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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.
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.
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…