Multi-backend SDK for quantum optimisation
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Updated
Jul 2, 2024 - Python
Multi-backend SDK for quantum optimisation
Source code for the book "Quantum Computing for Programmers", Cambridge University Press
qTorch (Quantum Tensor Contraction Handler) https://arxiv.org/abs/1709.03636 -> for quantum simulation using tensor networks
Implementation of Variational Quantum Factoring algorithm.
Optimize QAOA circuits for graph maxcut using tensorflow
Algorithms for optimization tasks (operations research)
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.
Solving the Travelling Salesman Problem, with applying the hard constraints using the QAutoencoder
Portfolio Optimization on a Quantum computer.
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.
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
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.
Generate QAOA circuits with just your objective function!
Implementation for QAOA: MaxCut for weighted graph
Some tests with QAOA, VQE, annealers and other procedures for NISQ quantum computers
Here we will compare one well-known (ED) and another new method (QAOA) for quantum simulations of many-body physics.
Quantum version of the classical Nim game. An automatic opponent allows to game to not be as easy as it seems.
A portfolio generator developed by QuantYantriki for the QSTH 2022 - a quantum hackathon organized by the Quantum Ecosystems and Technology Council of India (QETCI). It utilizes quantum annealing and quantum approximate optimization algorithms using a feedback-based metaheuristic that incorporates classical optimization tools to improve solutions.
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
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