Optimal control gradient precision trade-offs: application to fast generation of DeepControl libraries for MRI

TL;DR

This paper investigates methods to accelerate fidelity gradient calculations in quantum optimal control, enabling faster generation of training data for deep learning-based MRI pulse design.

Contribution

It introduces and evaluates four gradient approximation techniques, highlighting a first-order truncation scheme that balances accuracy and computational speed.

Findings

First-order truncation is sufficiently accurate for MRI spin systems.

The first-order method is up to five times faster than exact gradients.

Faster gradient computation makes training data generation more feasible.

Abstract

We have recently demonstrated supervised deep learning methods for rapid generation of radiofrequency pulses in magnetic resonance imaging (https://doi.org/10.1002/mrm.27740, https://doi.org/10.1002/mrm.28667). Unlike the previous iterative optimization approaches, deep learning methods generate a pulse using a fixed number of floating-point operations - this is important in MRI, where patient-specific pulses preferably must be produced in real time. However, deep learning requires vast training libraries, which must be generated using the traditional methods, e.g. iterative quantum optimal control methods. Those methods are usually variations of gradient descent, and the calculation of the fidelity gradient of the performance metric with respect to the pulse waveform can be the most numerically intensive step. In this communication, we explore various ways in which the calculation of fidelity gradients in quantum optimal control theory may be accelerated. Four optimization avenues are explored: truncated commutator series expansions at zeroth and first order, a novel midpoint truncation scheme at first order, and the exact complex-step method. For the spin systems relevant to MRI, the first-order truncation is found to be sufficiently accurate, but also up to five times faster than the machine precision gradient. This makes the generation of training databases for the machine learning methods considerably more realistic.