Reducing Memory Requirements of Quantum Optimal Control

TL;DR

This paper introduces a novel automatic differentiation technique that reduces memory usage in quantum optimal control algorithms like GRAPE by exploiting properties of unitary matrices, enabling larger and longer simulations.

Contribution

The authors develop a nonstandard automatic differentiation method that significantly lowers memory requirements for quantum control algorithms, addressing a key scalability challenge.

Findings

Memory requirements are reduced by exploiting unitary matrix properties.

The approach enables handling larger quantum systems and longer control sequences.

Benchmark results demonstrate improved efficiency in JAX implementations.

Abstract

Quantum optimal control problems are typically solved by gradient-based algorithms such as GRAPE, which suffer from exponential growth in storage with increasing number of qubits and linear growth in memory requirements with increasing number of time steps. These memory requirements are a barrier for simulating large models or long time spans. We have created a nonstandard automatic differentiation technique that can compute gradients needed by GRAPE by exploiting the fact that the inverse of a unitary matrix is its conjugate transpose. Our approach significantly reduces the memory requirements for GRAPE, at the cost of a reasonable amount of recomputation. We present benchmark results based on an implementation in JAX.