We develop a quantum algorithm to solve combinatorial optimization problems through quantum simulation of a classical annealing process. Our algorithm combines techniques from quantum walks, quantum phase estimation, and quantum Zeno effect. It can be viewed as a quantum analogue of the discrete-time Markov chain Monte Carlo implementation of classical simulated annealing. Our implementation re...

There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very ...

The question of satissability for a given proposi-tional formula arises in many areas of AI. Especially nding a model for a satissable formula is very important though known to be NP-complete. There exist complete algorithms for satissability testing like the Davis-Putnam-Algorithm, but they often do not construct a satisfying assignment for the formula , are not practically applicable for more...

We propose a variant of the Simulated Annealing method for optimization in the multivariate analysis of diierentiable functions. The method uses the Hybrid Monte Carlo algorithm for the proposal of new conngurations. We show how this choice can improve the performance of simulated annealing methods by allowing much faster annealing schedules.

An algorithm is developed to statistically find the best global fit of a nonlinear non-convex cost-function over a D-dimensional space. It is argued that this algorithm permits an annealing schedule for ''temperature'' T decreasing exponentially in annealing-time k, T = T 0 exp(−ck 1/D). The introduction of re-annealing also permits adaptation to changing sensitivities in the multi-dimensional ...

We propose a new stochastic algorithm (generalized simulated annealing) for computationally finding the global minimum of a given (not necessarily convex) energy/cost function defined in a continuous D-dimensional space. This algorithm recovers, as particular cases, the so called classical (“Boltzmann machine”) and fast (“Cauchy machine”) simulated annealings, and can be quicker than both. Key-...

We applied simulated annealing algorithm to nurse scheduling problem. For time complexity problem of simulated annealing, we suggested an efficient transition rule using cost matrix for simulated annealing. The experimental results showed that the suggested method generated a nurse scheduling faster in time and better in quality compared to traditional simulated annealing.

Beginning in 1983, simulated annealing was marketed as a global optimization methodology that mimics the physical annealing process by which molten substances cool to crystalline lattices of minimal energy. This marketing strategy had a polarizing effect, attracting those who delighted in metaphor and alienating others who found metaphor insufficient at best and facile at worst. In fact, the em...

In this paper we present a new algorithm for segmenting SAR images. A common problem with segmentation algorithms for SAR imagery is the poor placement of the edges of regions and hence of the regions themselves. This usually arises because the algorithm considers only a limited number of placements for regions. The new algorithm circumvents this shortcoming, and produces an optimal segmentatio...