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Presentation

Building scalable variational circuit training for machine learning tasks
Time
Location
Event Type
Special Session (Research Track)
Virtual Programs
Hosted in Virtual Platform
Topics
Design
DescriptionParameterized quantum circuits (PQC) have emerged as a quantum analogue of deep neural networks and can be trained for discriminative or generative tasks. Near-term quantum devices can support the training of circuits with moderate number of qubits (< 50) , and parameters. Understanding the resource requirements and computational overhead needed to train PQCs efficiently will aid in the development of scalable training methods. In this talk I will highlight several recent results which we have developed in pursuit of scalable methods for gradient-based training and error mitigation.