Tharrmashastha SAPV

PhD Candidate · Department of Computer Science · IIIT-Delhi

Current Research

Solving Maximum Independent Set using Variational Quantum Algorithms

In this project, I perform a comparison study of various Variational Quantum Algorithms for the Maximum Independent Set (MIS) problem. More specifically, I compare the use of Variational Quantum Eigensolve (VQE), Quantum Approximate Optimization Algorithm (QAOA), Adaptive Derivative Assembled Problem Tailored-QAOA (ADAPT-QAOA), and Digitized Counterdiabatic-QAOA (DC-QAOA) in solving the MIS problem. The study is performed using both noiseless and noisy simulators. In addition, error mitigation techniques like Zero Noise Extrapolation and Twirled Readout Error extinction are used to study the effectiveness of the mitigation techniques when using noisy simulators.

GitHub

Portfolio Optimization using Quantum Annealing and Variational Algorithms

In this project, I study the portfolio optimization problem using both gate-based (VQE) and quantum annealing approaches. Starting with a 0–1 mean-variance model under integer budget constraints, it extends to a discrete-weight mean-variance model, a discretized version of the original problem as proposed by Harry Markowitz. I use brute-force methods to obtain classical baselines, and use them to compare the performance of the quantum solvers. This study demonstrates how quantum optimization techniques can be used to address the combinatorial complexity of portfolio selection.

GitHub

Query Complexity of k-shallow Quantum Circuits

Collaborators: Dr. Debajyoti Bera, Dr. Jaikumar Radhakrishnan, Dr. Rohit Sarma Sarkar

Description: In this project, we investigate the query complexity of decision problems when on has access to shallow quantum circuits. A k-shallow quantum circuit is a circuit that makes atmost k oracle queries to the input and forcibly performs a measurement after the last query. When k is smaller than the optimal query complexity Q, it is expected that any algorithm with this restriction will require queries more than Q. In this work, we aim to lower bound the number of queries that any algorithm would need to make to solve the problem of interest if the algorithm can only use k-shallow quantum circuits.

Publications and Preprints

  1. Yash Saxena, Tharrmashastha SAPV, Sagnik Chatterjee. Realization of Maximally-Entangling Two-Qutrit Gates using the Cross-Resonance Scheme. Accepted for a talk at APS March Meeting 2025 and Quantum Innovation 2025, RIKEN. arXiv:2504.15265
  2. Debajyoti Bera, Tharrmashastha SAPV. Quantum Query-Space Lower Bounds using Branching Programs. arXiv:2407.06872
  3. Debajyoti Bera, Tharrmashastha SAPV. Low-space Quantum Algorithms for Estimate-Mark-Amplify Tasks. ICTCS 2024. DOI
  4. Sagnik Chatterjee, Tharrmashastha SAPV, Debajyoti Bera. Efficient Quantum Agnostic Improper Learning of Decision Trees. AISTATS 2024. DOI
  5. Debajyoti Bera, Tharrmashastha SAPV. A Generalized Quantum Branching Program. FSTTCS 2023. DOI
  6. Debajyoti Bera, Tharrmashastha SAPV. Quantum and Randomized Algorithms for Non-linearity Estimation. ACM Transactions in Quantum Computing, Vol. 2, No. 2, Article 5 (2021). DOI
  7. Tharrmashastha SAPV, Debajyoti Bera, Arpita Maitra, Subhamoy Maitra. Quantum Algorithms for Cryptographically Significant Boolean Functions: And IBM Experience. Springer Briefs in Computer Science, Springer 2021, SBN 978-981-16-3060-6, pp. 1-116. DOI
  8. Debajyoti Bera, Subhamoy Maitra, Tharrmashastha SAPV. Efficient Quantuam Algorithms Related to Autocorrelation Spectrum. INDOCRYPT 2019. Lecture Notes in Computer Science,vol 11898, Springer, Cham. DOI
  9. Debajyoti Bera, Tharrmashastha SAPV. Error Reduction in Quantum Algorithms. Physical Review A, 100:012331, July 2019. DOI