Hello, I’m Vinit!

I am a Ph.D. candidate at Purdue University, West Lafayette, advised by Prof. Sabre Kais, studying quantum many-body systems using Simulations, Quantum Computation, and Machine Learning. I have been working with Tensor Networks to understand the behavior of quantum entanglement in higher dimensions and arbitrary graphs. I strive to find emergent quantum phases of matter and study their phase transitions.

Research Interests

  • Quantum Computation
  • Machine Learning
  • Tensor Networks
  • Quantum Hamiltonian Complexity
  • Condensed Matter Physics
  • High-Performance Computing

Portfolio

Tensor Networks and AGSP

A study of the evolution of a quantum state under the action of an Approximate Ground State Projector.

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Tensor Networks and AGSP

Tensor Networks, Quantum Information

Supervisor
Umesh Vazirani and Zeph Landau
Duration
May - Aug 2021
Github
Github

A study of the evolution of a quantum state under the action of an Approximate Ground State Projector (AGSP). We are performing direct analysis to bound the worst case of growth of entanglement rank and entropy which might lead to area laws in higher dimensions or improve the bounds of the subvolume laws. In the project, we are using GPU-accelerated Tensor Network architecture by combining MPNUM and CuPy.

Machine Learning Skyrmion

Detecting Skyrmions in microscopic images using machine learning.

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Machine Learning Skyrmion

Condensed Matter & Materials Physics, Machine Learning, Convolutional Neural Networks, Ferromagnets, Skyrmions

Supervisors
Jung Hoon Han
Duration
May 2018 - April 2019
Paper
Physical Review B

Principles of Machine Learning are applied to physical models that support skyrmion phases in two dimensions. Using Convolutional Neural Network successful predictions on various phases of the skyrmion model were obtained. The training scheme based on features of the input configuration makes reliable prediction of magnetization, spin chirality, magnetic field and temperature.

Quantum Machine Learning

Determine mapping between X-ray scattering cross-section and molecular coordinates of N-Methyl-Morpholine.

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Quantum Machine Learning

Quantum Computation, Quantum Machine Learning, Convolutional Neural Networks

Supervisors
Brenda Rubenstein
Duration
April - Aug 2020
Weblink
GitHub

A study of Novel application of Quantum Neural Networks on the X-ray scattering data of N-Methyl-Morpholine. The Classical-Quantum Hybrid Machine Learning Algorithm was implemented on Tensorflow-Quantum. The project also involved building of a state preparation scheme on Qiskit and Cirq.

Inverse Swarm Problem

Determine the scattering cross-section of real gases from transport data using Machine Learning.

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Inverse Swarm Problem

Atomic and Molecular Physics, Statistical Mechanics, Machine Learning

Supervisors
Daniels Cocks, James Sullivan, and Joshua Machacek
Duration
May 2019 - July 2019
Web Link
Github

Application of Mixture Density Networks for Constraining Cross Sections from Swarm Measurements. The Swarm problem was simulated with Boltzmann Equations using Numerical Methods and Parallel Algorithms. We used Variational Autoencoder to find the critical features characterizing the transport coefficients and cross-sections. Recurrent Neural Network was used to capture the sequence to sequence mapping of the Inverse Swarm Problem.

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Research Experience

Aug 2021 - Present

Research Intern

Demystifying Quantum Machine Learning (QML) model using Information scrambling and Out-of-time correlators (OTOC).

Supervisor: Sabre Kais
Jun - Aug 2024

Research Intern

I developed a global gate ansatz with long-range entanglement and optimized its expressibility and trainability. I successfully simulated the ground state of the Toric code Hamiltonian and benchmarked the new ansatz against existing quantum algorithms.

Supervisor: Bin Yan and Lukasz Cincio
Apr - Aug 2021

Research Intern

A study of the evolution of a quantum state under the action of an Approximate Ground State Projector.

Supervisor: Umesh Vazirani and Zeph Landau
Sep 2020 - Apr 2021

Master's Thesis

Simulation of quantum many-body systems using Tensor Network algorithms.

Supervisor: Arghya Taraphder
Apr - Aug 2020

Research Intern

A study of a novel application of Quantum Convolutional Neural Networks on the X-ray scattering data of N-Methyl-Morpholine.

Supervisor: Brenda Rubenstein
May - Jul 2019

Future Research Talent Travel Award

As an FRT (Future Research Talent Travel Award) scholar, I participated in the project titled ‘Inverse Swarm Problems with Neural Networks’ in the Plasma Research Laboratory at the Department of Physics and Engineering, ANU.

Supervisor: Daniels Cocks, James Sullivan, Joshua Machacek
May - Jul 2018

Research Intern

Explored a new idea of applications of Machine Learning in the field of Condensed Matter Physics to detect the presence of Skyrmions in the images obtained from a Scanning Tunneling Microscope.

Supervisor: Prof. Jung Hoon Han

Education

2021-Present

Purdue University

Ph.D. in Chemistry
2016-2021

Indian Institute of Technology Kharagpur

M.Sc. in Physics

B.Sc. in Physics

Minor in Computer Science

Publications

Papers

  • PhysRevB  "Physics-Inspired Quantum Simulation of Resonating Valence Bond States: A Prototypical Template for a Spin-Liquid Ground State" - Paper

    The Journal of Physical Chemistry A 127 (41), 8751-8764

    M Sajjan, R Gupta, SS Kale, V Singh, K Kumaran, S Kais

  • PhysRevB  "Application of machine learning to two-dimensional Dzyaloshinskii-Moriya ferromagnets" - Paper

    Physical Review Research 5 (1), 013146

    M Sajjan, V Singh, R Selvarajan, S Kais

  • PhysRevB  "Application of machine learning to two-dimensional Dzyaloshinskii-Moriya ferromagnets" - Paper

    Published: Phys. Rev. B 99, 174426 – 23 May 2019

    Vinit Kumar Singh and Jung Hoon Han

  • PhysRevB  "Application of machine learning to two-dimensional Dzyaloshinskii-Moriya ferromagnets" - Paper

    Published: Phys. Rev. B 99, 174426 – 23 May 2019

    Vinit Kumar Singh and Jung Hoon Han

Conferences

  • arXiv   ”Statistical Recovery of the Classical Spin Hamiltonian” - arXiv

    Submitted on 13 Jul 2018

    Vinit Kumar Singh and Jung Hoon Han

Preprints

  • arXiv   ”Statistical Recovery of the Classical Spin Hamiltonian” - arXiv

    Submitted on 13 Jul 2018

    Vinit Kumar Singh and Jung Hoon Han

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