Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Physica A Statistical Mechanics and its Applications

Performance comparison between classical and quantum control for a simple quantum system

Zairong Xi, Guang-Sheng Jin

Published Oct 29, 2007• 5 citations
Publisher
DOI
10.1016/j.physa.2007.10.019

Related papers

Performance Comparison Between Classical and Quantum Control for a Simple Quantum System

Zairong Xi

Brańczyk et. al pointed out that the quantum control scheme is superior to the classical control scheme for a simple quantum system using simulation [1]. Here we strictly prove the result from mathematical view.

Chinese Control Conference (CCC)•Jul 1, 2006•0 citations

Classical Control of a Quantum System

Urmila Mahadev

•Jul 1, 2026•0 citations

Comparison of SNR between Classical and Quantum Imager

Charles Kim, Gary S. Kanner

Quantum imagers have been demonstrated in the laboratory by several groups. However, there are many practical concerns that must be considered in order to make such a system as successful as classical imagers in field applications. Consequently, we develop a model for signal-to-noise ratio (SNR) to estimate the performance of a quantum imager in comparison with that of the classical case. We assume simple architectures for both systems with components in the two as common to each other as possible. Comparisons between the imagers are made under conditions of solar background for ranges up to 2 km. The performance of quantum imager is shown to be superior to that of the classical case under conditions of narrow joint (or coincidence) detection windows and very strong pumping of the spontaneous parametric downconverter illumination source, for which the degree of photon entanglement may be severely degraded.

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE•Sep 5, 2008•1 citation

Feedback control of quantum system

Daoyi Dong, Zonghai Chen, Chenbin Zhang +1 more

物理学前沿：英文版•Aug 9, 2006•0 citations

FEEDBACK CONTROL OF QUANTUM SYSTEMS

M. R. James

Quantum Stochastics and Information•Aug 1, 2008•4 citations

Study of Optimization Problems by Quantum Annealing

Tadashi Kadowaki

We introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. The idea is tested by the two models, the transverse Ising model and the traveling salesman problem (TSP). Adding the transverse field to the Ising model is a simple way to introduce quantum fluctuations. The strength of the transverse field is controlled as a function of time similarly to the temperature in the conventional method. TSP can be described by the Ising spin, so that we also apply the same technique to TSP. We directory solve the time-dependent Schrödinger equation for small-size systems and perform the quantum Monte Carlo simulation for large-size systems. Comparison with the results of the corresponding classical (thermal) method reveals that the quantum method leads to the ground state with much larger probability in almost all cases if we use the same annealing schedule of the control parameters. We also find that the relaxation time is quite short for quantum systems by numerical simulations. We consider this is one of the reasons why the annealing in quantum systems have a better performance of finding the optimal state in comparison with classical systems.

arXiv:quant-ph/0205020•May 5, 2002•39 citations
Quantum imagers have been demonstrated in the laboratory by several groups. However, there are many practical concerns that must be considered in order to make such a system as successful as classical imagers in field applications. Consequently, we develop a model for signal-to-noise ratio (SNR) to estimate the performance of a quantum imager in comparison with that of the classical case. We assume simple architectures for both systems with components in the two as common to each other as possible. Comparisons between the imagers are made under conditions of solar background for ranges up to 2 km. The performance of quantum imager is shown to be superior to that of the classical case under conditions of narrow joint (or coincidence) detection windows and very strong pumping of the spontaneous parametric downconverter illumination source, for which the degree of photon entanglement may be severely degraded.
We introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. The idea is tested by the two models, the transverse Ising model and the traveling salesman problem (TSP). Adding the transverse field to the Ising model is a simple way to introduce quantum fluctuations. The strength of the transverse field is controlled as a function of time similarly to the temperature in the conventional method. TSP can be described by the Ising spin, so that we also apply the same technique to TSP. We directory solve the time-dependent Schrödinger equation for small-size systems and perform the quantum Monte Carlo simulation for large-size systems. Comparison with the results of the corresponding classical (thermal) method reveals that the quantum method leads to the ground state with much larger probability in almost all cases if we use the same annealing schedule of the control parameters. We also find that the relaxation time is quite short for quantum systems by numerical simulations. We consider this is one of the reasons why the annealing in quantum systems have a better performance of finding the optimal state in comparison with classical systems.