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Neural Controlled Differential Equations with Quantum Hidden Evolutions

arXiv 2404.19673 ↗First posted 30 Apr 2024cs.LGcited by 0

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Abstract (arXiv)

We introduce a class of neural controlled differential equation inspired by quantum mechanics. Neural quantum controlled differential equations (NQDEs) model the dynamics by analogue of the Schr"odinger equation. Specifically, the hidden state represents the wave function, and its collapse leads to an interpretation of the classification probability. We implement and compare the results of four variants of NQDEs on a toy spiral classification problem.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 30 references that are in the map. First posted on arXiv 30 Apr 2024.

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References · 24

  1. 2018Neural Ordinary Differential Equationscited by 3,733
  2. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  3. 2022Simplified State Space Layers for Sequence Modelingcited by 776
  4. 2021Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layerscited by 607
  5. 2020Neural Controlled Differential Equations for Irregular Time Seriescited by 501
  6. 2015Unitary Evolution Recurrent Neural Networkscited by 291
  7. 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 230
  8. 2019Neural Jump Stochastic Differential Equationscited by 147
  9. 2020Neural Rough Differential Equations for Long Time Seriescited by 140
  10. 2019Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Groupcited by 133
  11. 2021Modeling Irregular Time Series with Continuous Recurrent Unitscited by 101
  12. 2019Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noisecited by 98
12 more
  1. 2019Trivializations for Gradient-Based Optimization on Manifoldscited by 78
  2. 2021Efficient and Accurate Gradients for Neural SDEscited by 68
  3. 2021Neural ODE Processescited by 57
  4. 2021UnICORNN: A recurrent model for learning very long time dependenciescited by 56
  5. 2021Neural Controlled Differential Equations for Online Prediction Taskscited by 46
  6. 2021Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamicscited by 46
  7. 2021Heavy Ball Neural Ordinary Differential Equationscited by 42
  8. 2020The universal approximation theorem for complex-valued neural networkscited by 28
  9. 2022projUNN: efficient method for training deep networks with unitary matricescited by 27
  10. 2020Stochasticity in Neural ODEs: An Empirical Studycited by 13
  11. 2023Theory and Implementation of Complex-Valued Neural Networkscited by 11
  12. 2019Complex Evolution Recurrent Neural Networks (ceRNNs)cited by 6

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