Upgrade 2020: PHI LAB Speakers

Franco Nori

Research Scientist,
University of Michigan

Machine Learning Applied to Computationally Difficult Problems in Quantum Physics

Dr. Franco Nori, of the University of Michigan Physics Department, presented examples of novel uses of machine learning applied to three quantum physics problems. The first problem stems from issues encountered when attempting to calculate unknown quantum phases in an efficient manner using certain types of machine learning, which come with some limitations. Dr. Nori presented cutting-edge neural network technology that overcomes such limitations.

Dirk Englund

Associate Professor of Electrical Engineering and Computer Science at MIT

How Optical Technologies Overcome Limitations of Electronics in Machine Learning

Machine learning technology is facing limitations imposed by computational bottlenecks in electronics for tasks involving functions such as vision, games, control, and language processing. Dr. Dirk Englund, Associate Professor of Electrical Engineering and Computer Science at MIT, talked about the benefits of using photonic integrated circuits to overcome these bottlenecks.

Zoltan Toroczkai

University of Notre Dame

The Path to Identifying Fundamental Limits of Continuous Time Analog Computing

Boolean Satisfiability (SAT), which was the first NP-complete problem to be identified in the 1970s, is a family of logical constraint satisfaction problems that are still intractable today. An efficient solution to this problem would translate to all other hard computing problems that are known to also be in NP, though the general consensus is that there is no efficient solution to such problems.

Isaac Chuang

Professor of Physics, MIT

New Thinking on Reducing Cost and Time in Programmable Quantum Simulators

A quantum simulation that can determine molecular properties enables the advancement of technologies like quantum computers and aids in the progress of quantum chemistry and quantum physics. However, major complications in the form of cost and time arise from undertaking these calculations, given current methods and computing power.

Hideo Mabuchi

Professor, Applied Physics,
Stanford University

How Coherent Ising Machines Can Bring Improvements to Combinatorial Optimization and other Computing Challenges

Current research into optimization algorithms brings the added benefit of foresight into future computational challenges. By testing optimization techniques on tough problems, researchers can more deeply define what makes that problem difficult, and how that difficulty will impact scalability.
In his talk at Upgrade 2020, Dr. Hideo Mabuchi, Professor of Applied Physics at Stanford University…

Eli Yablonovitch

Professor, UC Berkeley

Eli Yablonovitch Outlines the Promise of Physics-Based Optimization Principles for the Future of Computing

In the physical world we constantly witness examples of optimization under constraints. Light through a glass window takes the route that requires the least amount of time. A leaf falling from a forest canopy follows a path which generates the least entropy in its floating freefall. It is thus useful to recognize where optimization principles come into play, and exploit them to solve optimization problems across many fields, including electronic engineering.

Alireza Marandi

Assistant Professor,
California Institute of Technology

Computing Opportunities Using Optical Parametric Oscillator Networks

As we reach limitations of standard computing, the need arises for different types of networks capable of solving incredibly complex and costly problems, from protein folding to social network optimization. In his talk, Dr. Alireza Marandi, Assistant Professor of Electrical Engineering and Applied Physics at Caltech, discussed the opportunities provided by Networks of Optical Parametric Oscillators (OPOs), which use the power of phase transitions for computation.

Amir Safavi-Naeini

Assistant Professor, Applied Physics, Stanford University

A Platform of Possibilities: Phononic and Photonic Circuits in Harmony

It’s possible to efficiently process both quantum and classical information on a single platform by employing mechanical motion and the efficiency of optics, according to Dr. Amir Safavi-Naeini, Assistant Professor of Applied Physics at Stanford University. In his Upgrade 2020 talk, Dr. Safavi-Naeini discussed his work-in-progress solution to overcoming limitations encountered with current platforms.

Timothee Leleu

University of Tokyo

Neuromorphic in Silico Simulator For the Coherent Ising Machine

The human brain is an inspiring model of what can be achieved computationally. To design a more effective simulation of a Coherent Ising Machine (CIM), we can use what we understand of the brain’s balance between speed, size, and energy efficiency.

“The brain computes using billions of neurons using only 20 Watts of power and operates at a relatively low frequency,” said Timothee Leleu, Project Professor of Information and Electronics at the University of Tokyo, in his talk at Update 2020.