Results 41 to 50 of about 17,439,974 (328)
When does reinforcement learning stand out in quantum control? A comparative study on state preparation [PDF]
npj Quantum Information, 2019 Reinforcement learning has been widely used in many problems, including quantum control of qubits. However, such problems can, at the same time, be solved by traditional, non-machine-learning methods, such as stochastic gradient descent and Krotov ...
Xiao-Ming Zhang +4 more
semanticscholar +1 more source
Quantum Control with Quantum Light of Molecular Nonadiabaticity [PDF]
, 2019 Coherent control experiments in molecules are often done with shaped laser fields. The electric field is described classically and control over the time evolution of the system is achieved by shaping the laser pulses in the time or frequency domain ...
Csehi, András +3 more
core +2 more sources
Quantum multiobservable control
Physical Review A, 2008 We present deterministic algorithms for the simultaneous control of an arbitrary number of quantum observables. Unlike optimal control approaches based on cost function optimization, quantum multiobservable tracking control (MOTC) is capable of tracking predetermined homotopic trajectories to target expectation values in the space of multiobservables ...
Chakrabarti, Raj +2 more
openaire +2 more sources
Breaking adiabatic quantum control with deep learning [PDF]
, 2020 In the era of digital quantum computing, optimal digitized pulses are requisite for efficient quantum control. This goal is translated into dynamic programming, in which a deep reinforcement learning (DRL) agent is gifted.
Yongcheng Ding +5 more
semanticscholar +1 more source
Quantum control and the Strocchi map [PDF]
, 2002 Identifying the real and imaginary parts of wave functions with coordinates and momenta, quantum evolution may be mapped onto a classical Hamiltonian system.
A. Heslot +22 more
core +3 more sources
QNMs of slowly rotating Einstein–Bumblebee black hole
European Physical Journal C: Particles and Fields, 2023 We have studied the quasinormal modes (QNMs) of a slowly rotating black hole with Lorentz-violating parameter in Einstein–Bumblebee gravity. We analyse the slow rotation approximation of the rotating black hole in the Einstein–Bumblebee gravity, and ...
Wentao Liu +3 more
doaj +1 more source
Universal quantum control through deep reinforcement learning [PDF]
npj Quantum Information, 2018 Emerging reinforcement learning techniques using deep neural networks have shown great promise in control optimization. They harness non-local regularities of noisy control trajectories and facilitate transfer learning between tasks.
M. Niu +3 more
semanticscholar +1 more source
Nanoscale Research Letters, 2019 Converting solar energy into sustainable hydrogen fuel by photoelectrochemical (PEC) water splitting is a promising technology to solve increasingly serious global energy supply and environmental issues.
Zhou Cao +9 more
doaj +1 more source
European Physical Journal C: Particles and Fields, 2023 We investigate the motion of a test scalar particle coupling to the Chern–Simons (CS) invariant in the background of a stationary axisymmetric black hole in the Einstein–Maxwell–Dilaton–Axion (EMDA) gravity.
Lina Zhang +3 more
doaj +1 more source
Noncommutative optimal control and quantum networks [PDF]
, 2006 Optimal control is formulated based on a noncommutative calculus of operator derivatives. The use of optimal control methods in the design of quantum systems relies on the differentiation of an operator-valued function with respect to the relevant ...
Yanagisawa, Masahiro
core +1 more source