SaneLYX / quantumzero

Paper《Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks》

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Overview

Quantumzero aims to automate the design of annealing schedules in a hybrid quantum-classical framework. We study both MCTS and QZero algorithms's performance in discovering effective annealing schedules even when the annealing time is short for the 3-SAT examples.

Software requirements

OS Requirements

This package is supported for macOS and Windows. The package has been tested on the following systems:

macOS: Mojave (10.14.1)

windows 10

Python Dependencies

quantumzero mainly depends on the Python scientific stack.

numpy

math

scipy

matplotlib

qutip

Demo

One can download the code pacage,and directly run "demo-mcts", "demo-sd","demo-QZERO" to get the results for some examples.

About

Paper《Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks》


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Language:Python 89.6%Language:Jupyter Notebook 10.4%