SYNERGY BETWEEN QUANTUM COMPUTING AND MACHINE LEARNING IN QUANTUM NEURAL NETWORK

Sadia  Rafi; Rana Muhammad  Ali Maisam; Anum  Zahid; Muhammad Haroon  Yaqoob; Shoaib  Ajmal; Adnan  Azam

Sadia Rafi School of Natural Sciences, National University of Sciences and Technology
Rana Muhammad Ali Maisam Department of Physics, COMSATS University Islamabad, Lahore, Pakistan
Anum Zahid Department of Physics, COMSATS University Islamabad, Lahore, Pakistan
Muhammad Haroon Yaqoob Department of Physics, COMSATS University Islamabad, Lahore, Pakistan
Shoaib Ajmal Department of Physics, COMSATS University Islamabad, Lahore, Pakistan
Adnan Azam Department of solid-state physics Punjab university

Keywords: Machine learning

Abstract

Machine learning has made significant contributions to the fields of chemistry and materials science, enabling the exploration of vast chemical space through large-scale quantum chemical calculations. These models provide fast and accurate predictions of atomistic chemical properties, but they have limitations when it comes to capturing the electronic degrees of freedom of a molecule. This restricts their application in reactive chemistry and chemical analysis. To address this limitation, we introduce a deep learning framework that predicts the quantum mechanical wavefunction of a molecule in a local basis of atomic orbitals. The wavefunction serves as a foundational representation from which all other ground-state properties can be derived. Our approach maintains complete access to the electronic structure through the wavefunction, while achieving computational efficiency comparable to force-field methods. Moreover, the framework captures quantum mechanics in a form that can be analytically differentiated, allowing for efficient optimization and exploration of chemical systems. We demonstrate the potential of our approach through several examples. By leveraging the predicted wavefunction, we showcase the ability to perform inverse design of molecular structures to target specific electronic property optimizations. This opens exciting avenues for tailoring molecular structures to achieve desired electronic characteristics. Additionally, our framework paves the way for enhanced synergy between machine learning and quantum chemistry, enabling more comprehensive investigations into complex chemical systems.

References

1. Bartók, A. P., et al. (2010). "Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons." Physical review letters 104(13): 136403.
2. Behler, J. and M. J. P. r. l. Parrinello (2007). "Generalized neural-network representation of high-dimensional potential-energy surfaces." Physical review letters 98(14): 146401.
3. Bergholm, V., et al. (2018). "Pennylane: Automatic differentiation of hybrid quantum-classical computations."
4. Boutet, A., et al. (2021). "Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning." Nature communications 12(1): 3043.
5. Braams, B. J. and J. M. J. I. R. i. P. C. Bowman (2009). "Permutationally invariant potential energy surfaces in high dimensionality." International Reviews in Physical Chemistry 28(4): 577-606.
6. Carleo, G. and M. J. S. Troyer (2017). "Solving the quantum many-body problem with artificial neural networks." Science 355(6325): 602-606.
7. Chen, M., et al. (2019). "Federated learning of n-gram language models."
8. Cheng, L., et al. (2019). "A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules." The Journal of chemical physics 150(13).
9. Chmiela, S., et al. (2018). "Towards exact molecular dynamics simulations with machine-learned force fields." Nature communications 9(1): 3887.
10. Chmiela, S., et al. (2017). "Machine learning of accurate energy-conserving molecular force fields." Science advances 3(5): e1603015.
11. De Andrade, D. C., et al. (2018). "A neural attention model for speech command recognition."
12. Farhi, E., et al. (2000). "Quantum computation by adiabatic evolution."
13. Grisafi, A., et al. (2018). "Symmetry-adapted machine learning for tensorial properties of atomistic systems." Physical review letters 120(3): 036002.
14. Havlíček, V., et al. (2019). "Supervised learning with quantum-enhanced feature spaces." Nature 567(7747): 209-212.
15. Henderson, M., et al. (2020). "Quanvolutional neural networks: powering image recognition with quantum circuits." Quantum Machine Intelligence 2(1): 2.
16. Henderson, M., et al. (2020). "Quanvolutional neural networks: powering image recognition with quantum circuits." Quantum Machine Intelligence 2(1): 2.
17. Hochreiter, S. and J. J. N. c. Schmidhuber (1997). "Long short-term memory." 9(8): 1735-1780.
18. Jha, D., et al. (2018). "Elemnet: Deep learning the chemistry of materials from only elemental composition." Scientific reports 8(1): 17593.
19. Li, F., et al. (2002). Quantum neural network in speech recognition. 6th International Conference on Signal Processing, 2002., IEEE.
20. Li, H., et al. (2018). "A density functional tight binding layer for deep learning of chemical Hamiltonians." Journal of chemical theory computation 14(11): 5764-5776.
21. Mitarai, K., et al. (2018). "Quantum circuit learning." Physical Review A 98(3): 032309.
22. Qi, J., et al. (2020). Submodular rank aggregation on score-based permutations for distributed automatic speech recognition. ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.
23. Raju, A., et al. (2019). "Scalable multi corpora neural language models for asr."
24. Rupp, M., et al. (2012). "Fast and accurate modeling of molecular atomization energies with machine learning." Physical review letters 108(5): 058301.
25. Ryczko, K., et al. (2019). "Deep learning and density-functional theory." Physical Review A 100(2): 022512.
26. Smith, J. S., et al. (2017). "ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost." Chemical science 8(4): 3192-3203.
27. Sugawara, M. J. C. P. C. (2001). "Numerical solution of the Schrödinger equation by neural network and genetic algorithm." Computer Physics Communications 140(3): 366-380.
28. Von Lilienfeld, O. A. J. A. C. I. E. (2018). "Quantum machine learning in chemical compound space." Angewandte Chemie International Edition 57(16): 4164-4169.
29. Warden, P. J. a. p. a. (2018). "Speech commands: A dataset for limited-vocabulary speech recognition."
30. Welborn, M., et al. (2018). "Transferability in machine learning for electronic structure via the molecular orbital basis." Journal of chemical theory computation 14(9): 4772-4779.
31. Yang, C.-H. H., et al. (2021). Decentralizing feature extraction with quantum convolutional neural network for automatic speech recognition. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.
32. Yang, Q., et al. (2019). "Federated machine learning: Concept and applications." ACM Transactions on Intelligent Systems Technology 10(2): 1-19.