About

Hi! I am a postdoc at Jefferson Lab working on various aspects of quantum computing, utilizing both qubit-based and qumode-based approaches to universal quantum computing. The US Department of Energy's (DOE) established research centers fund this position through C2QA, one of the five Quantum Information Science (QIS) centers. The goal is to work toward quantum advantage in nuclear physics, chemistry, materials science, and condensed matter physics, among many others. Before this position, I spent three years at the Perimeter Institute for Theoretical Physics in Waterloo, Canada, where my mentor was Pedro Vieira. In 2019, I completed my Ph.D. from Syracuse University and worked with Simon Catterall on various aspects of lattice field theory, especially its application to the thermodynamics of black holes in supergravity.

I grew up in different states of India (Darbhanga, Bihar during 1993-1999, Delhi from 1999 until 2001, and Sonipat, Haryana, from 2001 until 2007) and studied for my Bachelors degree in Physics at St. Stephen's College, Delhi while being a KVPY (highly competitive program funded by the Department of Science and Technology of the Government of India) scholar. In 2010, I received a one-year Erasmus scholarship from the European Union to study for an MS at the University of Paris (now Sorbonne University). I did my Masters thesis research on trilayer graphene using ab-initio density functional theory calculations using Quantum ESPRESSO. I then moved back to India and completed another Masters in Astroparticle Physics from Bose Institute and St. Xavier's College (2011-2013) and wrote my MSc thesis research on Monte Carlo methods for path integral approach to quantum field theories (QFTs). The Government of India awarded me the CSIR/UGC fellowship in my final year of MSc which I had to decline since I moved to the US. In 2013, I joined the physics department at Syracuse University and obtained my PhD in 2019 on aspects of holography, large N, and lattice supersymmetry. Later that year, I moved to Waterloo, Canada, for my first postdoc and returned to the US for my second postdoc in 2022.

Research

For Students: If you'd like to work on a research project, please contact me. Until I get a position, I cannot write a reference letter for your future applications. If you are still interested, I'd love to talk and discuss potential projects. In my research, I am broadly interested in various problems related to lattice gauge theory, tensor networks, qubits and qumodes approach to quantum computing, matrix models, computational complexity theory, variational algorithms like conventional VQE and d-sparse VQE, Hamiltonian simulation, and quantum chaos in random systems.
In my PhD, I focused exclusively on supersymmetric gauge theories as non-perturbative formulations of string theory through gauge/gravity duality especially finite-temperature maximally supersymmetric gauge theories using Monte Carlo (MC) methods to test and understand non-extremal black p-branes in dual supergravity (SUGRA). This numerical approach provides a tool for potential non-trivial checks of the AdS/CFT conjecture and explore away from the classical SUGRA and planar limit.

During my PhD, I started exploring the tensor network renormalization group methods to study lower-dimensional gauge theories and spin models with continuous or discrete symmetries. Since the tensor network methods are computationally expensive in higher dimensions, one of my other recent interests is also exploring different algorithms which will enable us to study a wide range of statistical models with sufficient accuracy. A long-term goal is to compute critical exponents in 3d models by going close to the QFT limit and comparing it to the results from the well-established conformal bootstrap program and MC method. Another direction is to study models that are affected by sign problem in conventional Monte Carlo methods, such as those at finite chemical potential or with a topological term.

My focus at the moment is on available/near-term quantum devices and applications of quantum computation to quantum many-body problems. My research focuses on continuous variable (CV) approach to quantum computing and information but I also work on qubit based algorithms and simulation using both noisy and fault-tolerant algorithms. In the CV approach, unlike the qubits, the quantum information and unitary transformations are written in terms of bosonic operators which have infinite-dimensional Hilbert space (suitably truncated). I wrote a small review several years ago, please refer to it on arXiv -- here . The material in this article is based on lectures given at Rensselaer Polytechnic Institute (RPI) Summer School in June 2022, Hampton University Graduate Studies (HUGS) program and Quantum Computing Bootcamp at Jefferson Lab in June 2023.

