Hybrid Quantum-Classical Algorithms

TL;DR

This thesis investigates hybrid quantum-classical algorithms, specifically a quantum Metropolis solver and a classical tool for evaluating quantum algorithms in chemistry, aiming to improve performance and practicality.

Contribution

It introduces a quantum Metropolis solver and a classical evaluation tool, advancing hybrid algorithms for industrial and chemical applications.

Findings

QMS outperforms classical methods in industrial tasks.

TFermion helps evaluate quantum algorithm costs in chemistry.

Hybrid algorithms leverage strengths of both classical and quantum computing.

Abstract

This thesis explores hybrid algorithms that combine classical and quantum computing to enhance the performance of classical algorithms. Two approaches are studied: a hybrid search and sample optimization algorithm and a classical algorithm that assesses the cost and performance of quantum algorithms in chemistry. Hybrid algorithms are vital due to limitations in both classical and quantum computing, offering a solution by leveraging the strengths of both. The first algorithm, quantum Metropolis Solver (QMS), adapts a quantum walk to a Metropolis-Hastings algorithm for industrial applications, demonstrating advantages over classical counterparts in various sectors. The second algorithm, TFermion, is a classical tool for evaluating the cost of T-type gates in quantum chemistry algorithms, aiding in the comparison and execution of these algorithms on real quantum hardware, and applied to the design of more efficient electric batteries.