Amazon.co.jp: Quantum Annealing and Related Optimization Methods (Lecture Notes in Physics, 679): Das, Arnab, Chakrabarti, Bikas K.: 本
Quantum Annealing for Clustering
This paper studies quantum annealing (QA) for clustering, which can be seen as an extension of simulated annealing (SA). We derive a QA algorithm for clustering and propose an annealing schedule, which is crucial in practice. Experiments show the proposed QA algorithm finds better clustering assignments than SA. Furthermore, QA is as easy as SA to implement.
Quantum Simulated Annealing - barnum.pdf
Quantum Simulated Annealing Howard Barnum1 (presenting) & Rolando Somma2 Sergio Boixo1,3 Manny Knill 4 1Los Alamos National Laboratory 2Perimeter Institute 3University of New Mexico 4National Institute of Standards and Technology (Boulder) March 28, 2008 / Classical and Quantum Information Theory, Santa Fe, NM Introduction and outline Motivation Markov chain Monte-Carlo (MCMC) algorithms, such as
Quantum annealing - Wikipedia
This article may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts, without removing the technical details. (January 2022) (Learn how and when to remove this message) Quantum annealing (QA) is an optimization process for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states),
Quantum annealing in the transverse Ising model
We introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. Quantum fluctuations cause transitions between states and thus play the same role as thermal fluctuations in the conventional approach. The idea is tested by the transverse Ising model, in which the transverse field is a function of time similar to the
Experimental signature of programmable quantum annealing
Quantum annealing is a general strategy for solving difficult optimization problems with the aid of quantum adiabatic evolution. Both analytical and numerical evidence suggests that under idealized, closed system conditions, quantum annealing can outperform classical thermalization-based algorithms such as simulated annealing. Do engineered quantum annealing devices effectively perform classical t
Quantum annealing with more than one hundred qubits
Quantum technology is maturing to the point where quantum devices, such as quantum communication systems, quantum random number generators and quantum simulators, may be built with capabilities exceeding classical computers. A quantum annealer, in particular, solves hard optimisation problems by evolving a known initial configuration at non-zero temperature towards the ground state of a Hamiltonia
Quantum Simulations of Classical Annealing Processes
We describe a quantum algorithm that solves combinatorial optimization problems by quantum simulation of a classical simulated annealing process. Our algorithm exploits quantum walks and the quantum Zeno effect induced by evolution randomization. It requires order $1/\sqrt{\delta}$ steps to find an optimal solution with bounded error probability, where $\delta$ is the minimum spectral gap of the s
Quantum Annealing of a Disordered Magnet
View PDF Abstract: Traditional simulated annealing utilizes thermal fluctuations for convergence in optimization problems. Quantum tunneling provides a different mechanism for moving between states, with the potential for reduced time scales. We compare thermal and quantum annealing in a model disordered Ising magnet, Li\sub{Ho}{0.44}\sub{Y}{0.56}\sub{F}{4}, where the effects of quantum mechanics
Adiabatic Quantum Computing for Binary Clustering
Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest. In this paper, we therefore consider this paradigm and discuss how to adopt it to the problem of binary clustering. Numerical simulations demonstrate the feasibility of our approach and illustrate how syste
December 3, 2013 - CS 201: Experimental Programable Quantum Annealing, SERGIO BOIXO, Google — UCLA Computer Science
Experimental Programable Quantum Annealing Sergio Boixo Google Abstract: I will start by motivating quantum computation, and explaining some of its principles. Quantum annealers are special purpose quantum optimizers. "Noise" (open system dynamics) is an important feature of quantum annealers, and they are not necessarily universal quantum computers. Can quantum effects play a functional role in
Hidetoshi Nishimori: "Theory of Quantum Annealing"
Hidetoshi Nishimori visited Google LA on March 28, 2014 to give a talk: "Theory of Quantum Annealing" Abstract: Quantum annealing is a generic framework, metaheuristic, for combinatorial optimization. I will first review the basic formulation of quantum annealing and numerical evidence for its performance, particularly in comparison with classical simulated annealing. I will then explain a few
Quantum annealing speedup over simulated annealing on random Ising chains
We show clear evidence of a quadratic speedup of a quantum annealing (QA) Schroedinger dynamics over a Glauber master-equation simulated annealing (SA) for a random Ising model in one dimension, via an equal-footing exact deterministic dynamics of the Jordan-Wigner fermionized problems. This is remarkable, in view of the arguments of Katzgraber et al., PRX 4, 021008 (2014), since SA does not encou
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
https://www.bloomberg.com/news/features/2017-06-14/the-machine-of-tomorrow-today-quantum-computing-on-the-verge
https://www.ozy.com/rising-stars/can-this-quantum-computing-genius-beat-out-ibm-and-google/79169
https://www.inc.com/greg-satell/null
Private Site
Private Site
Private Site
IBM Developer
