TY - GEN
T1 - Memory-Efficient Differentiable Programming for Quantum Optimal Control of Discrete Lattices
AU - Wang, Xian
AU - Kairys, Paul
AU - Narayanan, Sri Hari Krishna
AU - Huckelheim, Jan
AU - Hovland, Paul
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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 non-standard 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.
AB - 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 non-standard 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.
KW - Automatic Differentiation
KW - Lattice Gauge Theory
KW - Quantum Optimal Control
UR - http://www.scopus.com/inward/record.url?scp=85148584945&partnerID=8YFLogxK
U2 - 10.1109/QCS56647.2022.00016
DO - 10.1109/QCS56647.2022.00016
M3 - Conference contribution
AN - SCOPUS:85148584945
T3 - Proceedings of QCS 2022: 3rd International Workshop on Quantum Computing Software, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis
SP - 94
EP - 99
BT - Proceedings of QCS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE/ACM International Workshop on Quantum Computing Software, QCS 2022
Y2 - 13 November 2022 through 13 November 2022
ER -