(The talk will be given in English)

Speaker:     Dr. Tamir Bendory
                   Princeton University

Monday, November 12^th, 2018
15:00 - 16:00

Room 011, Kitot Bldg., Faculty of Engineering

Estimation in extreme noise levels with application to cryo-electron microscopy
 

Abstract

Single-particle cryo-electron microscopy (cryo-EM) is an innovative technology for elucidating structures of biological molecules at atomic-scale resolution.In a cryo-EM experiment, tomographic projections of a molecule, taken at unknown viewing directions, are embedded in highly noisy images at unknown locations. The cryo-EM problem is to estimate the 3-D structure of a molecule from these noisy images. 

Inspired by cryo-EM, the talk will focus on two estimation problems: multi-reference alignment and blind deconvolution. These problems abstract away much of the intricacies of cryo-EM, while retaining some of its essential features. In multi-reference alignment, we aim to estimate a signal from its noisy, rotated observations. While the rotations and the signal are unknown, the goal is only to estimate the signal. In the blind deconvolution problem, the goal is to estimate a signal from its convolution with an unknown, sparse signal in the presence of noise. Focusing on the low SNR regime, I will propose the method of moments as a computationally efficient estimation framework for both problems and will introduce its properties. In particular, I will show that the method of moments allows estimating the sought signal accurately in any noise level, provided sufficiently many observations are collected, with only one pass over the data. I will then argue that the same principles carry through to cryo-EM, show examples, and draw potential implications.‏

You are invited to attend a lecture

Merging Control Calibration and System Characterization for Scalable Quantum Computers

:By

Dr. Shai Machnes

Theoretical physics, Saarland University, Saarbrücken, Germany

Abstract

Quantum computing is a revolution in the making, but the field is facing difficulties scaling up from the current 20-30 qubits to larger scale devices. For Josephson junction quantum computers, manufacturing variabilities necessitate individual characterization of each qubit and each coupling, and calibration of each control sequence, totaling many thousands of measurements and calibrations for a 100 qubit chip, and many months of dedicated work by a large team. Further, control pulses are currently designed using highly simplified analytic models, resulting in initially poor fidelities. The controls are then calibrated in-situ, achieving high-fidelities, but without a corresponding model. We are therefore left with a ridiculous situation: a model we know is inaccurate, working controls for which we do not have a matching model, and a calibration process from which we learned nothing about the system. We propose a novel procedure to rectify the above problems, clearing a path to scalable quantum computation.

We begin with a quick review of the current state of quantum computing hardware. We then detail the new quantum optimal control technique, and how it may be utilized to merge control sequence design, calibration and system characterization into a single, scalable process. Finally, we'll present how a Computer Algebra System may be utilized to derive simplified system models, guiding automatic progressive characterization and calibration of large complex systems, and how generative-adversarial learning may be utilized to allow small quantum computers to boot-up larger ones.

We believe these new approaches will greatly improve both the accuracy of current quantum computers and our understanding of their dynamics - both critical components on the road to large scale quantum computation.

Main reference: Shai Machnes, Elie Assémat, David Tannor, and Frank K. Wilhelm - Phys. Rev. Lett. 120, 150401 (2018) - Tunable, Flexible, and Efficient Optimization of Control Pulses for Practical Qubits

On Thursday, Nov 08th, 2018, 15:00

Room 011, ‘Kitot’ building