Experimental Deep Reinforcement Learning for Error-Robust Gateset Design on a Superconducting Quantum Computer

Abstract

Quantum computers promise tremendous impact across applications -- and have shown great strides in hardware engineering -- but remain notoriously error prone. Careful design of low-level controls has been shown to compensate for the processes which induce hardware errors, leveraging techniques from optimal and robust control. However, these techniques rely heavily on the availability of highly accurate and detailed physical models which generally only achieve sufficient representative fidelity for the most simple operations and generic noise modes. In this work, we use deep reinforcement learning to design a universal set of error-robust quantum logic gates on a superconducting quantum computer, without requiring knowledge of a specific Hamiltonian model of the system, its controls, or its underlying error processes. We experimentally demonstrate that a fully autonomous deep reinforcement learning agent can design single qubit gates up to $3\times$ faster than default DRAG operations without additional leakage error, and exhibiting robustness against calibration drifts over weeks. We then show that $ZX(-\pi /2)$ operations implemented using the cross-resonance interaction can outperform hardware default gates by over $2\times$ and equivalently exhibit superior calibration-free performance up to 25 days post optimization using various metrics. We benchmark the performance of deep reinforcement learning derived gates against other black box optimization techniques, showing that deep reinforcement learning can achieve comparable or marginally superior performance, even with limited hardware access.

12 pages, 5 figures

References in corpus (21)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum computational advantage using photons
• Randomized Benchmarking of Quantum Gates
• Simple pulses for elimination of leakage in weakly nonlinear qubits
• Optimized Dynamical Decoupling in a Model Quantum Memory
• Fault-Tolerant Quantum Dynamical Decoupling
• Complete universal quantum gate set approaching fault-tolerant thresholds with superconducting qubits
• Coherent quantum LQG control
• Optimal Dynamical Decoherence Control of a Qubit
• Microwave-induced coupling of superconducting qubits
• Leakage reduction in fast superconducting qubit gates via optimal control
• Restoring Coherence Lost to a Slow Interacting Mesoscopic Bath
• Qiskit Pulse: Programming Quantum Computers Through the Cloud with Pulses
• Experimental noise filtering by quantum control
• First-principles analysis of cross-resonance gate operation
• Quantum Control of the Hyperfine Spin of a Cs Atom Ensemble
• Deep Reinforcement Learning for Quantum Gate Control
• Operation and intrinsic error budget of a two-qubit cross-resonance gate
• Integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits
• Quantum optimal control of multi-level dissipative quantum systems with Reinforcement Learning
• Error-robust quantum logic optimization using a cloud quantum computer interface

Cited by in corpus (31)

• Fast Universal Control of an Oscillator with Weak Dispersive Coupling to a Qubit
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Learning Quantum Systems
• Model-Free Quantum Control with Reinforcement Learning
• Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements
• Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system
• Approximate Autonomous Quantum Error Correction with Reinforcement Learning
• Deep reinforcement learning for quantum multiparameter estimation
• Gradient Ascent Pulse Engineering with Feedback
• Experimental benchmarking of an automated deterministic error suppression workflow for quantum algorithms
• Topological data analysis and machine learning
• Hidden Inverses: Coherent Error Cancellation at the Circuit Level
• Optimal control of families of quantum gates
• Pulse based Variational Quantum Optimal Control for hybrid quantum computing
• Optimizing quantum control pulses with complex constraints and few variables through Tensorflow
• Neural networks for on-the-fly single-shot state classification
• Numerical Gate Synthesis for Quantum Heuristics on Bosonic Quantum Processors
• Model predictive control for robust quantum state preparation
• Simulating neutrino oscillations on a superconducting qutrit
• Experimental graybox quantum system identification and control
• Reverse engineering of one-qubit filter functions with dynamical invariants
• Markovian Noise Modelling and Parameter Extraction Framework for Quantum Devices
• Multi-Axis Control of a Qubit in the Presence of Unknown Non-Markovian Quantum Noise
• Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe
• Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons
• Effect of Decoherence for Gate Operations on a Superconducting Bosonic Qubit
• Preparing Quantum States by Measurement-feedback Control with Bayesian Optimization
• Quantum compiling with a variational instruction set for accurate and fast quantum computing
• Anticipative measurements in hybrid quantum-classical computation
• Optimizing Quantum Variational Circuits with Deep Reinforcement Learning
• Parametric Amplification of an Optomechanical Quantum Interconnect