Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

Abstract

Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes a novel approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of less than 30 minutes, and sometimes as little as 1 minute. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices.

References in corpus (40)

• Quantum Computation with Quantum Dots
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Dueling Network Architectures for Deep Reinforcement Learning
• Coherent control of a single electron spin with electric fields
• An addressable quantum dot qubit with fault-tolerant control fidelity
• A Two Qubit Logic Gate in Silicon
• Quantum CNOT Gate for Spins in Silicon
• Fidelity benchmarks for two-qubit gates in silicon
• Efficient Representation of Quantum Many-body States with Deep Neural Networks
• Coherent spin manipulation in an exchange-only qubit
• Self-Consistent Measurement and State Tomography of an Exchange-Only Spin Qubit
• A singlet triplet hole spin qubit in planar Ge
• Deep Reinforcement Learning for Quantum Gate Control
• Loading a quantum-dot based "Qubyte" register
• Machine Learning techniques for state recognition and auto-tuning in quantum dots
• Notch filtering the nuclear environment of a spin qubit
• Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics
• Generalizable control for quantum parameter estimation through reinforcement learning
• Coherent Transport of Quantum States by Deep Reinforcement Learning
• Machine learning enables completely automatic tuning of a quantum device faster than human experts
• Closed-loop control of a GaAs-based singlet-triplet spin qubit with 99.5% gate fidelity and low leakage
• Auto-tuning of double dot devices in situ with machine learning
• Machine Learning Bell Nonlocality in Quantum Many-body Systems
• Automated tuning of double quantum dots into specific charge states using neural networks
• Model-Based Reinforcement Learning with Value-Targeted Regression
• Hyperfine-phonon spin relaxation in a single-electron GaAs quantum dot
• Sensitive radio-frequency measurements of a quantum dot by tuning to perfect impedance matching
• Computer-automated tuning of semiconductor double quantum dots into the single-electron regime
• Efficiently measuring a quantum device using machine learning
• Improving the dynamics of quantum sensors with reinforcement learning
• Autonomous tuning and charge state detection of gate defined quantum dots
• Radio frequency charge sensing in InAs nanowire double quantum dots
• A Machine Learning Approach for Automated Fine-Tuning of Semiconductor Spin Qubits
• QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
• Rapid detection of coherent tunneling in an InAs nanowire quantum dot through dispersive gate sensing
• Quantum device fine-tuning using unsupervised embedding learning
• Radio-frequency reflectometry of a quantum dot using an ultra-low-noise SQUID amplifier
• Level spectrum and charge relaxation in a silicon double quantum dot probed by dual-gate reflectometry
• Spectroscopy of Quantum-Dot Orbitals with In-Plane Magnetic Fields
• Deep reinforcement learning for robust quantum optimization

Cited by in corpus (19)

• Learning Quantum Systems
• Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements
• Precise atom manipulation through deep reinforcement learning
• Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system
• Optimal control of quantum thermal machines using machine learning
• Artificial Intelligence for Quantum Computing
• Toward Robust Autotuning of Noisy Quantum Dot Devices
• Robust and fast post-processing of single-shot spin qubit detection events with a neural network
• Identifying Pauli spin blockade using deep learning
• Machine Learning for Estimation and Control of Quantum Systems
• Fully autonomous tuning of a spin qubit
• Applications of model-aware reinforcement learning in Bayesian quantum metrology
• Visual explanations of machine learning model estimating charge states in quantum dots
• Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
• On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots
• Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams
• QDsim: A user-friendly toolbox for simulating large-scale quantum dot devices
• Automated in situ optimization and disorder mitigation in a quantum device
• Learning Coulomb Diamonds in Large Quantum Dot Arrays