Multi-backend SDK for quantum optimisation
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Updated
May 16, 2024 - Python
Multi-backend SDK for quantum optimisation
Source code for the book "Quantum Computing for Programmers", Cambridge University Press
Algorithms for optimization tasks (operations research)
Implementation of Variational Quantum Factoring algorithm.
Optimize QAOA circuits for graph maxcut using tensorflow
Application of Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimisation Algorithm (QAOA) to the Travelling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP) using Qiskit on IBM's quantum devices.
qTorch (Quantum Tensor Contraction Handler) https://arxiv.org/abs/1709.03636 -> for quantum simulation using tensor networks
Portfolio Optimization on a Quantum computer.
Implementation of Quantum Approximate Thermalization using Qiskit which involves performing approximated simulation of annealing to do Gibbs sampling of the given system. Based on https://arxiv.org/pdf/1712.05304.pdf
Solution of the Bin-Packing problem using QAOA and Qiskit optimization library
Generate QAOA circuits with just your objective function!
This repository holds implementations of QAOA-specific compilation policies on top of the Qiskit compiler.
Some tests with QAOA, VQE, annealers and other procedures for NISQ quantum computers
This package is a flexible python implementation of the Quantum Approximate Optimization Algorithm /Quantum Alternating Operator ansatz (QAOA) aimed at researchers to readily test the performance of a new ansatz, a new classical optimizers, etc.
The max-cut problem on a random graph with 7 nodes is solved using QAOA, ED, Monte-Carlo, and simulated annealing.
Here we will compare one well-known (ED) and another new method (QAOA) for quantum simulations of many-body physics.
QAOA is one of the flavors of VQA, and it is considered to assert so-called "Quantum Supremacy". I have implemented a Quantum circuit to solve Max-Cut problem. I have written a report of my work.
Implementation for QAOA: MaxCut for weighted graph
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