Around the World

Sinquerim Beach, Goa, India
Miyajima Island, Japan
Athens, Greece
Jaco, Costa Rica
Santorini, Greece
Porto, Portugal
Chicago, USA
Dublin, Ireland
Maracas Bay, Trinidad and Tobago
Maldives
New York City, USA
Prague, Czech Republic
Lagos, Portugal
Boulder, USA
Whiteface Mountains, USA

Notes and Learning Resources

Talks

(see CV for detailed description and PDFs)

  1. State preparation and operator growth of SYK model on IBM quantum computer (November 17, 2024) - Tensor Network 2024 workshop, Ishikawa, Japan [Online]
  2. Thermal state preparation and dynamics of random all-to-all fermionic model (July 17, 2024) - Talk at Massachusetts Institute of Technology (MIT), C2QA meeting, Boston, USA
  3. Introduction to tensor networks (29-30 April, 2 May 2024) - University of Pretoria, South Africa
  4. Quantum computing for quantum many-body systems (17 April, 2024) - William & Mary, VA, USA
  5. Approaches to universal quantum computing for spin and gauge models (16 April, 2024) - University of Iowa
  6. Random dense Hamiltonians on current noisy quantum computers (28 March, 2024) - University of Maryland
  7. Extracting some Physics with IBM's 127-qubit quantum processor (13 March, 2024) - Jefferson Lab
  8. Real-time dynamics of SYK model on a noisy quantum computer (05 March, 2024) - Workshop on 'Toward quantum simulation of gauge/gravity duality and lattice gauge theory', Queen Mary University of London (Online) (PDF)
  9. SYK model on a noisy quantum computer (06 February, 2024) - Indian Institute of Science (IISc), Bangalore, India (PDF)
  10. Quantum Computation of the O(3) model using qumodes (02 August, 2023) - Lattice 2023, Fermilab, USA
  11. Computation with Quantum Mechanics (June 20, 2023) - Set of two lectures at Quantum Computation Bootcamp, Jefferson Lab, USA
  12. Can quantum computation improve our understanding of quantum fields? (June 7, 2023) - Set of two lectures at HUGS 2023 Summer School, Jefferson Lab, USA
  13. Non-linear sigma models using quantum computation (May 30, 2023) at C2QA Meeting, New York City, USA
  14. Introduction to Quantum Computing methods in Physics (April 27, 2023) at Tata Institute, Mumbai, India (Online) (PDF)
  15. Aspects of Classical and Quantum Computing of Quantum Many-Body Systems (February 10, 2023) at Ashoka University (Online) (PDF)
  16. Classical computation using tensor networks and quantum computation with qubits and qumodes (November 14, 2022) at Jefferson Lab, USA
  17. Application of tensor methods to real-space renormalization and real-time study of field theories (October 31, 2022) at Brookhaven National Lab, USA (Online)
  18. New tools for old problems in spin and gauge models on the lattice (October 12, 2022) at IIT Hyderabad, India (Online)
  19. Some old problems on the lattice using tensors (August 26, 2022) at NUMSTRINGS 2022 conference at ICTS, India
  20. Introduction to Quantum Computation using QISKIT (June 21 and 22, 2022) at Rensselaer Polytechnic Institute, Troy, USA (Online)  
  21. New approach to continuous spin models in two and three dimensions (May 17, 2022) at APTCP, Pohang, South Korea (Online)  
  22. Holography with large matrices on the lattice (March 24, 2022) at UNAM, Mexico City, Mexico  
  23. Large N matrix models using Monte Carlo and Bootstrap (February 22, 2022) at University of Surrey, UK (Online)  
  24. Introduction to tensor networks and spin systems (January 11, 2022) at Azim Premji University, Bengaluru, India (Online)  
  25. Tensor networks and spin models (December 7, 2021) - at Indian Institute of Science Education and Research (IISER), Mohali, India (Online) (PDF)
  26. Real-space tensor renormalization for spin models in three dimensions - November 19, 2021 at Perimeter Institute 
  27. Solving matrix models at large and finite N (June 28 and 29, 2021) - Two lectures for Summer School 2021 at Rensselaer Polytechnic Institute, USA (Online due to COVID-19 pandemic) (PDF)
  28. Holographic gauge theories on the lattice - June 23, 2021 at Dublin Institute for Advanced Studies, Dublin, Ireland (Online via Zoom due to COVID-19 pandemic) (PDF)
  29. Old and new methods for new and old problems in Physics - March 8, 2021 at Indian Institute of Technology (IIT) Madras (Online via Zoom due to COVID-19 pandemic) (PDF)
  30. Probing holographic dualities with lattice supersymmetric Yang-Mills theories - February 25, 2021 at Massachusetts Institute of Technology (Online via Zoom due to COVID-19 pandemic) (PDF) (YouTube)
  31. New tool for old problems — Tensor network approach to spin models and gauge theories - October 14, 2020 at University of Liverpool, UK (Online via Zoom due to COVID-19 pandemic) (PDF)
  32. Tensor Networks: Algorithm & Applications — June 10 and 11, 2020 – Two lectures [1.5 hours each] for CyberTraining Summer School 2020 at Rensselaer Polytechnic Institute, USA (Online due to COVID-19 pandemic) (PDF)
  33. Holographic aspects of supersymmetric gauge theories – October 4, 2019 - Perimeter Institute
  34. Numerical Approaches to Holography — August 28, 2019 - Seminar at Ashoka University, Sonipat, India (PDF)
  35. Numerical Approaches to Holography — August 08, 2019 - Seminar at Indian Institute of Science Education and Research (IISER), Mohali, India
  36. Holography, large $N$, and supersymmetry on the lattice — April 02, 2019 - Ph.D. thesis defense (PDF)
  37. Fundamentals of Quantum Entropy — March 29, 2019
  38. Holographic dualities and tensor renormalization group study of gauge theories — March 11, 2019 - Interdisciplinary Quantum Fields and Strings + Tensor Networks Initiative invited talk at Perimeter Institute (PDF) (PIRSA)
  39. Matrix Models — December 7, 2018 - Theory HEP Group talk at Syracuse University
  40. Lattice gravity and scalar fields — July 23, 2018 at Annual Lattice Conference 2018, Michigan, USA (PDF)
  41. Supersymmetry breaking and gauge/gravity duality on the lattice — April 06, 2018 - Lattice beyond Standard Model 2018 at UC Boulder, Colorado (PDF)
  42. Large $N$ gauge theories — March 09, 2018 - Theory HEP Group talk at Syracuse University
  43. Recent results from lattice supersymmetry in $2 \le d < 4$ dimensions — January 31, 2018 - NUMSTRINGS I conference at ICTS, Bangalore (PDF) (YouTube)
  44. Testing gauge/gravity duality using lattice simulations — July 22, 2017 at Annual Lattice Conference 2017 , Granada, Spain (PDF)
  45. Testing holography through lattice simulations — April 04, 2017 at Quantum Gravity, String theory, and Holography conference at Yukawa Institute for Theoretical Physics, Kyoto, Japan (PDF)
  46. Maximally supersymmetric Yang-Mills and dual gravitational theories — October 07, 2016 - Theory HEP Group talk at Syracuse University
  47. Supersymmetry on the lattice — April 17, 2016 at the APS 2016 Meeting, Salt Lake City, Utah, USA (PDF)
  48. Lattice studies of $ \mathcal{N} = (8,8)$ SYM - April 08, 2016 — Theory HEP Group talk at Syracuse University

