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arXiv:1508.05434 · Physical Review Letters

Are there Traps in Quantum Control Landscapes?

Alexander Nikolaevich Pechen, David J. Tannor

Published Mar 22, 2011• 107 citations
arXivHTMLPDF

Abstract

There has been great interest in recent years in quantum control landscapes. Given an objective $J$ that depends on a control field $\ensuremath{\epsilon}$ the dynamical landscape is defined by the properties of the Hessian ${\ensuremath{\delta}}^{2}J/\ensuremath{\delta}{\ensuremath{\epsilon}}^{2}$ at the critical points $\ensuremath{\delta}J/\ensuremath{\delta}\ensuremath{\epsilon}=0$. We show that contrary to recent claims in the literature the dynamical control landscape can exhibit trapping behavior due to the existence of special critical points and illustrate this finding with an example of a 3-level $\ensuremath{\Lambda}$ system. This observation can have profound implications for both theoretical and experimental quantum control studies.

DOI
10.1103/physrevlett.106.120402

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There has been great interest in recent years in quantum control landscapes. Given an objective JJJ that depends on a control field \ensuremathϵ\ensuremath{\epsilon}\ensuremathϵ the dynamical landscape is defined by the properties of the Hessian \ensuremathδ2J/\ensuremathδ\ensuremathϵ2{\ensuremath{\delta}}^{2}J/\ensuremath{\delta}{\ensuremath{\epsilon}}^{2}\ensuremathδ2J/\ensuremathδ\ensuremathϵ2 at the critical points \ensuremathδJ/\ensuremathδ\ensuremathϵ=0\ensuremath{\delta}J/\ensuremath{\delta}\ensuremath{\epsilon}=0\ensuremathδJ/\ensuremathδ\ensuremathϵ=0. We show that contrary to recent claims in the literature the dynamical control landscape can exhibit trapping behavior due to the existence of special critical points and illustrate this finding with an example of a 3-level \ensuremathΛ\ensuremath{\Lambda}\ensuremathΛ system. This observation can have profound implications for both theoretical and experimental quantum control studies.
Many quantum control problems are formulated as a search for an optimal field that maximizes a physical objective. This search is performed over a landscape defined as the objective as a function of the control field. A recent Letter [A. N. Pechen and D. J. Tannor, Phys. Rev. Lett. 106, 120402 (2011)] asserts that the existence of special landscape critical points (CPs) with trapping characteristics is "contrary to recent claims in the literature" and "can have profound implications for both theoretical and experimental quantum control studies." We show here that these assertions are inaccurate and misleading.
Quantum control landscape theory was formulated to assess the ease of finding optimal control fields in simulations and in the laboratory. The landscape is the observable as a function of the controls, and a primary goal of the theory is the analysis of landscape features. In what is referred to as the kinematic picture of the landscape, prior work showed that the landscapes are generally free of traps that could halt the search for an optimal control at a suboptimal observable value. The present paper considers the dynamical picture of the landscape, seeking the existence of singular controls, especially of a nonkinematic nature along with an assessment of whether they correspond to traps. We analyze the necessary and sufficient conditions for singular controls to be kinematic or nonkinematic critical solutions and the likelihood of their being encountered while maximizing an observable. An algorithm is introduced to seek singular controls on the landscape in simulations along with an associated Hessian landscape analysis. Simulations are performed for a large number of model finite-level quantum systems, showing that all the numerically identified kinematic and nonkinematic singular critical controls are not traps, in support of the prior empirical observations on the ease of finding high-quality optimal control fields.
Seeking an effective quantum control entails searching over a landscape defined as the objective as a functional of the control field. This paper considers the problem of driving a state-to-state transition in a finite level quantum system, and analyzes the local topology of the landscape of the final transition probability in terms of the variables specifying the control field. Numerical calculation of the eigenvalues of the Hessian of the transition probability with respect to the control field variables reveals systematic structure in the spectra reflecting the existence of a generic and simple control landscape topology. An illustration shows that the number of nonzero Hessian eigenvalues is determined by the number of quantum states in the system. The Hessian eigenvectors associated with its nonzero eigenvalues are shown to give insight into the cooperative roles of the control variables. The practical consequences of these findings for quantum control are discussed.
Abstract Quantum control is a necessary tool for a variety of modern quantum technologies as it allows to optimally manipulate quantum systems for various tasks. Traps are points of local but not global optimum of the objective functional for a given quantum control problem. In a more general sense, traps are critical points of the objective functional which are hard to escape by local search algorithms. Here a review of some results of the analysis of possibility of having traps in landscapes of coherently controlled closed quantum systems is given. In one-qubit case, there are no traps. For special multilevel quantum systems, higher-order traps may appear.
We investigate the control landscapes of closed, finite level quantum systems beyond the dipole approximation by including a polarizability term in the Hamiltonian. Theoretical analysis is presented for the nnn level case and formulas for singular controls, which are candidates for landscape traps, are compared to their analogues in the dipole approximation. A numerical analysis of the existence of traps in control landscapes beyond the dipole approximation is made in the four level case. A numerical exploration of these control landscapes is achieved by generating many random Hamiltonians which include a term quadratic in a single control field. The landscapes of such systems are found numerically to be trap free in general. This extends a great body of recent work on typical landscapes of quantum systems where the dipole approximation is made. We further investigate the relationship between the magnitude of the polarizability and the magnitude of the controls resulting from optimization. It is shown numerically that including a polarizability term in an otherwise uncontrollable system removes traps from the landscapes of a specific family of systems by restoring controllability. We numerically assess the effect of a random polarizability term on the know example of a three level system with a second order trap in its control landscape. It is found that the addition of polarizability removes the trap from the landscape. The implications for laboratory control are discussed.
Quantum optimal control has enjoyed wide success for a variety of theoretical and experimental objectives. These favorable results have been attributed to advantageous properties of the corresponding control landscapes, which are free from local optima if three conditions are met: (1) the quantum system is controllable, (2) the Jacobian of the map from the control field to the evolution operator is full rank, and (3) the control field is not constrained. This paper explores how gradient searches for globally optimal control fields are affected by deviations from assumption (2). In some quantum control problems, so-called singular critical points, at which the Jacobian is rank deficient, may exist on the landscape. Using optimal control simulations, we show that search failure is only observed when a singular critical point is also a second-order trap, which occurs if the control problem meets additional conditions involving the system Hamiltonian and/or the control objective. All known second-order traps occur at constant control fields, and we also show that they only affect searches that originate very close to them. As a result, even when such traps exist on the control landscape, they are unlikely to affect well-designed gradient optimizations under realistic searching conditions.