Refreshments: 3:30, talk 4:00 - 5:00 p.m.

2019

Apr
24
2019
Low Center for Industrial Innovation (CII) 3051 4:00 pm

Apr
18
2019
Low Center for Industrial Innovation (CII) 3051 4:00 pm

Apr
10
2019
Low Center for Industrial Innovation (CII) 3051 4:00 pm

Apr
3
2019
The Herta Leng Memorial Lecture
Low Center for Industrial Innovation (CII) 3051 4:00 pm

Mar
13
2019
Low Center for Industrial Innovation (CII) 3051 4:00 pm

Feb
13
2019
A Theoretical Study of Schwarzites and Linear Carbon

Pure carbon structures contain a wealth of information and potential properties, we focus on two, long linear carbon chains and Schwarzites. Long linear carbon chains, a one dimensional sp hybridized carbon chain,
have been observed to be encapsulated by a carbon nanotube. Together this system produces a resonant Raman signal from 1770-1860 cm-1, known as the C-mode. The origin of this signal is still under scrutiny. We explore the nature of Raman activity in long linear carbon chains through the use of first principles density functional theory and identify the effect of exact exchange in calculations using hybrid functionals. With exact exchange the most intense Raman active mode, the longitudinal optical mode, converges, with respect to length, to 1831
cm-1, within the range of reported measurements of the C-mode. Also the electronic gap converges, with respect to length, to 1.8 eV, near the known resonance energy.

Schwarzites are sp2, or hexagonal, triply periodic minimal carbon surfaces with negative Gaussian curvature from the introduction of 7-, 8-, 9-, and 10-membered rings. Recently theoretical impregnation of zeolites, simulating the templating processes, has produced Schwarzites.
We present first principles and classical dynamics calculations of electronic and vibronic density of states calculations to establish a connection between the theoretical structures proposed to the experimentally obtained materials. We identify the theoretical structures as energetically and dynamically stable, graphitic in nature, and semimetallic.

Ross Siegel, RPI
Low Center for Industrial Innovation (CII) 3051 4:00 pm

Jan
30
2019
Efficiently Embedding QUBO Problems on Adiabatic Quantum Computers; A Classical-Quantum Hybrid Approach for Unsupervised Probabilistic Machine Learning

Abstract 1: Adiabatic quantum computers like the D-Wave 2000Q can approximately solve the QUBO problem, which is an NP-Hard problem, and have been shown to outperform classical computers on several instances. Solving the QUBO problem literally means solving virtually any NP-Hard problem like the Traveling Salesman Problem (TSP), Airline Scheduling Problem, Protein Folding Problem, Genotype Imputation Problem etc., thereby enabling significant scientific progress, and potentially saving millions / billions of dollars in logistics, airlines, healthcare and many other industries. However, before QUBO problems are solved on quantum computers, they must be embedded (or compiled) onto the hardware of quantum computers, which in itself is a very hard problem. In this work, we propose an efficient embedding algorithm, that lets us embed QUBO problems fast, uses less qubits and gets the objective function value close to the global minimum value.  We then compare the performance of our embedding algorithm to that of D-Wave's embedding algorithm, which is the current state of the art, and show that our embedding algorithm convincingly outperforms D-Wave's embedding algorithm. Our embedding approach works with perfect Chimera graphs, i.e. Chimera graphs with no missing qubits.

Abstract 2: For training unsupervised probabilistic machine learning models, matrix computation and sample generation are the two key steps. While GPUs excel at matrix computation, they use pseudo-random numbers to generate samples. Contrarily, Adiabatic Quantum Processors (AQP) use quantum mechanical systems to generate samples accurately and quickly, but are not suited for matrix computation. We present a Classical-Quantum Hybrid Approach for training unsupervised probabilistic machine learning models, leveraging GPUs for matrix computations and the D-Wave quantum sampling library for sample generation. We compare this approach to classical and quantum approaches across four performance metrics. Our results indicate that while the hybrid approach--which uses one AQP and one GPU--outperforms quantum and one of the classical approaches, it performs comparably to the GPU approach, and is outperformed by the CPU approach, which uses 56 high-end CPUs. Lastly, we compare sampling on AQP versus sampling library and show that AQP performs better.

