Quantum Physics Commons^™

Generative Adversarial Game With Tailored Quantum Feature Maps For Enhanced Classification, Anais Sandra Nguemto Guiawa

Doctoral Dissertations

In the burgeoning field of quantum machine learning, the fusion of quantum computing and machine learning methodologies has sparked immense interest, particularly with the emergence of noisy intermediate-scale quantum (NISQ) devices. These devices hold the promise of achieving quantum advantage, but they grapple with limitations like constrained qubit counts, limited connectivity, operational noise, and a restricted set of operations. These challenges necessitate a strategic and deliberate approach to crafting effective quantum machine learning algorithms.

This dissertation revolves around an exploration of these challenges, presenting innovative strategies that tailor quantum algorithms and processes to seamlessly integrate with commercial quantum platforms. A …

Path Integral Monte Carlo For Entanglement In Bosonic Lattices At T = 0, Emanuel Casiano-Diaz

Doctoral Dissertations

Path-Integral Monte Carlo Worm Algorithm is one of many Quantum Monte Carlo (QMC) methods that serve as powerful tools for the simulation of quantum many-body systems. Developed in the late 90’s, this algorithm has been used with great success to study a wide array of physical models where exact calculation of observables is not possible due to the exponential size of the Hilbert space. One type of systems that have eluded PIMC-WA implementation are lattice models at zero temperature, which are of relevance in experimental settings, such as in optical lattices of ultra-cold atoms. In this thesis, we develop a …