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, pizza, and thinking about how hard quantum many-body systems are to compute. I'm a Master's student in Computer Science at the Hamburg University of Technology (TUHH), in Prof. Martin Kliesch's group 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 and spent a while on deep learning for computer vision, which turned into an ACCV 2024 paper.

In 2024 I moved over to quantum complexity. Most of that year went into independent work on quantum Hamiltonian complexity, mainly the quantum PCP conjecture and its hardness-of-approximation form, and later a collaboration with the Quantum Information Theory Group at Seoul National University's Research Institute of Mathematics. These days I work on quantum Hamiltonian complexity, quantum learning theory and quantum certification.

Open to Collaboration

Happy to talk about quantum Hamiltonian complexity, quantum PCP, quantum learning theory or anything nearby. If you're working on something in that direction, email me 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