A Focused Approach Can Help Untangle Messy Quantum Scrambling Problems
The world is a cluttered, noisy place, and the ability to effectively focus is a valuable skill. Researchers at JQI have identified a new way to focus their attention and obtain useful insights into the way information associated with a configuration of interacting particles gets dispersed and effectively lost over time. Their technique focuses on a single feature that describes how various amounts of energy can be held by different configurations a quantum system. The approach provides insight into how a collection of quantum particles can evolve without the researchers having to grapple with the intricacies of the interactions that make the system change over time.
Microscopic and Emergent Dynamics of Quantum Information Flows
Abstract: The past fifty years of scientific and technological progress have clearly highlighted information as a physical resource - one that can be traded for heat, work, and other energetic resources. With the ongoing new wave of quantum-based technologies, understanding the microscopic and emergent dynamics of quantum information in many-body quantum systems has thus become a priority.
Ferromagnetism in the Hubbard Model: Squares, Rings and More
Nagaoka ferromagnetism (NF) is a long-predicted example of itinerant ferromagnetism in the Hubbard model and has been studied theoretically for many years. NF occurs when there is one hole in a half-filled band and a large onsite Coulomb repulsion, which does not arise naturally in materials. Quantum dots systems like dopant arrays in Si, can be fabricated with atomically precise complex geometries to create highly controllable systems. This makes them good candidates to study itinerant ferromagnetism in different array geometries.
High Performance Nanophotonic Cavities and Interconnects for Optical Parametric Oscillators and Quantum Emitters
Dissertation Committee Chair: Mohammad Hafezi and Kartik Srinivasan
Committee:
Yanne Chemo
Efrain Rodriguez
Edo Waks
Xiyuan Lu
Quantum Simulation of High-Energy Physics with Trapped Ions
Dissertation Committee Chair: Professor Alicia Kollár
Committee:
Professor Norbert Linke, Advisor and Co-Chair
Professor Zohreh Davoudi
Professor Ian Spielman
Professor Xiaodi Wu
Towards a Renormalization Group scheme for field theories on loops
Theories whose fluctuating degrees of freedom live on extended loops as opposed to points, are abundant in nature. One example is the action obtained upon eliminating the redundant gauge fields in a gauge theory. Formulating a Renormalization Group (RG) procedure for such a theory is an open problem. In this work, we outline a procedure that in principle computes the outcome of coarse-graining and rescaling of such a theory. We make estimates that lead to qualitative agreement with known results of phase transitions in gauge theories and the XY-model.
Bullseye! New Method Accurately Centers Quantum Dots Within Photonic Chips
Researchers at JQI and the National Institute of Standards and Technology (NIST) have developed standards and calibrations for optical microscopes that allow quantum dots to be aligned with the center of a photonic component to within an error of 10 to 20 nanometers (about one-thousandth the thickness of a sheet of paper). Such alignment is critical for chip-scale devices that employ the radiation emitted by quantum dots to store and transmit quantum information.
Carving Up Infinite Quantum Spaces into Simpler Surrogates
Researchers have constructed new mathematical tools for continuous variable (CV) quantum systems, which could lead to more efficient benchmarking for quantum devices and more efficient ways of representing quantum states on classical hardware.
Universal Sharpness Dynamics in Neural Network Training: Fixed Point Analysis, Edge of Stability, and Route to Chaos
In gradient descent dynamics of neural networks, the top eigenvalue of the Hessian of the loss (sharpness) displays a variety of robust phenomena throughout training. This includes early time regimes where the sharpness may decrease during early periods of training (sharpness reduction), and later time behavior such as progressive sharpening and edge of stability. We demonstrate that a simple $2$-layer linear network (UV model) trained on a single training example exhibits all of the essential sharpness phenomenology observed in real-world scenarios.