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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 #None
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.

Author Information

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

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