Hedge, Pratibha Raghupati (2022) Genetic Algorithms Assisted Adiabatic Quantum Computing. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Resource language: English
Title: Genetic Algorithms Assisted Adiabatic Quantum Computing
Creators:
CreatorsEmail
Hedge, Pratibha Raghupatipratibharaghupati.hegde@unina.it
Date: 10 March 2022
Number of Pages: 148
Institution: Università degli Studi di Napoli Federico II
Department: Fisica
Dottorato: Fisica
Ciclo di dottorato: 34
Coordinatore del Corso di dottorato:
nomeemail
Capozziello, Salvatoresalvatore.capozziello@unina.it
Tutor:
nomeemail
Lucignano, ProcoloUNSPECIFIED
Cataudella, VittorioUNSPECIFIED
Date: 10 March 2022
Number of Pages: 148
Keywords: Quantum computing, Adiabatic quantum computing, quantum annealing, minor embedding, shortcuts to adiabaticity, genetic algorithms, evolutionary algorithms
Settori scientifico-disciplinari del MIUR: Area 02 - Scienze fisiche > FIS/03 - Fisica della materia
Date Deposited: 16 Mar 2022 15:37
Last Modified: 28 Feb 2024 10:46
URI: http://www.fedoa.unina.it/id/eprint/14446

Collection description

In the current Noisy Intermediate Scale Quantum (NISQ) era, quantum computational resources can be utilized efficiently by optimizing them with suitable classical algorithms. In the same spirit, this thesis addresses the relevant problems in the paradigm of adiabatic quantum computation and in turn quantum annealing. We mainly resort to heuristic optimization techniques of evolutionary algorithms as a numerical tool and demonstrate their effectiveness in finding the solutions to the problems considered. The first problem we focus on is to find an equivalent 2-body interactions of a $p$-body Hamiltonian as it is a necessity for embedding optimization problems with $p$-local interactions in the current quantum annealer hardware architectures. We use genetic algorithms to optimize a function which minimizes the energy difference between the lower spectrum of the original and the mapped 2-body Hamiltonian. We consider the two analytically solvable cases of a ferromagnetic $p$-spin model to discuss our results. We also show further improvements by implementing memetic algorithms which enforces additional local searches. As the second problem of this thesis, we propose an effective approach to shortcuts to adiabaticity using a numerical approach based on genetic algorithms. The hard optimization problems often have small spectral gaps which make the system to undergo diabatic transitions in the finite time quantum annealing. In this thesis, we tackle this problem by engineering the annealing schedules starting from the polynomial ansatz by treating their coefficients as chromosomes of a genetic algorithm. We also explore shortcuts to adiabaticity by computing a practically feasible k-local optimal driving operator, showing that even for k = 1 we achieve substantial improvement of the fidelity over the standard annealing solution. With these genetically optimized annealing schedules and/or optimal driving operators, we are able to perform quantum annealing in relatively short time-scales and with higher fidelity compared to traditional approaches.

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