NMR studies of quantum thermalization

Quantum thermalization is a generic process that a quantum system reaches its equilibrium. Quantum thermalization lies in the interface of various distinct fields, ranging from condensed matter physics and quantum cosmology to quantum information science. The converging efforts from different fields open up avenues for the development of quantum many-body physics and quantum technology. This thesis presents a study of quantum thermalization in solid-state nuclear spin systems using nuclear magnetic resonance technique. By developing the RF control sequences, novel states, observables and Hamiltonians are created to explore and characterize various quantum thermalization phenomena. Leveraging the intrinsic disorder, a novel method to detect spin dynamics at single-site level is introduced. The method is applied to study hydrodynamics emerged from thermalizing quantum systems. In an interacting integrable system, coexistence of ballistic energy transport and diffusive spin transport is observed. With accurate Hamiltonian engineering RF sequences, the thermalization of driven systems are discussed. The exponentially slow thermalization is observed experimentally by measuring the prethermal energy autocorrelation. Beyond the prethermal energy, an even more robust prethermal conserved quantity is discovered. The result suggests a Floquet phase may exist beyond the prethermal regime. To understand many-body localized (MBL) systems, an algorithm to compute local integrals of motion (LIOMs) is designed. From LIOMs, various localization lengths are extracted and their critical behavior is studied. To further improve the Hamiltonian engineering sequences, the deep reinforcement learning (DRL) techniques are adopted. The sequences designed by the DRL show better decoupling performance than the previously best known sequence. Beyond that, a new and advantageous pattern is discovered from the DRL sequences which serves as a useful building block for more complex Hamiltonian engineering sequences.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Peng, Pai

Advisor dc:contributor.advisor

• Cappellaro, Paola

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144798

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144798