Prasanna Date, RPI
Low Center for Industrial Innovation (CII) 3051 4:00 pm

2018

Nov
14
2018
Neutrino Physics and the PROSPECT Experiment

Neutrinos, the most ghostly of Standard Model particles, were first detected by Clyde Cowan and Frederick Reines in 1956. Although these particles are included in the Standard Model, their properties such as mass and mixing parameters are not predicted and must be measured. Ever since their discovery, physicists have been trying to piece together a comprehensive understanding of the neutrino and over the past 6 decades a nearly complete picture has emerged. However, there are still some undetermined parameters as well as phenomena that have resisted explanation. One of these unexplained phenomena that has arisen rather recently termed the "reactor antineutrino anomaly" is the deficit in the measured flux of antineutrinos from nuclear reactors relative to the expected flux from calculation. Furthermore, the energy spectrum of reactor antineutrinos from recent experiments deviates from the calculated shape in the region of 5-7 MeV antineutrino energy. PROSPECT, the Precision Reactor Oscillation and Spectrum experiment, currently taking data at Oak Ridge National Lab, aims to shed some light on these anomalies by measuring the flux a few meters from the core of a highly enriched uranium (HEU) research reactor. In so doing, many obstacles had to be overcome, not the least of which was designing a detector that could isolate antineutrino events from huge backgrounds inherent in the environment at the surface of the Earth and next to a reactor.  In this talk, I will give a brief history of neutrinos and how their properties were determined highlighting some key experiments. Special emphasis will be given to the PROSPECT experiment with recent data and analysis shown.

Low Center for Industrial Innovation (CII) 3051 4:00 pm

Oct
24
2018
Yung Joon Jung, Northeastern University
Low Center for Industrial Innovation (CII) 3051 4:00 pm

Oct
17
2018
The Robert Resnick Lecture
Stephon Alexander, Brown University
Sage 3303 4:00 pm

Oct
3
2018
Integrated Slow-Light Silicon Photonic Devices and Systems for RF Photonic and Optical Computing Applications

Significant research and development in the field of Si Photonics have been done in the past two decades leading to the worldwide establishment of Si Photonics foundry services. The technology advancement includes hybrid integration of III-V laser diodes on Si platform, micro-ring or disk resonators, integrated PN junction based Mach-Zehnder (MZ) light modulators, SiGe on Si photodetectors, SiN low-loss waveguides, integrated optical delay line, array waveguide gratings etc. In my group, the current focus of research is slow-light Bragg grating rib waveguides and slot waveguides on Si substrate. Slow-light waveguide has enhanced light-matter interaction so allowing significant size reduction in active photonic devices such as MZ light modulators. The MZ modulator is a basic building block for a large array of tunable optical interference units for the on-chip optical neuromorphic computing application in which the MZ interferometer serves as an arbitrary unitary matrix. The passive slow-light waveguide is also a key element for chip scale photonic system integration in many application areas. For example, the slow-light Bragg grating waveguide has been used to construct tunable optical delay lines for beamforming in phased array antennas. For on-chip optical reservoir computing, a dispersive Bragg grating is a critical element in obtaining nanoseconds of true-time delay for storage of memories.

Low Center for Industrial Innovation (CII) 3051 4:00 pm

Sep
19
2018
Yiping Zhao, University of Georgia
Low Center for Industrial Innovation (CII) 3051 4:00 pm

Sep
12
2018
Michael Agiorgousis, RPI
Low Center for Industrial Innovation (CII) 3051 4:00 pm

Apr
25
2018
Marc Miskin, Cornell University

Apr
11
2018
Steven G. Louie, University of California at Berkeley and Lawrence Berkeley National Lab

Mar
21
2018
J. Michael Kosterlitz, Brown University

Feb
28
2018
Guy Consolmagno, Vatican Observatory

Feb
21
2018

Feb
14
2018

Jan
31
2018

Jan
24
2018
Boleslaw Szymanski, Rensselaer Polytechnic Institute