Multi-backend SDK for quantum optimisation
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Updated
Aug 29, 2024 - Python
Multi-backend SDK for quantum optimisation
Source code for the book "Quantum Computing for Programmers", Cambridge University Press
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.
Implementation of Variational Quantum Factoring algorithm.
qTorch (Quantum Tensor Contraction Handler) https://arxiv.org/abs/1709.03636 -> for quantum simulation using tensor networks
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.
Algorithms for optimization tasks (operations research)
Portfolio Optimization on a Quantum computer.
A General Quantum Software
Solving the Travelling Salesman Problem, with applying the hard constraints using the QAutoencoder
Compare QAOA and Quantum Annealing using 127 qubit higher order Ising problems
This repository contains a quantum computing framework implemented in TypeScript.
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.
Lectures on hybrid quantum-classical machine learning given during "VI Pyrenees Winter School Quantum Information Meeting for Barcelona's Community" on 14-17.02.2023, Setcases, Spain
QuACS: Variational Quantum Algorithm for Coalition Structure Generation in Induced Subgraph Games
Enhancing portfolio optimization solutions: wisely encoding constrained combinatorial optimization problems on quantum devices
Implementation for QAOA: MaxCut for weighted graph
Generate QAOA circuits with just your objective function!
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