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Poster
Quantum Perceptron Models
Ashish Kapoor · Nathan Wiebe · Krysta Svore

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #136
in Tuesday Posters »
We demonstrate how quantum computation can provide non-trivial improvements in the computational and statistical complexity of the perceptron model. We develop two quantum algorithms for perceptron learning. The first algorithm exploits quantum information processing to determine a separating hyperplane using a number of steps sublinear in the number of data points $N$, namely $O(\sqrt{N})$. The second algorithm illustrates how the classical mistake bound of $O(\frac{1}{\gamma^2})$ can be further improved to $O(\frac{1}{\sqrt{\gamma}})$ through quantum means, where $\gamma$ denotes the margin. Such improvements are achieved through the application of quantum amplitude amplification to the version space interpretation of the perceptron model.
Keywords: (Other) Classification · (Other) Machine Learning Topics

Author Information

Ashish Kapoor (Microsoft)
Nathan Wiebe (Microsoft Research)
Krysta Svore (Microsoft)

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