Speedup for quantum optimal control from automatic differentiation based on graphics processing units
arXiv:1612.04929 · doi:10.1103/PhysRevA.95.042318
Abstract
We implement a quantum optimal control algorithm based on automatic differentiation and harness the acceleration afforded by graphics processing units (GPUs). Automatic differentiation allows us to specify advanced optimization criteria and incorporate them in the optimization process with ease. We show that the use of GPUs can speed up calculations by more than an order of magnitude. Our strategy facilitates efficient numerical simulations on affordable desktop computers, and exploration of a host of optimization constraints and system parameters relevant to real-life experiments. We demonstrate optimization of quantum evolution based on fine-grained evaluation of performance at each intermediate time step, thus enabling more intricate control on the evolution path, suppression of departures from the truncated model subspace, as well as minimization of the physical time needed to perform high-fidelity state preparation and unitary gates.
14 pages, 6 figures
References in corpus (14)
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Charge insensitive qubit design derived from the Cooper pair box
- QuTiP 2: A Python framework for the dynamics of open quantum systems
- Detecting arbitrary quantum errors via stabilizer measurements on a sublattice of the surface code
- Multi-GPU Accelerated Multi-Spin Monte Carlo Simulations of the 2D Ising Model
- Optimal control of entangling operations for trapped ion quantum computing
- Application of Optimal Control to CPMG Refocusing Pulse Design
- Protecting coherence in Optimal Control Theory: State dependent constraint approach
- Optimal control of a leaking qubit
- Montonic convergent optimal control theory to modulate bandwidth limited laser pulses in linear and non-linear optical processes
- Optimal control of time-dependent targets
- Optimizing for an arbitrary perfect entangler: I. Functionals
- Optimizing for an arbitrary perfect entangler. II. Application
- Implementation of Fault-tolerant Quantum Logic Gates via Optimal Control
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