Optimal control of families of quantum gates

Abstract

Quantum Optimal Control (QOC) enables the realization of accurate operations, such as quantum gates, and support the development of quantum technologies. To date, many QOC frameworks have been developed but those remain only naturally suited to optimize a single targeted operation at a time. We extend this concept to optimal control with a continuous family of targets, and demonstrate that an optimization based on neural networks can find families of time-dependent Hamiltonians that realize desired classes of quantum gates in minimal time.

References in corpus (8)

• Hyperparameter Search in Machine Learning
• High-Fidelity Single-Shot Toffoli Gate via Quantum Control
• Experimental Deep Reinforcement Learning for Error-Robust Gateset Design on a Superconducting Quantum Computer
• Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
• Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements
• Exploiting Landscape Geometry to Enhance Quantum Optimal Control
• Control of Stochastic Quantum Dynamics by Differentiable Programming
• Efficient Assessment of Process Fidelity

Cited by in corpus (10)

• Lie-algebraic classical simulations for quantum computing
• Preparing Schrödinger cat states in a microwave cavity using a neural network
• Efficient control pulses for continuous quantum gate families through coordinated re-optimization
• Pulse family optimization for parametrized quantum gates using spectral clustering
• Parametrized multiqubit gate design for neutral-atom based quantum platforms
• Differentiable master equation solver for quantum device characterisation
• Geodesic Algorithm for Unitary Gate Design with Time-Independent Hamiltonians
• Low-rank optimal control of quantum devices
• Autonomously Designed Pulses for Precise, Site-Selective Control of Atomic Qubits
• Using optimal control to guide neural-network interpolation of continuously-parameterized gates