Memory-Efficient Differentiable Programming for Quantum Optimal Control
of Discrete Lattices
- URL: http://arxiv.org/abs/2210.08378v1
- Date: Sat, 15 Oct 2022 20:59:23 GMT
- Title: Memory-Efficient Differentiable Programming for Quantum Optimal Control
of Discrete Lattices
- Authors: Xian Wang, Paul Kairys, Sri Hari Krishna Narayanan, Jan H\"uckelheim,
Paul Hovland
- Abstract summary: Quantum optimal control problems are typically solved by gradient-based algorithms such as GRAPE.
QOC reveals that memory requirements are a barrier for simulating large models or long time spans.
We employ a nonstandard differentiable programming approach that significantly reduces the memory requirements at the cost of a reasonable amount of recomputation.
- Score: 1.5012666537539614
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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. Employing QOC for discrete lattices reveals
that these memory requirements are a barrier for simulating large models or
long time spans. We employ a nonstandard differentiable programming approach
that significantly reduces the memory requirements at the cost of a reasonable
amount of recomputation. The approach exploits invertibility properties of the
unitary matrices to reverse the computation during back-propagation. We utilize
QOC software written in the differentiable programming framework JAX that
implements this approach, and demonstrate its effectiveness for lattice gauge
theory.
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