Natural variables for controlling quantum dynamics

The Surprising Ease of Finding Optimal Solutions for Controlling Nonlinear Phenomena in Quantum and Classical Complex Systems

This Perspective addresses the often observed surprising ease of achieving optimal control of nonlinear phenomena in quantum and classical complex systems. The circumstances involved are wide-ranging, with scenarios including manipulation of atomic scale processes, maximization of chemical and material properties or synthesis yields, Nature's optimization of species' populations by natural selection, and directed evolution. Natural evolution will mainly be discussed in terms of laboratory experiments with microorganisms, and the field is also distinct from the other domains where a scientist specifies the goal(s) and oversees the control process. We use the word "control" in reference to all of the available variables, regardless of the circumstance. The empirical observations on the ease of achieving at least good, if not excellent, control in diverse domains of science raise the question of why this occurs despite the generally inherent complexity of the systems in each scenario. The key to addressing the question lies in examining the associated control landscape, which is defined as the optimization objective as a function of the control variables that can be as diverse as the phenomena under consideration. Controls may range from laser pulses, chemical reagents, chemical processing conditions, out to nucleic acids in the genome and more. This Perspective presents a conjecture, based on present findings, that the systematics of readily finding good outcomes from controlled phenomena may be unified through consideration of control landscapes with the same common set of three underlying assumptions─the existence of an optimal solution, the ability for local movement on the landscape, and the availability of sufficient control resources─whose validity needs assessment in each scenario. In practice, many cases permit using myopic gradient-like algorithms while other circumstances utilize algorithms having some elements of stochasticity or introduced noise, depending on whether the landscape is locally smooth or rough. The overarching observation is that only relatively short searches are required despite the common high dimensionality of the available controls in typical scenarios.

One decade of quantum optimal control in the chopped random basis

The chopped random basis (CRAB) ansatz for quantum optimal control has been proven to be a versatile tool to enable quantum technology applications such as quantum computing, quantum simulation, quantum sensing, and quantum communication. Its capability to encompass experimental constraints-while maintaining an access to the usually trap-free control landscape-and to switch from open-loop to closed-loop optimization (including with remote access-or RedCRAB) is contributing to the development of quantum technology on many different physical platforms. In this review article we present the development, the theoretical basis and the toolbox for this optimization algorithm, as well as an overview of the broad range of different theoretical and experimental applications that exploit this powerful technique.

Constrained control landscape for population transfer in a two-level system

Controlling population transfer in a two-level quantum system reveals a landscape with a rich structure containing highly connected optimal regions.

Dynamic dimensionality identification for quantum control

The control of quantum systems with shaped laser pulses presents a paradox since the relative ease with which solutions are discovered appears incompatible with the enormous variety of pulse shapes accessible with a standard pulse shaper. Quantum landscape theory indicates that the relevant search dimensionality is not dictated by the number of pulse shaper elements, but rather is related to the number of states participating in the controlled dynamics. The actual dimensionality is encoded within the sensitivity of the observed yield to all of the pulse shaper elements. To investigate this proposition, the Hessian matrix is measured for controlled transitions amongst states of atomic rubidium, and its eigendecomposition reveals a dimensionality consistent with that predicted by landscape theory. Additionally, this methodology furnishes a low-dimensional picture that captures the essence of the light-matter interaction and the ensuing system dynamics.

Exploring constrained quantum control landscapes

The broad success of optimally controlling quantum systems with external fields has been attributed to the favorable topology of the underlying control landscape, where the landscape is the physical observable as a function of the controls. The control landscape can be shown to contain no suboptimal trapping extrema upon satisfaction of reasonable physical assumptions, but this topological analysis does not hold when significant constraints are placed on the control resources. This work employs simulations to explore the topology and features of the control landscape for pure-state population transfer with a constrained class of control fields. The fields are parameterized in terms of a set of uniformly spaced spectral frequencies, with the associated phases acting as the controls. This restricted family of fields provides a simple illustration for assessing the impact of constraints upon seeking optimal control. Optimization results reveal that the minimum number of phase controls necessary to assure a high yield in the target state has a special dependence on the number of accessible energy levels in the quantum system, revealed from an analysis of the first- and second-order variation of the yield with respect to the controls. When an insufficient number of controls and/or a weak control fluence are employed, trapping extrema and saddle points are observed on the landscape. When the control resources are sufficiently flexible, solutions producing the globally maximal yield are found to form connected "level sets" of continuously variable control fields that preserve the yield. These optimal yield level sets are found to shrink to isolated points on the top of the landscape as the control field fluence is decreased, and further reduction of the fluence turns these points into suboptimal trapping extrema on the landscape. Although constrained control fields can come in many forms beyond the cases explored here, the behavior found in this paper is illustrative of the impacts that constraints can introduce.

Optimal alignment control of a nonpolar molecule through nonresonant multiphoton transitions

Alignment control of an ensemble of nonpolar molecules is numerically studied by means of optimal control simulation. A nitrogen molecule that is modeled by a quantum rigid rotor is adopted. Controlled rotational wave packets are created through nonresonant optical transitions induced by polarizability coupling. Optimal pulses are designed to achieve the alignment control at a specified time in the absence/presence of external static fields in zero- and finite-temperature cases, as well as to maintain an aligned state. When maintaining an aligned state over a specified time interval is chosen as a target, the control mechanism is primarily attributed to a dynamical one. Multiple optimal solutions that lead to virtually the same control achievement are found, which are consistent with the topology of the quantum control landscape.