Quantum Many-Body Problems: Quantum Machine Learning Applications

Pramoda Medisetty; Veda Manohara Sunanda Vulavalapudi; Poorna Chand Evuru; Mufti Mahmud; Kolla Bhanu Prakash

doi:10.1007/978-3-031-61929-8_12

Quantum Many-Body Problems: Quantum Machine Learning Applications

Pramoda Medisetty
, Veda Manohara Sunanda Vulavalapudi
, Poorna Chand Evuru
, Mufti Mahmud
, Kolla Bhanu Prakash^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A groundbreaking approach has been developed to accurately depict and predict the essential traits of quantum many-body systems. This inventive technique merges quantum ground state computations, classical shadow representations, and sophisticated machine learning approaches. The newly engineered software precisely identifies the ground state of a 2D antiferromagnetic Heisenberg model and forms a classical shadow representation for the quantum state. Machine learning models are deployed to forecast ground-state correlation functions with remarkable accuracy. This pioneering technique shows enormous potential in transforming quantum simulations and quantum machine learning, offering flexible solutions for complex quantum systems.Its implications extend to fields such as material science and drug discovery.

Original language	English
Title of host publication	Proceedings of the International Conference on Internet of Everything and Quantum Information Processing
Editors	Valentina E. Balas, Kolla Bhanu Prakash, G. P. Saradhi Varma
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	79-86
Number of pages	8
ISBN (Print)	9783031619281
DOIs	https://doi.org/10.1007/978-3-031-61929-8_12
State	Published - 2024
Externally published	Yes
Event	International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023 - Vijayawada, India Duration: 24 Nov 2023 → 25 Nov 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1029 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023
Country/Territory	India
City	Vijayawada
Period	24/11/23 → 25/11/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Quantum Computational Tasks
Quantum Ground State
Quantum Machine Learning
Quantum Many-Body Systems
Quantum Simulation
Quantum Wavefunction

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-031-61929-8_12

Cite this

Medisetty, P., Vulavalapudi, V. M. S., Evuru, P. C., Mahmud, M., & Prakash, K. B. (2024). Quantum Many-Body Problems: Quantum Machine Learning Applications. In V. E. Balas, K. B. Prakash, & G. P. S. Varma (Eds.), Proceedings of the International Conference on Internet of Everything and Quantum Information Processing (pp. 79-86). (Lecture Notes in Networks and Systems; Vol. 1029 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-61929-8_12

Medisetty, Pramoda ; Vulavalapudi, Veda Manohara Sunanda ; Evuru, Poorna Chand et al. / Quantum Many-Body Problems : Quantum Machine Learning Applications. Proceedings of the International Conference on Internet of Everything and Quantum Information Processing. editor / Valentina E. Balas ; Kolla Bhanu Prakash ; G. P. Saradhi Varma. Springer Science and Business Media Deutschland GmbH, 2024. pp. 79-86 (Lecture Notes in Networks and Systems).

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abstract = "A groundbreaking approach has been developed to accurately depict and predict the essential traits of quantum many-body systems. This inventive technique merges quantum ground state computations, classical shadow representations, and sophisticated machine learning approaches. The newly engineered software precisely identifies the ground state of a 2D antiferromagnetic Heisenberg model and forms a classical shadow representation for the quantum state. Machine learning models are deployed to forecast ground-state correlation functions with remarkable accuracy. This pioneering technique shows enormous potential in transforming quantum simulations and quantum machine learning, offering flexible solutions for complex quantum systems.Its implications extend to fields such as material science and drug discovery.",

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note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023 ; Conference date: 24-11-2023 Through 25-11-2023",

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doi = "10.1007/978-3-031-61929-8\_12",

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series = "Lecture Notes in Networks and Systems",

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Medisetty, P, Vulavalapudi, VMS, Evuru, PC, Mahmud, M & Prakash, KB 2024, Quantum Many-Body Problems: Quantum Machine Learning Applications. in V E. Balas, KB Prakash & GPS Varma (eds), Proceedings of the International Conference on Internet of Everything and Quantum Information Processing. Lecture Notes in Networks and Systems, vol. 1029 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 79-86, International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023, Vijayawada, India, 24/11/23. https://doi.org/10.1007/978-3-031-61929-8_12

Quantum Many-Body Problems: Quantum Machine Learning Applications. / Medisetty, Pramoda; Vulavalapudi, Veda Manohara Sunanda; Evuru, Poorna Chand et al.
Proceedings of the International Conference on Internet of Everything and Quantum Information Processing. ed. / Valentina E. Balas; Kolla Bhanu Prakash; G. P. Saradhi Varma. Springer Science and Business Media Deutschland GmbH, 2024. p. 79-86 (Lecture Notes in Networks and Systems; Vol. 1029 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Medisetty P, Vulavalapudi VMS, Evuru PC, Mahmud M, Prakash KB. Quantum Many-Body Problems: Quantum Machine Learning Applications. In E. Balas V, Prakash KB, Varma GPS, editors, Proceedings of the International Conference on Internet of Everything and Quantum Information Processing. Springer Science and Business Media Deutschland GmbH. 2024. p. 79-86. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-61929-8_12

Quantum Many-Body Problems: Quantum Machine Learning Applications

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this