Multi-backend SDK for quantum optimisation
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Updated
May 31, 2024 - Python
Multi-backend SDK for quantum optimisation
Implementation of Variational Quantum Factoring algorithm.
Optimize QAOA circuits for graph maxcut using tensorflow
Portfolio Optimization on a Quantum computer.
Generate QAOA circuits with just your objective function!
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.
Variational Quantum Factoring
Solving the Travelling Salesman Problem, with applying the hard constraints using the QAutoencoder
Addressed smart city transportation complexities using quantum-centric supercomputing. Implemented Grover’s, QAOA and Quantum Walk algorithms
Generate QAOA circuits with just your objective function!
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.
Code for the paper "Multistart Methods for Quantum Approximate Optimization"
Repository for the Software and Computing for Applied Physics Project
Quantum algorithms for Portfolio Management.
An optimized implementation of the Quantum Approximate Optimization Algorithm (QAOA) with Quimb.
Quantum computational projects using the Cirq framework
My reimplementation of the Variational Quantum-Classical Hybrid algorithm, the Quantum Approximate Optimization Algorithm. It features the standard implementation on the Ring of Disagrees Cost Hamiltonian, and my new implementation (called Power Iteration) that utilizes a new cost function. This Quantum Machine Learning Model outperforms QAOA on…
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