# Implementing perceptron models with qubits

@article{Wiersema2019ImplementingPM, title={Implementing perceptron models with qubits}, author={Roeland Wiersema and Hilbert J. Kappen}, journal={Physical Review A}, year={2019} }

We propose a method for learning a quantum probabilistic model of a perceptron. By considering a cross entropy between two density matrices we can learn a model that takes noisy output labels into account while learning. A multitude of proposals already exist that aim to utilize the curious properties of quantum systems to build a quantum perceptron, but these proposals rely on a classical cost function for the optimization procedure. We demonstrate the usage of a quantum equivalent of the…

## 4 Citations

### Learning quantum models from quantum or classical data

- Computer Science, PhysicsJournal of Physics A: Mathematical and Theoretical
- 2020

The proposed method is to learn the quantum Hamiltonian, that is such that its ground state approximates the given classical distribution, that can be significantly more accurate than the classical maximum likelihood approach, both for unsupervised learning and for classification.

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- 2021

This work introduces basic concepts related to quantum computations, and then the core functionalities of technologies that implement the Gate Model and Adiabatic Quantum Computing paradigms are explained.

### A Leap among Quantum Computing and Quantum Neural Networks: A Survey

- Computer ScienceACM Computing Surveys
- 2022

First, basic concepts related to quantum computations are introduced, and then the core functionalities of technologies that implement the Gate Model and Adiabatic Quantum Computing paradigms are explained.

### Chaos and Complexity from Quantum Neural Network: A study with Diffusion Metric in Machine Learning

- Computer ScienceJournal of High Energy Physics
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This work establishes the parametrized version of Quantum Complexity and Quantum Chaos in terms of physically relevant quantities, which are not only essential in determining the stability, but also essential in providing a very significant lower bound to the generalization capability of QNN.

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