mlsanityData to 7 Oct 2026

An Introduction to Quantum Machine Learning for Engineers

arXiv 2205.09510 ↗First posted 11 May 2022quant-ph cs.ET cs.LGcited by 22

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Abstract (arXiv)

In the current noisy intermediate-scale quantum (NISQ) era, quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. In quantum machine learning, the gates of a quantum circuit are parametrized, and the parameters are tuned via classical optimization based on data and on measurements of the outputs of the circuit. Parametrized quantum circuits (PQCs) can efficiently address combinatorial optimization problems, implement probabilistic generative models, and carry out inference (classification and regression). This monograph provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra. It first describes the necessary background, concepts, and tools necessary to describe quantum operations and measurements. Then, it covers parametrized quantum circuits, the variational quantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 2 of its 25 references that are in the map. First posted on arXiv 11 May 2022.

Builds on

2 of 25 references.

References · 2

  1. 2018Barren plateaus in quantum neural network training landscapescited by 624
  2. 2019Monte Carlo Gradient Estimation in Machine Learningcited by 281

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 3; 2024: 5; 2025: 7; 2026: 1.

22 papers.

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  1. 2023Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learningcited by 11
  2. 2023Statistical Complexity of Quantum Learningcited by 11
  3. 2022Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysiscited by 6
  4. 2022Quantum Split Neural Network Learning using Cross-Channel Poolingcited by 4
  5. 2022Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Codingcited by 4
  6. 2025Dynamic Estimation Loss Control in Variational Quantum Sensing via Online Conformal Inferencecited by 4
  7. 2024Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysiscited by 3
  8. 2024Neuromorphic Wireless Split Computing with Multi-Level Spikescited by 3
  9. 2024QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learningcited by 3
  10. 2025Adaptive Bayesian Single-Shot Quantum Sensingcited by 2
  11. 2022Learning Quantum Entanglement Distillation with Noisy Classical Communicationscited by 1
  12. 2022Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channelscited by 1
10 more
  1. 2024Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networkscited by 1
  2. 2025On the Generalization of Adversarially Trained Quantum Classifierscited by 1
  3. 2025Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learningcited by 1
  4. 2025Variational Quantum Integrated Sensing and Communicationcited by 1
  5. 2022Quantum Machine Learning for Distributed Quantum Protocols with Local Operations and Noisy Classical Communicationscited by 0
  6. 2023Variational Quantum Eigensolver with Constraints (VQEC): Solving Constrained Optimization Problems via VQEcited by 0
  7. 2024QMViT: A Mushroom is worth 16x16 Wordscited by 0
  8. 2025Conservative quantum offline model-based optimizationcited by 0
  9. 2025Adaptive Conformal Prediction for Quantum Machine Learningcited by 0
  10. 2026Quantum Random Forest for the Regression Problemcited by 0