mlsanityData to 14 Oct 2026

Quantum Perceptron Models

arXiv 1602.04799 ↗First posted 15 Feb 2016quant-ph cs.LG stat.MLcited by 28

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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.

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

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Citing papers per year: 2021: 9; 2022: 2; 2023: 7; 2024: 2; 2025: 5; 2026: 3.

28 papers.

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  1. 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43
  2. 2023Simulation of a Variational Quantum Perceptron using Grover's Algorithmcited by 12
  3. 2021A Leap among Quantum Computing and Quantum Neural Networks: A Surveycited by 8
  4. 2022Experimental quantum pattern recognition in IBMQ and diamond NVscited by 8
  5. 2021Quantum Machine Learning for Financecited by 7
  6. 2023Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Gamescited by 6
  7. 2023Near-Optimal Quantum Coreset Construction Algorithms for Clusteringcited by 6
  8. 2025Quantum computing and artificial intelligence: status and perspectivescited by 6
  9. 2021Quantum Perceptron Revisited: Computational-Statistical Tradeoffscited by 5
  10. 2022Quark: A Gradient-Free Quantum Learning Framework for Classification Taskscited by 4
  11. 2021Quantum Fair Machine Learningcited by 3
  12. 2023Post-variational quantum neural networkscited by 3
16 more
  1. 2023Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithmcited by 3
  2. 2024Quantum Supervised Learningcited by 3
  3. 2026Fermi-Dirac machines as quantizations of neuronscited by 3
  4. 2021Data science and Machine learning in the Clouds: A Perspective for the Futurecited by 2
  5. 2021Discriminating Quantum States with Quantum Machine Learningcited by 2
  6. 2024Regression and Classification with Single-Qubit Quantum Neural Networkscited by 2
  7. 2021From Digital Humanities to Quantum Humanities: Potentials and Applicationscited by 1
  8. 2021Image Compression and Classification Using Qubits and Quantum Deep Learningcited by 1
  9. 2023Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networkscited by 1
  10. 2025Quantum-Enhanced Weight Optimization for Neural Networks Using Grover's Algorithmcited by 1
  11. 2025Accelerating Inference for Multilayer Neural Networks with Quantum Computerscited by 1
  12. 2025Quantum Deep Learning Still Needs a Quantum Leapcited by 1
  13. 2023CQural: A Novel CNN based Hybrid Architecture for Quantum Continual Machine Learningcited by 0
  14. 2025On Quantum Perceptron Learning via Quantum Searchcited by 0
  15. 2026Canonical quantization of neuronscited by 0
  16. 2026Bernstein-Vazirani Networks: Quantum Machine Learning by Interferencecited by 0