Kanishk Bakshi

Graduate Research Scholar

About Me

I am a Graduate Research Scholar at Tsinghua University specializing in quantum algorithms and their applications in physics. My research focuses on Hamiltonian simulation (including SQD and Krylov time-evolution) and computational methods for differential equations. I am also exploring the intersection of physics and AI through quantum machine learning and Hamiltonian-based embedding methods.

My academic journey has been shaped by research experience at DRDO and mentorship under Dr. Xiongfeng Ma, Dr. Amitansu Pattanaik, and Dr. Kathiravan Srinivasan.

Beyond the lab: I am an amateur radio astronomer. I operate a personal 3-meter radio dish for pulsar detection currently I am trying to develop custom algorithm to isolate deep-space signals from PSR B0329+54 Pulsar.

Education

  • Master's in Computer Science and Engineering
    Tsinghua University, Beijing, P.R.China (Aug 2025 - Sep 2027)
  • Bachelor of Technology in Computer Science and Engineering
    Vellore Institute of Technology, Vellore, India (Aug 2021 - July 2025)
    Minor: Information Security
  • Senior Secondary Education
    Delhi Public School, Ambala, India (April 2006 - March 2021)

Research Experience

  • Graduate Research Scholar - Institute of Interdisciplinary Sciences (IIIS), Tsinghua University, Beijing, P.R.China (Sep 2025 - Present)
  • Research Fellow - Defence Terrain Research Laboratory (DRDO), New Delhi, India (May 2024 - August 2024)
  • Undergraduate Research Scholar - Vellore Institute of Technology, Vellore, India (Oct 2022 - Present)
  • Reviewer - Elsevier (May 2024 - Present)

Awards and Honors

  • Raman Research Award - This prestigious award was conferred by VIT Vellore in recognition of outstanding contributions to research. The award was granted for the manuscript titled "Advancements in Heart Disease Diagnosis: Harnessing Predictive Modeling Techniques for Cardiovascular Health Management." The research explores advanced machine learning techniques to enhance the accuracy of cardiovascular disease predictions, paving the way for better diagnostic tools.

Projects

Track ML Project

Track ML

Track ML is a machine learning project focused on particle tracking and event reconstruction in high-energy physics experiments. The project leverages advanced deep learning architectures to improve the accuracy and efficiency of identifying particle trajectories from detector data, contributing to faster and more reliable scientific discoveries.

Surface Code Project

Surface Code Reliability Validation

Design, Analysis and Validation of Reliable Computing Systems
This project presents a structured review and simulation of Google Quantum AI’s 2023 breakthrough on surface code error correction. It covers experimental and simulated fault injection, reliability modeling, and validation strategies for logical qubits. The work demonstrates how increasing code distance suppresses logical errors, and how fault injection techniques validate system reliability—directly connecting quantum error correction to classical reliability engineering.