Quantum Hamiltonian-Based Models & the Variational Quantum Thermalizer Algorithm

VQT for d-wave superconductor

We introduce a new class of generative quantum-neural-network-based models called Quantum Hamiltonian-Based Models (QHBMs). In doing so, we establish a paradigmatic approach for quantum-probabilistic hybrid variational learning, where we efficiently decompose the tasks of learning classical and quantum correlations in a way which maximizes the utility of both classical and quantum processors. In addition, we introduce the Variational Quantum Thermalizer (VQT) for generating the thermal state of a given Hamiltonian and target temperature, a task for which QHBMs are naturally well-suited.

Full paper can be found here.

Comparison of fault-tolerant thresholds for planar qudit geometries






We introduce and analyze a new type of decoding algorithm called General Color Clustering (GCC), based on renormalization group methods, to be used in qudit color codes. The performance of this decoder is analyzed under depolarizing and generalized bit flip noise models, and is used to obtain the first fault-tolerant threshold estimates for qudit 6-6-6 color codes. The proposed decoder is compared with similar decoding schemes for qudit surface codes as well as the current leading qubit decoders for both sets of codes…

Examining the Effects of Simplified Neural Net Structure on Jet Identification





The summer after my freshman year at Yale, I worked under Tobias Golling in the Yale ATLAS High Energy Research Group. At CERN, particles are accelerated and collided in the Large Hadron Collider (LHC), forming “jets” of subatomic particles. In these collision events, each particle gives off unique signatures, which are measured by calorimeters, spectrometers, and magnets in the detector. Continue reading “Examining the Effects of Simplified Neural Net Structure on Jet Identification”