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)

A General Quantum Software

Portfolio Optimization on a Quantum computer.

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

In this work, we use LR-QAOA protocol as an easy-to-implement scalable benchmarking methodology that assesses quantum processing units (QPUs) at different widths (number of qubits) and 2-qubit gate depths.

Enhancing portfolio optimization solutions: wisely encoding constrained combinatorial optimization problems on quantum devices

A project making use of the Variational Quantum Eigensolver ( VQE ) and Quantum Approximate Optimization Algorithms ( QAOA ) in qiskit to optimize an endowment portfolio, with weights defined by risk-parity analysis..

Fixed linear ramp schedules in QAOA constitute a universal set parameters, i.e., a set of γ and β parameters that rapidly approximate the optimal solution, x∗, independently of the COP selected, and that the success probability of finding it, probability(x∗), increases with the number of QAOA layers p.