Ilia A. Luchnikov, Alexander Ryzhov, Sergey N. Filippov, Henni Ouerdane
SciPost Phys. 10, 079 (2021) ·
published 30 March 2021
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· pdf
Many theoretical problems in quantum technology can be formulated and
addressed as constrained optimization problems. The most common quantum
mechanical constraints such as, e.g., orthogonality of isometric and unitary
matrices, CPTP property of quantum channels, and conditions on density
matrices, can be seen as quotient or embedded Riemannian manifolds. This allows
to use Riemannian optimization techniques for solving quantum-mechanical
constrained optimization problems. In the present work, we introduce QGOpt, the
library for constrained optimization in quantum technology. QGOpt relies on the
underlying Riemannian structure of quantum-mechanical constraints and permits
application of standard gradient based optimization methods while preserving
quantum mechanical constraints. Moreover, QGOpt is written on top of
TensorFlow, which enables automatic differentiation to calculate necessary
gradients for optimization. We show two application examples: quantum gate
decomposition and quantum tomography.
