자료유형  

 학위논문

Control Number  

0017163297

International Standard Book Number  

9798384424093

Dewey Decimal Classification Number  

530.1

Main Entry-Personal Name  

Rad, Ali.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Maryland, College Park., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

227 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

General Note  

Advisor: Hafezi, Mohammad;Gullans, Michael.

Dissertation Note  

Thesis (Ph.D.)--University of Maryland, College Park, 2024.

Summary, Etc.  

요약Statistical learning is emerging as a new paradigm in science. This has ignited interest within our inherently quantum world in exploring quantum machines for their advantages in learning, generating, and predicting various aspects of our universe by processing both quantum and classical data. In parallel, the pursuit of scalable science through physical simulations using both digital and analog quantum computers is rising on the horizon.In the first part, we investigate how physics can help classical Artificial Intelligence (AI) by studying hybrid classical-quantum algorithms. We focus on quantum generative models and address challenges like barren plateaus during the training of quantum machines. We further examine the generalization capabilities of quantum machine learning models, phase transitions in the over-parameterized regime using random matrix theory, and their effective behavior approximated by Gaussian processes.In the second part, we explore how AI can benefit physics. We demonstrate how classical Machine Learning (ML) models can assist in state recognition in qubit systems within solid-state devices. Additionally, we show how ML-inspired optimization methods can enhance the efficiency of digital quantum simulations with ion-trap setups.Finally, in the third part, we focus on how physics can help physics by using quantum systems to simulate other quantum systems. We propose native fermionic analog quantum systems with fermion-spin systems in silicon to explore non-perturbative phenomena in quantum field theory, offering early applications for lattice gauge theory models.

Subject Added Entry-Topical Term  

Quantum physics.

Subject Added Entry-Topical Term  

Mechanical engineering.

Subject Added Entry-Topical Term  

Physics.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Theoretical physics.

Index Term-Uncontrolled  

Analog quantum simulation

Index Term-Uncontrolled  

Quantum circuits

Index Term-Uncontrolled  

Quantum machine learning

Index Term-Uncontrolled  

Quantum simulations

Index Term-Uncontrolled  

Statistical learning

Added Entry-Corporate Name  

University of Maryland, College Park Physics

Host Item Entry  

Dissertations Abstracts International. 86-03B.

Electronic Location and Access  

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Control Number  

joongbu:656071