mlsanityData to 14 Oct 2026

Input Redundancy for Parameterized Quantum Circuits

arXiv 1901.11434 ↗First posted 31 Jan 2019quant-ph cs.LGcited by 33

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Abstract (arXiv)

The topic area of this paper parameterized quantum circuits (quantum neural networks) which are trained to estimate a given function, specifically the type of circuits proposed by Mitarai et al. (Phys. Rev. A, 2018). The input is encoded into amplitudes of states of qubits. The no-cloning principle of quantum mechanics suggests that there is an advantage in redundantly encoding the input value several times. We follow this suggestion and prove lower bounds on the number of redundant copies for two types of input encoding. We draw conclusions for the architecture design of QNNs.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Jan 2019.

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Citing papers per year: 2021: 4; 2022: 6; 2023: 12; 2024: 4; 2025: 7.

33 papers.

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  1. 2021Generalization in quantum machine learning from few training datacited by 216
  2. 2021Supervised quantum machine learning models are kernel methodscited by 200
  3. 2021Encoding-dependent generalization bounds for parametrized quantum circuitscited by 82
  4. 2022Classical surrogates for quantum learning modelscited by 39
  5. 2023Quantum Recurrent Neural Networks for Sequential Learningcited by 25
  6. 2023Quantum Deep Hedgingcited by 22
  7. 2023Let Quantum Neural Networks Choose Their Own Frequenciescited by 22
  8. 2023Potential and limitations of random Fourier features for dequantizing quantum machine learningcited by 22
  9. 2023Training robust and generalizable quantum modelscited by 21
  10. 2022High Dimensional Quantum Machine Learning With Small Quantum Computerscited by 18
  11. 2023Reduction of finite sampling noise in quantum neural networkscited by 13
  12. 2024A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Modelscited by 12
21 more
  1. 2025A Primer on Quantum Machine Learningcited by 12
  2. 2023Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuitscited by 10
  3. 2025Artificial intelligence for representing and characterizing quantum systemscited by 10
  4. 2022Overfitting in quantum machine learning and entangling dropoutcited by 9
  5. 2021A Leap among Quantum Computing and Quantum Neural Networks: A Surveycited by 8
  6. 2022Quantum neural network autoencoder and classifier applied to an industrial case studycited by 8
  7. 2023Expressive variational quantum circuits provide inherent privacy in federated learningcited by 8
  8. 2023Symmetry-invariant quantum machine learning force fieldscited by 8
  9. 2025Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolvercited by 7
  10. 2025Layerwise Federated Learning for Heterogeneous Quantum Clients using Quoruscited by 5
  11. 2022Quantum neural networks force fields generationcited by 4
  12. 2023BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploadingcited by 3
  13. 2023Post-variational quantum neural networkscited by 3
  14. 2024Spectral invariance and maximality properties of the frequency spectrum of quantum neural networkscited by 3
  15. 2024Concept learning of parameterized quantum models from limited measurementscited by 3
  16. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  17. 2022Expressibility-Enhancing Strategies for Quantum Neural Networkscited by 1
  18. 2023Fourier series weight in quantum machine learningcited by 0
  19. 2024The role of data-induced randomness in quantum machine learning classification taskscited by 0
  20. 2025On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Modelscited by 0
  21. 2025Fisher Information, Training and Bias in Fourier Regression Modelscited by 0