# A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks

arXiv: https://arxiv.org/abs/2009.13264 · HTML: https://mlsanity.com/paper/2009.13264/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [Quantum machine learning with differential privacy](https://mlsanity.com/paper/2103.06232/) (2021)
- [A Leap among Quantum Computing and Quantum Neural Networks: A Survey](https://mlsanity.com/paper/2107.03313/) (2021)
- [Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction](https://mlsanity.com/paper/2210.07451/) (2022)

## Abstract (arXiv)

In this paper, we present a gradient-free approach for training multi-layered neural networks based upon quantum perceptrons. Here, we depart from the classical perceptron and the elemental operations on quantum bits, i.e. qubits, so as to formulate the problem in terms of quantum perceptrons. We then make use of measurable operators to define the states of the network in a manner consistent with a Markov process. This yields a Dirac-Von Neumann formulation consistent with quantum mechanics. Moreover, the formulation presented here has the advantage of having a computational efficiency devoid of the number of layers in the network. This, paired with the natural efficiency of quantum computing, can imply a significant improvement in efficiency, particularly for deep networks. Finally, but not least, the developments here are quite general in nature since the approach presented here can also be used for quantum-inspired neural networks implemented on conventional computers.