Comparison of D-Wave Quantum Annealing and Classical Simulated Annealing for Local Minima Determination

11/08/2019
by   Yaroslav Koshka, et al.
0

Restricted Boltzmann Machines trained with different numbers of iterations were used to provide a diverse set of energy functions each containing many local valleys (LVs) with different energies, widths, escape barrier heights, etc. They were used to verify the previously reported possibility of using the D-Wave quantum annealer (QA) to find potentially important LVs in the energy functions of Ising spin glasses that may be missed by classical searches. For classical search, extensive simulated annealing (SA) was conducted to find as many LVs as possible regardless of the computational cost. SA was conducted long enough to ensure that the number of SA-found LVs approaches that and eventually significantly exceeds the number of the LVs found by a single call submitted to the D-Wave. Even after a prohibitively long SA search, as many as 30-50 establish if LVs found only by the D-Wave represent potentially important regions of the configuration space, they were compared to those that were found by both techniques. While the LVs found by the D-Wave but missed by SA predominantly had higher energies and lower escape barriers, there was a significant fraction having intermediate values of the energy and barrier height. With respect to most other important LV parameters, the LVs found only by the D-Wave were distributed in a wide range of the parameters' values. It was established that for large or small, shallow or deep, wide or narrow LVs, the LVs found only by the D-Wave are distinguished by a few-times smaller size of the LV basin of attraction (BoA). Apparently, the size of the BoA is not or at least is less important for QA search compared to the classical search, allowing QA to easily find many potentially important (e.g., wide and deep) LVs missed by even prohibitively lengthy classical searches.

READ FULL TEXT

page 1

page 5

page 6

page 8

research
05/01/2019

Towards Sampling from Nondirected Probabilistic Graphical models using a D-Wave Quantum Annealer

A D-Wave quantum annealer (QA) having a 2048 qubit lattice, with no miss...
research
11/14/2016

Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines

Quantum annealing (QA) is a hardware-based heuristic optimization and sa...
research
11/14/2019

Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer

We present the application of Restricted Boltzmann Machines (RBMs) to th...
research
05/19/2013

Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering

We developed a new quantum annealing (QA) algorithm for Dirichlet proces...
research
12/31/2019

Investigating Wave Energy Potential in Southern Coasts of the Caspian Sea and Evaluating the Application of Gray Wolf Optimizer Algorithm

There is a significantly accelerating trend in the application of the wa...
research
04/24/2023

Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated Annealing

This paper presents a local energy distribution based hyperparameter det...

Please sign up or login with your details

Forgot password? Click here to reset