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A Beginner's Guide to Quantum Optimization

Published on:

4 January 2023

Primary Category:

Quantum Physics

Paper Authors:

Sarthak Gupta,

Vassilis Kekatos

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Key Details

Proposes a quantum heuristic to handle binary optimization problems with quadratic constraints

Uses a quantum circuit to sample optimized probability distributions

Employs dual decomposition to iteratively adjust Lagrange multipliers

Tests on simulated problems show ability to find near-optimal feasible solutions

AI generated summary

A Beginner's Guide to Quantum Optimization

This paper proposes a novel quantum computing approach to efficiently solve challenging optimization problems with binary variables and constraints. It builds quantum circuits that can generate high-quality solutions by sampling optimized probability distributions. The method is tested on simulated problems and shows promise in finding near-optimal feasible solutions.

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