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A Boltzmann Machine Implementation for the D-Wave
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The D-Wave is an adiabatic quantum computer. It is an understatement to say that it is not a traditional computer. It can be viewed as a computational accelerator or more precisely a computational oracle, where one asks it a relevant question and it returns a useful answer. The question is how do you ask a relevant question and how do you use the answer it returns. This paper addresses these issues in a way that is pertinent to machine learning. A Boltzmann machine is implemented with the D-Wave since the D-Wave is merely a hardware instantiation of a partially connected Boltzmann machine. This paper presents a prototype implementation of a 3-layered neural network where the D-Wave is used as the middle (hidden) layer of the neural network. This paper also explains how the D-Wave can be utilized in a multi-layer neural network (more than 3 layers) and one in which each layer may be multiple times the size of the D-Wave being used.
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Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification
A supervised quantum Boltzmann machine with parallel annealing reaches small-CNN-level accuracy on PneumoniaMNIST and BreastMNIST in fewer epochs, while cutting QPU time by 69.65% versus sequential annealing.
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