Exploring Quantum Control Landscapes: Topology, Features, and Optimization Scaling

TL;DR

This study systematically investigates quantum control landscapes across systems with 5 to 100 states, confirming that the effort to find optimal controls remains invariant with system size under ideal conditions.

Contribution

It provides extensive simulation evidence supporting the theoretical prediction that quantum control landscape optimization effort is independent of system complexity.

Findings

Optimization effort remains invariant with system size N.

Control landscapes are free of sub-optimal traps under ideal conditions.

Scaling behavior can be affected by initial control fluence and target state receding.

Abstract

Quantum optimal control experiments and simulations have successfully manipulated the dynamics of systems ranging from atoms to biomolecules. Surprisingly, these collective works indicate that the effort (i.e., the number of algorithmic iterations) required to find an optimal control field appears to be essentially invariant to the complexity of the system. The present work explores this matter in a series of systematic optimizations of the state-to-state transition probability on model quantum systems with the number of states $N$ ranging from 5 through 100. The optimizations occur over a landscape defined by the transition probability as a function of the control field. Previous theoretical studies on the topology of quantum control landscapes established that they should be free of sub-optimal traps under reasonable physical conditions. The simulations in this work include nearly 5000 individual optimization test cases, all of which confirm this prediction by fully achieving optimal population transfer of at least 99.9% upon careful attention to numerical procedures to ensure that the controls are free of constraints. Collectively, the simulation results additionally show invariance of required search effort to system dimension $N$. This behavior is rationalized in terms of the structural features of the underlying control landscape. The very attractive observed scaling with system complexity may be understood by considering the distance traveled on the control landscape during a search and the magnitude of the control landscape slope. Exceptions to this favorable scaling behavior can arise when the initial control field fluence is too large or when the target final state recedes from the initial state as $N$ increases.