Reducing Memory Requirements of Quantum Optimal Control

Understand

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.