# Quantum Perceptron Models

arXiv: https://arxiv.org/abs/1602.04799 · HTML: https://mlsanity.com/paper/1602.04799/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2016.

## Built on by (most cited first, 28 of 28)

- [Towards understanding the power of quantum kernels in the NISQ era](https://mlsanity.com/paper/2103.16774/) (2021)
- [Simulation of a Variational Quantum Perceptron using Grover's Algorithm](https://mlsanity.com/paper/2305.11040/) (2023)
- [A Leap among Quantum Computing and Quantum Neural Networks: A Survey](https://mlsanity.com/paper/2107.03313/) (2021)
- [Experimental quantum pattern recognition in IBMQ and diamond NVs](https://mlsanity.com/paper/2205.00561/) (2022)
- [Quantum Machine Learning for Finance](https://mlsanity.com/paper/2109.04298/) (2021)
- [Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games](https://mlsanity.com/paper/2304.14197/) (2023)
- [Near-Optimal Quantum Coreset Construction Algorithms for Clustering](https://mlsanity.com/paper/2306.02826/) (2023)
- [Quantum computing and artificial intelligence: status and perspectives](https://mlsanity.com/paper/2505.23860/) (2025)
- [Quantum Perceptron Revisited: Computational-Statistical Tradeoffs](https://mlsanity.com/paper/2106.02496/) (2021)
- [Quark: A Gradient-Free Quantum Learning Framework for Classification Tasks](https://mlsanity.com/paper/2210.01311/) (2022)
- [Quantum Fair Machine Learning](https://mlsanity.com/paper/2102.00753/) (2021)
- [Post-variational quantum neural networks](https://mlsanity.com/paper/2307.10560/) (2023)
- [Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithm](https://mlsanity.com/paper/2309.14406/) (2023)
- [Quantum Supervised Learning](https://mlsanity.com/paper/2407.17161/) (2024)
- [Fermi-Dirac machines as quantizations of neurons](https://mlsanity.com/paper/2605.24386/) (2026)
- [Data science and Machine learning in the Clouds: A Perspective for the Future](https://mlsanity.com/paper/2109.01661/) (2021)
- [Discriminating Quantum States with Quantum Machine Learning](https://mlsanity.com/paper/2112.00313/) (2021)
- [Regression and Classification with Single-Qubit Quantum Neural Networks](https://mlsanity.com/paper/2412.09486/) (2024)
- [From Digital Humanities to Quantum Humanities: Potentials and Applications](https://mlsanity.com/paper/2103.11825/) (2021)
- [Image Compression and Classification Using Qubits and Quantum Deep Learning](https://mlsanity.com/paper/2110.05476/) (2021)
- [Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks](https://mlsanity.com/paper/2301.08167/) (2023)
- [Quantum-Enhanced Weight Optimization for Neural Networks Using Grover's Algorithm](https://mlsanity.com/paper/2504.14568/) (2025)
- [Accelerating Inference for Multilayer Neural Networks with Quantum Computers](https://mlsanity.com/paper/2510.07195/) (2025)
- [Quantum Deep Learning Still Needs a Quantum Leap](https://mlsanity.com/paper/2511.01253/) (2025)
- [CQural: A Novel CNN based Hybrid Architecture for Quantum Continual Machine Learning](https://mlsanity.com/paper/2305.09738/) (2023)
- [On Quantum Perceptron Learning via Quantum Search](https://mlsanity.com/paper/2503.17308/) (2025)
- [Canonical quantization of neurons](https://mlsanity.com/paper/2607.05000/) (2026)
- [Bernstein-Vazirani Networks: Quantum Machine Learning by Interference](https://mlsanity.com/paper/2608.19043/) (2026)

## Abstract (arXiv)

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(√N). The second algorithm illustrates how the classical mistake bound of O(frac1γ2) can be further improved to O(frac1√γ) through quantum means, where γ denotes the margin. Such improvements are achieved through the application of quantum amplitude amplification to the version space interpretation of the perceptron model.