Reducing Memory Requirements of Quantum Optimal Control

• URL: http://arxiv.org/abs/2203.12717v1
• Date: Wed, 23 Mar 2022 20:42:54 GMT
• Title: Reducing Memory Requirements of Quantum Optimal Control
• Authors: Sri Hari Krishna Narayanan, Thomas Propson, Marcelo Bongarti, Jan Hueckelheim and Paul Hovland
• Abstract summary: gradient-based algorithms such as GRAPE suffer from exponential growth in storage with increasing number of qubits and linear growth in memory requirements with increasing number of time steps. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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