Tristan Austin
M.A.Sc. Candidate
Quantum Nanophotonics Lab
Physics, Engineering Physics & Astronomy
Tristan is a second year Masters student co-supervised with Bhavin Shastri. He has broad interests in various novel computing systems, and their unique applications, and is especially excited about quantum computing and quantum machine learning. Tristan hopes to have a positive impact on the world through the research and development of cutting-edge technologies. Prior to beginning his undergraduate degree, he had the opportunity to travel throughout western Canada and India and Nepal. His interests include travel, cooking, music, and exercise.
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Abstract: Neuromorphic (brain-inspired) photonics leverages photonic chips to accelerate artificial intelligence, offering high-speed and energy efficient solutions in RF communication, tensor processing, and data classification. However, the limited physical size of integrated photonic hardware constrains network complexity and computational capacity. In light of recent advances in photonic quantum technology, it is natural to utilize quantum exponential speedup to scale photonic neural network capabilities. Here we show a combination of classical network layers with trainable continuous variable quantum circuits yields hybrid networks with improved trainability and accuracy. On a classification task, hybrid networks achieve the same performance when benchmarked against fully classical networks that are twice the size. When the bit precision of the optimized networks is reduced through added noise, the hybrid networks still achieve greater accuracy when evaluated at state of the art bit precision. These hybrid quantum classical networks demonstrate a unique route to improve computational capacity of integrated photonic neural networks without increasing the physical network size.