Kartik Anand
Quantum Hamiltonian Complexity Many-Body Physics Quantum Learning Theory
Profile photo
Oct 2025
Joined the Institute for Quantum Inspired & Quantum Optimization at TUHH as a Master's student, working in the group of Prof. Martin Kliesch.
Jun 2025
New preprint: Collapses in quantum-classical probabilistically checkable proofs and the quantum polynomial hierarchy, joint work with Kabgyun Jeong and Junseo Lee (Seoul National University).
Oct 2024
Work on calibration transfer accepted at ACCV 2024Calibration Transfer via Knowledge Distillation.

Hi — I'm Kartik. I like cats, pizzas, and thinking hard about the computational nature of quantum many-body systems. I'm currently a Master's student in Computer Science at the Hamburg University of Technology (TUHH), working in the group of Prof. Martin Kliesch at the Institute for Quantum Inspired and Quantum Optimization.

I hold a B.Tech. in Computer Science & Engineering from the Indian Institute of Technology Goa (2019–2023). After graduating I joined IIT Delhi's Vision Lab, where I worked on deep learning problems in computer vision, leading to a publication at ACCV 2024.

In 2024 my interests shifted decisively toward quantum complexity. I did independent research in Quantum Hamiltonian Complexity with a focus on the quantum PCP conjecture and its hardness-of-approximation formulation — work that was warmly received by the community. I also collaborated with researchers at the Quantum Information Theory Group at Seoul National University's Research Institute of Mathematics. My research spans Quantum Hamiltonian Complexity, Quantum Learning Theory, and Quantum Certification.

Open to Collaboration

I'm always happy to discuss and collaborate on problems in Quantum Hamiltonian Complexity, Quantum PCP, Quantum Learning Theory, and related areas. If you're thinking about something in the same orbit, feel free to reach out at Kartik.anand@tuhh.de.

2025 · Preprint
Collapses in quantum-classical probabilistically checkable proofs and the quantum polynomial hierarchy
Kartik Anand, Kabgyun Jeong, Junseo Lee
arXiv:2506.19792
PDF
2024 · Preprint
Feynman's Entangled Paths to Optimized Circuit Design
Kartik Anand
arXiv:2411.08928
PDF
2024 · Conference
Calibration Transfer via Knowledge Distillation
R. Hebbalaguppe, M. Baranwal, K. Anand, C. Arora
Asian Conference on Computer Vision (ACCV) 2024, pp. 513–530
PDF