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

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.

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.

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

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.

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.

QuACS: Variational Quantum Algorithm for Coalition Structure Generation in Induced Subgraph Games

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

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..

This repository holds implementations of QAOA-specific compilation policies on top of the Qiskit compiler.

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

Python package to solve vehicle routing problems using quantum optimization algorithms

Quantum version of the classical Nim game. An automatic opponent allows to game to not be as easy as it seems.

Classical vs Quantum comparison using QAOA for asset portfolio optimization | Qiskit | Finance + Quantum Computing

QAOA energy landscape exploration.

A hands-on workshop repository featuring Qiskit-based quantum computing exercises tailored for the LHCb physics community. Includes beginner-friendly notebooks on Qiskit fundamentals, Deutsch-Jozsa, QML, QAOA for Max-Cut, and Grover’s algorithm, with step-by-step explanations and simulations. Ideal for physicists exploring quantum algorithms.

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