Optimal control of large quantum systems: assessing memory and runtime performance of GRAPE
arXiv:2304.06200 · doi:10.1088/2399-6528/ad22e5
Abstract
Gradient Ascent Pulse Engineering (GRAPE) is a popular technique in quantum optimal control, and can be combined with automatic differentiation (AD) to facilitate on-the-fly evaluation of cost-function gradients. We illustrate that the convenience of AD comes at a significant memory cost due to the cumulative storage of a large number of states and propagators. For quantum systems of increasing Hilbert space size, this imposes a significant bottleneck. We revisit the strategy of hard-coding gradients in a scheme that fully avoids propagator storage and significantly reduces memory requirements. Separately, we present improvements to numerical state propagation to enhance runtime performance. We benchmark runtime and memory usage and compare this approach to AD-based implementations, with a focus on pushing towards larger Hilbert space sizes. The results confirm that the AD-free approach facilitates the application of optimal control for large quantum systems which would otherwise be difficult to tackle.
arXiv comment14 pages, 6 figures, 1 table
References in corpus (29)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- Array Programming with NumPy
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Training Schrödinger's cat: quantum optimal control
- Quantum error correction in a solid-state hybrid spin register
- High fidelity spin entanglement using optimal control
- Quantum walks on a programmable two-dimensional 62-qubit superconducting processor
- Leakage reduction in fast superconducting qubit gates via optimal control
- Introduction to Quantum Optimal Control for Quantum Sensing with Nitrogen-Vacancy Centers in Diamond
- Protecting a Bosonic Qubit with Autonomous Quantum Error Correction
- High-performance Raman quantum memory with optimal control
- Speedup for quantum optimal control from automatic differentiation based on graphics processing units
- Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation
- Scqubits: a Python package for superconducting qubits
- Optimal control of entangling operations for trapped ion quantum computing
- Auxiliary matrix formalism for interaction representation transformations, optimal control and spin relaxation theories
- Fast closed-loop optimal control of ultracold atoms in an optical lattice
- QuanEstimation: An open-source toolkit for quantum parameter estimation
- Universal gates for protected superconducting qubits using optimal control
- Optimal control of fast and high-fidelity quantum gates with electron and nuclear spins of a nitrogen-vacancy center in diamond
- Optimal control of the transport of Bose-Einstein condensates with atom chips
- Optimal control of Bose-Einstein condensates in three dimensions
- Arbitrary quantum-state preparation of a harmonic oscillator via optimal control
- Optimal control of two qubits via a single cavity drive in circuit quantum electrodynamics
- Quantum Optimal Control via Semi-Automatic Differentiation
- Optimal control for fast and high-fidelity quantum gates in coupled superconducting flux qubits
- QEngine: An open-source C++ Library for Quantum Optimal Control of Ultracold Atoms
- Optimal control of a nitrogen-vacancy spin ensemble in diamond for sensing in the pulsed domain
- Optimal Phonon-to-Spin Mapping in a system of a trapped ion