for Channel Coding: A Survey
• Techniques for combining fast local decoders with global decoders under circuit-level noise
• General tensor network decoding of 2D Pauli codes
• Neural-network approach for identifying nonclassicality from click-counting data
• Performance enhancement of surface codes via recursive MWPM decoding
• Symmetries for a High Level Neural Decoder on the Toric Code
• Determination of the Semion Code Threshold using Neural Decoders
• Decoding of Quantum Data-Syndrome Codes via Belief Propagation
• Convolutional neural network based decoders for surface codes
• A Scalable Decoder Micro-architecture for Fault-Tolerant Quantum Computing
• Data-driven decoding of quantum error correcting codes using graph neural networks
• Machine Learning based Laser Failure Mode Detection
• Neural network decoder for near-term surface-code experiments
• Non-Markovian feedback for optimized quantum error correction
• Neural networks in quantum many-body physics: a hands-on tutorial
• Artificial Neural Network Syndrome Decoding on IBM Quantum Processors
• Error-rate-agnostic decoding of topological stabilizer codes
• Surface Code Design for Asymmetric Error Channels
• Decoherence and Quantum Error Correction for Quantum Computing and Communications
• On the Efficacy of Surface Codes in Compensating for Radiation Events in Superconducting Devices
• Quantum noise modeling through Reinforcement Learning
• Introduction to Quantum Error Correction with Stabilizer Codes
• Efficient Syndrome Decoder for Heavy Hexagonal QECC via Machine Learning
• Efficient Decoding of Surface Code Syndromes for Error Correction in Quantum Computing
• Mapping Surface Code to Superconducting Quantum Processors
• Lattice Surgery for Dummies
• Managing Classical Processing Requirements for Quantum Error Correction
• On the interpretability of neural network decoders
• Neural Decoders for Universal Quantum Algorithms
• Subsystem many-hypercube codes: High-rate concatenated codes with low-weight syndrome measurements
• Overflow-Safe Polylog-Time Parallel Minimum-Weight Perfect Matching Decoder: Toward Experimental Demonstration