The dynamics induced while controlling quantum systems by optimally shaped laser pulses have often been difficult to understand in detail. A method is presented for quantifying the importance of specific sequences of quantum transitions involved in the control process. The method is based on a ``beable'' formulation of quantum mechanics due to John Bell that rigorously maps the quantum evolution onto an ensemble of stochastic trajectories over a classical state space. Detailed mechanism identification is illustrated with a model 7-level system. A general procedure is presented to extract mechanism information directly from closed-loop control experiments. Application to simulated experimental data for the model system proves robust with up ...
Starting from an initial pure quantum state, we present a strategy for reaching a target state corre...
Advances in quantum control theory and optimization techniques have made possible a new phenomena of...
Scientific machine learning denotes the integration of machine learning into traditional scientific ...
This paper explores the utility of instantaneous and continuous observations in the optimal control ...
Tunable lasers, sources of intense narrow-band coherent radiation, enabled the parity violation expe...
We look at time evolution of a physical system from the point of view of dynamical control theory. N...
A central feature of quantum mechanics is that a measurement result is intrinsically probabilistic. ...
Quantum feedback can stabilize a two-level atom against decoherence (spontaneous emission), putting ...
In this article we reconsider a version of quantum trajectory theory based on the stochastic Schröd...
http://www-direction.inria.fr/international/arima/009/00920.htmlInternational audienceThis paper des...
We present a protocol for deterministic generation of Fock states and Schroedinger cat-like states i...
This chapter presents an overview of the current state of attempts to control quantum phenomena. The...
Understanding how to tailor quantum dynamics to achieve a desired evolution is a crucial problemin a...
$^{a}$T. Baumert et al, Appl. Phys. B 65, 779 (1997) $^{b}$M. Bergt et al, Science 282, 919 (1998)Au...
A non-equilibrium, generally time-dependent, environment whose form is deduced by optimal learning c...
Starting from an initial pure quantum state, we present a strategy for reaching a target state corre...
Advances in quantum control theory and optimization techniques have made possible a new phenomena of...
Scientific machine learning denotes the integration of machine learning into traditional scientific ...
This paper explores the utility of instantaneous and continuous observations in the optimal control ...
Tunable lasers, sources of intense narrow-band coherent radiation, enabled the parity violation expe...
We look at time evolution of a physical system from the point of view of dynamical control theory. N...
A central feature of quantum mechanics is that a measurement result is intrinsically probabilistic. ...
Quantum feedback can stabilize a two-level atom against decoherence (spontaneous emission), putting ...
In this article we reconsider a version of quantum trajectory theory based on the stochastic Schröd...
http://www-direction.inria.fr/international/arima/009/00920.htmlInternational audienceThis paper des...
We present a protocol for deterministic generation of Fock states and Schroedinger cat-like states i...
This chapter presents an overview of the current state of attempts to control quantum phenomena. The...
Understanding how to tailor quantum dynamics to achieve a desired evolution is a crucial problemin a...
$^{a}$T. Baumert et al, Appl. Phys. B 65, 779 (1997) $^{b}$M. Bergt et al, Science 282, 919 (1998)Au...
A non-equilibrium, generally time-dependent, environment whose form is deduced by optimal learning c...
Starting from an initial pure quantum state, we present a strategy for reaching a target state corre...
Advances in quantum control theory and optimization techniques have made possible a new phenomena of...
Scientific machine learning denotes the integration of machine learning into traditional scientific ...