Talk Title #1: A photonic quantum computer design with only one controllable qubit
Ben Bartlett (Stanford University, Prof. Shanhui Fan's group)
We describe a design for a photonic quantum computer which requires minimal quantum resources: a single coherently-controlled atom. Quantum operations applied to the atomic qubit can be teleported onto the photonic qubits via projective measurement, and arbitrary quantum circuits can be compiled into a sequence of these teleported operators. The proposed device has a machine size which is independent of quantum circuit depth, does not require single-photon detectors, operates deterministically, and is robust to experimental imperfections.
Talk title #2: Towards MEMS-driven photonic computing
Sunil Pai (Stanford University, advised by Prof. Olav Solgaard and co-advised by Profs. David Miller and Shanhui Fan)
ABSTRACT: Programmable nanophotonic networks of Mach-Zehnder interferometers are energy-efficient circuits for matrix-vector multiplication that benefit a wide variety of applications such as artificial intelligence, quantum computing and cryptography. In this talk, we discuss the theory and algorithms to set up field-programmable photonic networks using MEMS (microelectromechanical systems)-actuated phase shifts, which unlike currently more commonplace thermal phase shifters, cost no energy to maintain a constant phase shift and therefore significantly improve overall energy efficiency. We discuss the corresponding implications on gradient-based optimization (on-chip machine learning), scalability and dispersion of such networks for commercial applications such as training and inference in photonic neural networks, optical cryptocurrency, and low-loss quantum computers. We will then attempt a live demonstration of programming a small thermally-driven 6x6 triangular photonic network and observe the practical considerations such as thermal drift, dispersion, and phase shift calibration in these devices.
Ben Bartlett is an applied physics Ph.D. candidate in Prof. Shanhui Fan's group at Stanford University. He designs computing architectures which use light to process classical and quantum information. His other research interests include machine learning, nanophotonics, and quantum optics, and he has a B.S. in physics and computer science from Caltech.
Sunil Pai is a PhD candidate in Electrical Engineering at Stanford University advised by Olav Solgaard and co-advised by David Miller and Shanhui Fan. He received a B.S. degree with Honors in Physics and an M.S. degree in Computer Science, with a concentration in Artificial Intelligence and Biocomputation, from Stanford University in 2015 and 2016, respectively. His research interests include machine learning, photonics, and quantum optics, specifically using machine learning to design photonic devices and photonics to accelerate machine learning capabilities.