A Projection Operator-based Newton Method for the Trajectory Optimization of Closed Quantum Systems

• URL: http://arxiv.org/abs/2111.08795v3
• Date: Wed, 26 Oct 2022 02:22:13 GMT
• Title: A Projection Operator-based Newton Method for the Trajectory Optimization of Closed Quantum Systems
• Authors: Jieqiu Shao, Joshua Combes, John Hauser and Marco M. Nicotra
• Abstract summary: This paper develops a new general purpose solver for quantum optimal control based on the PRojection Operator Newton method for Trajectory Optimization, or PRONTO. Specifically, the proposed approach uses a projection operator to incorporate the Schr"odinger equation directly into the cost function, which is then minimized using a quasi-Newton method. The resulting method guarantees monotonic convergence at every iteration and quadratic convergence in proximity of the solution.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum optimal control is an important technology that enables fast state preparation and gate design. In the absence of an analytic solution, most quantum optimal control methods rely on an iterative scheme to update the solution estimate. At present, the convergence rate of existing solvers is at most superlinear. This paper develops a new general purpose solver for quantum optimal control based on the PRojection Operator Newton method for Trajectory Optimization, or PRONTO. Specifically, the proposed approach uses a projection operator to incorporate the Schr\"odinger equation directly into the cost function, which is then minimized using a quasi-Newton method. At each iteration, the descent direction is obtained by computing the analytic solution to a Linear-Quadratic trajectory optimization problem. The resulting method guarantees monotonic convergence at every iteration and quadratic convergence in proximity of the solution. To highlight the potential of PRONTO, we present an numerical example that employs it to solve the optimal state-to-state mapping problem for a qubit and compares its performance to a state-of-the-art quadratic optimal control method.

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