Authors: Jose Pablo Pinilla Gomez and Steven JE Wilton (University of British Columbia)
Abstract: Quantum-assisted sampling is a promising technique to enable training probabilistic ML models, which otherwise depend on slow-mixing classical sampling methods; such as, the use of Quantum Annealing Processors (QAP) to train Boltzmann Machines (BMs). Previous work has shown that QAPs can sample from a Boltzmann distribution, although, at an unknown instance-dependent temperature. Due to this distribution divergence, existing training algorithms have resorted to negative-phase temperature scaling.
This method, although effective under arduous tuning, introduces unwanted noise to the sample set due to the quantization errors caused by the under-utilization of the QAP bias ranges; and is prone to bias overflow. We introduce a change in the training algorithm to allow positive-phase temperature scaling; an approach that reduces the impact of quantization noise, while still incorporating temperature scaling. As a result, we see an overall improvement in the convergence rate and testing accuracy, when compared to the state-of-the-art approach.
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