Online Seminar: Asst.Prof. Nathan Wiebe, PNNL | UW, USA

A physics inspired class of quantum neural network for generative training

 

blue computer chip with overlay of white brain connections

Training Fully Quantum Boltzmann Machines

SPEAKER: Affiliate Assistant Professor Nathan Wiebe

AFFILIATION: Pacific Northwest National Labs | University of Washington, USA

HOSTED BY: Dr Mária Kieferová, UTS Centre for Quantum Software and Information

ABSTRACT: 
In recent years quantum machine learning has grown by leaps and bounds but a major problem still vexes the field is how to efficiently train quantum neural networks.  This is particularly challenging because of the lack of a natural backpropagation algorithm for updating the quantum model. 
In this talk, I will focus on an approach that can mitigate this problem through generative training.  We will show how to construct a fully quantum model of a Boltzmann machine and train all of the parameters of that model for both the quantum and classical parameters in the model.  In contrast, existing methods were not able to achieve this. 
In particular, we will show explicit query upper bounds for the cost of simulation, provide a formal proof for BQP-completeness for evaluating such neural networks and also discuss remaining problems in the field and how to generalize the ideas presented here to go beyond Boltzmann machines to allow efficient training of broad classes of quantum neural networks.

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UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.