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
Implementation of Variational Quantum Factoring algorithm.
qTorch (Quantum Tensor Contraction Handler) https://arxiv.org/abs/1709.03636 -> for quantum simulation using tensor networks
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.
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.
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
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.
This repository contains a quantum computing framework implemented in TypeScript.
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
Implementation for QAOA: MaxCut for weighted graph
Some tests with QAOA, VQE, annealers and other procedures for NISQ quantum computers
Generate QAOA circuits with just your objective function!
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
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