• Corpus ID: 239049957

Learning quantum dynamics with latent neural ODEs

@article{Choi2021LearningQD,
  title={Learning quantum dynamics with latent neural ODEs},
  author={Matthew Choi and Daniel Flam-Shepherd and Thi Ha Kyaw and Al{\'a}n Aspuru‐Guzik},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.10721}
}
Matthew Choi, ∗ Daniel Flam-Shepherd, 2, ∗ Thi Ha Kyaw, 3, † and Alán Aspuru-Guzik 2, 3, 4, ‡ Department of Computer Science, University of Toronto, Toronto, Ontario M5S 2E4, Canada Vector Institute for Artificial Intelligence, Toronto, Ontario M5S 1M1, Canada Department of Chemistry, University of Toronto, Toronto, Ontario M5G 1Z8, Canada Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada (Dated: October 22, 2021) 

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References

SHOWING 1-10 OF 51 REFERENCES
Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
TLDR
This paper presents a probabilistic procedure for estimating the E-modulus of the Higgs boson in the presence of X-ray diffraction.
Neural Ordinary Differential Equations
TLDR
This work shows how to scalably backpropagate through any ODE solver, without access to its internal operations, which allows end-to-end training of ODEs within larger models.
Hamiltonian Neural Networks
TLDR
Inspiration from Hamiltonian mechanics is drawn to train models that learn and respect exact conservation laws in an unsupervised manner, and this model trains faster and generalizes better than a regular neural network.
Using a Recurrent Neural Network to Reconstruct Quantum Dynamics of a Superconducting Qubit from Physical Observations
TLDR
It is demonstrated that a recurrent neural network can be trained in real time to infer the individual quantum trajectories associated with the evolution of a superconducting qubit under unitary evolution, decoherence and continuous measurement from raw observations only.
Multiparameter optimisation of a magneto-optical trap using deep learning
TLDR
A deep artificial neural network is implemented to optimise the magneto-optic cooling and trapping of neutral atomic ensembles, showing an improvement in the resulting resonant optical depth compared to more traditional solutions.
Identifying quantum phase transitions using artificial neural networks on experimental data
TLDR
This work employs an artificial neural network and deep-learning techniques to identify quantum phase transitions from single-shot experimental momentum-space density images of ultracold quantum gases and obtains results that were not feasible with conventional methods.
Unboxing Quantum Black Box Models: Learning Non-Markovian Dynamics
TLDR
This approach provides the physical interpretability that machine learning and opaque superoperators lack and is aware of the underlying continuous dynamics typically disregarded by superoperator-based tomography.
Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow
TLDR
It is demonstrated for the first time that dense 3D+time functions of physics system can be predicted within the latent spaces of neural networks, and the method arrives at a neural‐network based simulation algorithm with significant practical speed‐ups.
Auto-Encoding Variational Bayes
TLDR
A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
Neural-network quantum state tomography
TLDR
It is demonstrated that machine learning allows one to reconstruct traditionally challenging many-body quantities—such as the entanglement entropy—from simple, experimentally accessible measurements, and can benefit existing and future generations of devices.
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