ML Stuff

    Apart from Physics, I am also interested in machine learning (and data analytics). Here is a small collection of sample projects I have done.
    1. EDA and Data Visualization

      Exploratory Data Analysis (EDA) is the process of retrieving, processing, and 'knowing' the data. Here, I present few examples from the projects I have done. For example, the first two figures are taken from the analysis of my own flight data (i.e., flight routes taken since 2009). It is obvious that I have spent major part of these 14 years traveling between India and where I did PhD (New York) and postdoc (Toronto). Unfortunately, I have not been to the southern hemisphere but crossed the Date Line several times. In the next figure, we see about 1200 years of data predicting the day when the cherry will blossom in Kyoto, Japan (neglecting effects of temperature etc.). This project was inspired by a paper published which studied the effect of global warming on this annual event. The next figure shows the histogram for the comparison of various statistical measures such -- Accuracy, F1 Score, Precision, Recall (attributes from the confusion matrix) for different algorithms. I mostly use Matplotlib, Plotly, and Seaborn for data visualization. I have carried out these EDA mostly on Jupyter notebooks, Google Colab, or on Google Cloud Platform (GCP)

    2. Natural Language Processing (NLP)

      NLP is the field of data science/machine learning related to the analysis of texts, speech and drawing meaningful conclusions from it. In fact, advanced NLP models based on AGI (Artificial General Intelligence) can also suggest texts and answer questions based on trained models. This is why with Chat-GPT, the interest in LLM (large language models) have considerably increased. Below we see a snapshot of the project involving the determination of whether a message is spam or not and glimpse of processing of such written texts. The packages I have used for NLP problems are the NL tool-kit (NLTK) (it is a leading platform for building Python programs to work with human language data) and spaCy. In the first figure, we see one step of how we clean the data through various columns based on removing punctuations, stop words, stemming/lemmatization, and tokenization. In the second figure, we see a word cloud of the papers written in Quantum Physics during the 1990s obtained from the dataset from arXiv preprint server, a short project I did to bring out the physicist in me!

