The Quantum Jump Approach to Dissipative Dynamics in Quantum Optics
M. B. Plenio, P. L. Knight
Abstract
Dissipation, the irreversible loss of energy and coherence, from a microsystem, is the result of coupling to a much larger macrosystem (or reservoir) which is so large that one has no chance of keeping track of all of its degrees of freedom. The microsystem evolution is then described by tracing over the reservoir states, resulting in an irreversible decay as excitation leaks out of the initially excited microsystems into the outer reservoir environment. Earlier treatments of this dissipation described an ensemble of microsystems using density matrices, either in Schroedinger picture with Master equations, or in Heisenberg picture with Langevin equations. The development of experimental techniques to study single quantum systems (for example single trapped ions, or cavity radiation field modes) has stimulated the construction of theoretical methods to describe individual realizations conditioned on a particular observation record of the decay channel, in the environment. These methods, variously described as Quantum Jump, Monte Carlo Wavefunction and Quantum Trajectory methods are the subject of this review article. We discuss their derivation, apply them to a number of current problems in quantum optics and relate them to ensemble descriptions.
Create a lesson
Related papers
Using topology to reveal all mutually unbiased bases in dimension six
Mateo Cárdenes Wuttig, Joseph Tindall
Arithmetic Depth, Minimal Gate Alphabets, and Exact Program-State Resources for Single-Qubit Dyadic Programs
Tony Newton, Dan-Adrian German
Probing gravity at megahertz frequencies with levitated rotors
Matteo Fadel, Andrea Vinante
Optimal Holding Times for Entanglement Swapping
Van Sy Mai, Richard J. La, Abdella Battou
Quantum Algorithms for Molecular Response Functions: Resolvent Kernels, Time-Domain Sampling, Query Lower Bounds with Matched Single-Point Calibers, and a Six-Route Comparison
Yunlong Liu, Yujin Tang, Miao Xu et al.
GFlowQEC: Generative Flow Networks that Learn to Sample Quantum LDPC Codes
Nuno Batista, Gabriel Falcao, Penousal Machado