    3. Unsupervised and Deep Learning

      One of the most popular unsupervised learning algorithms is k-means clustering. The goal of k-means is to group data points into distinct non-overlapping subgroups. This is very useful for problems like customer segmentation. In the first figure, we show an example based on k-means algorithm with five clusters for a problem related to how people (customers) in some mall spend their money. The choice of number of clusters is crucial since it can lead to overfitting (high variance) for large 'k' and will underfit for small 'k'. A method for making a good choice is the elbow method. In the second figure, we show one of the simplest CNN architecture known as 'LeNet' network (deep learning). It consists of repeating convolution and pooling layers before the fully dense layer and logit output. This can be used to identify a hand-drawn "8" [as shown] with good accuracy. In the last figure, we classify the single-channel (no RGB) fashion dataset image with deep learning models in PyTorch.

Publications

Last updated: 2024, December 03. Please check iNSPIRE-HEP for the most up to date publication list. To download list of papers as a single file PDF, click PDF here

  1. Real-Time Scattering in Ising Field Theory using Matrix Product States (arXiv)
  2. Quantum computation of SU(2) lattice gauge theory with continuous variables (arXiv)
  3. Sparsity dependence of Krylov state complexity in the SYK model (arXiv)
  4. Thermal state preparation of the SYK model using a variational quantum algorithm (arXiv)
  5. SU(2) principal chiral model with tensor renormalization group on a cubic lattice (arXiv)
  6. Phase diagram of generalized XY model using tensor renormalization group (arXiv)
  7. Hamiltonian simulation of minimal holographic sparsified SYK model (arXiv)
  8. Tensor renormalization group study of 3D principal chiral model (arXiv)
  9. Phase diagram of two-dimensional $SU(N)$ super-Yang--Mills theory with four supercharges (arXiv)
  10. Sachdev-Ye-Kitaev model on a noisy quantum computer (arXiv)
  11. Continuous variable quantum computation of the O(3) model in 1+1 dimensions (arXiv)
  12. Toward quantum computations of the O(3) model using qumodes (arXiv)
  13. GPU-Acceleration of Tensor Renormalization with PyTorch using CUDA (arXiv)
  14. Notes on Quantum Computation and Information (arXiv)
  15. Supersymmetric Wilson loops on the lattice in the large $N$ limit (Springer) (Published in EPJ-ST)
  16. Non-perturbative phase structure of the bosonic BMN matrix model (arXiv)
  17. Thermal phase structure of dimensionally reduced super-Yang--Mills (arXiv)
  18. Tensor renormalization of three-dimensional Potts model (arXiv)
  19. Introduction to Monte Carlo for Matrix Models (arXiv)
  20. Large-$N$ limit of two-dimensional Yang--Mills theory with four supercharges (arXiv)
  21. Tensor renormalization group study of the 3d O(2) model (arXiv)
  22. Three-dimensional super-Yang–Mills theory on the lattice and dual black branes (arXiv)
  23. Positive geometries for all scalar theories from twisted intersection theory (arXiv)
  24. Critical analysis of two-dimensional classical XY model using tensor renormalization group (arXiv)
  25. Thermal phase structure of a supersymmetric matrix model (arXiv)
  26. Finite $N$ unitary matrix models (arXiv)
  27. Tensor renormalization group study of the non-Abelian Higgs model in two dimensions (arXiv)
  28. Lattice quantum gravity with scalar fields (arXiv)
  29. The properties of D1-branes from lattice super Yang–Mills theory using gauge/gravity duality (arXiv)
  30. On the removal of the trace mode in lattice $\mathcal{N} = 4$ super Yang-Mills theory (arXiv)
  31. Nonperturbative study of dynamical SUSY breaking in $\mathcal{N} = (2,2)$ Yang-Mills theory (arXiv)
  32. Truncation of lattice $\mathcal{N} = 4$ super Yang-Mills
  33. Testing the holographic principle using lattice simulations (arXiv)
  34. Testing holography using the lattice with super-Yang-Mills theory on a 2-torus (arXiv)
Number of papers ('Y' denotes the year), number of citations per paper, and number of author(s) per paper
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