Kfir Sulimany

Incoming Assistant Professor, Technion ECE

Professional Summary

We secure information with quantum light, run AI directly in physics, and build AI that proves its own results.

Kfir Sulimany is an incoming Assistant Professor at the Technion’s Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering, where he will establish the Quantum Machine Intelligence Lab. He currently leads the Quantum Perception team at MIT’s Research Laboratory of Electronics, working with Prof. Dirk Englund, and co-founded the Center for Verifiable AI at MIT-Technion. He received his PhD in Physics from the Hebrew University of Jerusalem under Prof. Yaron Bromberg. His research spans quantum cryptography, computing in physical hardware and machine-checked proofs.

Education

PhD in Physics

2017
2023

Hebrew University of Jerusalem

MSc in Physics

2014
2017

Hebrew University of Jerusalem

BSc in Physics and Mathematics

2008
2011

Hebrew University of Jerusalem (Talpiot)

Diploma in Computer Science for BSc Graduates

2013
2016

Open University of Israel

Interests

Quantum Technology In-Physics Computing AI for Science Integrated Photonics

New paper: Quantum Cryptography and Autoformalization in Lean. An end-to-end, machine-checked security proof of BB84 in Lean, down to the mathematical axioms, built with LLM-based autoformalization. Read more →

Join the Quantum Machine Intelligence Lab

We are building a new research lab at the Technion’s ECE faculty. We are hiring PhD and MSc students to work on quantum technology, in-physics computing, and AI for science, with a unique opportunity of a ~1 year funded visit to MIT as part of their studies.

Open Positions

Research Highlights

🔐 Quantum Technology

Quantum cryptography, secure computation, and quantum machine learning on integrated photonic platforms. Deep learning with quantum-bounded leakage of client data and model weights (Phys. Rev. X, 2025).

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⚡ In-Physics Computing

Data centers for AI consume a growing share of the world's electricity. We build optical and RF neural networks that perform AI directly in the physical domain at a fraction of the energy (Science Advances, 2026), and study the fundamental limits of computation.

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🧠 AI for Science

We build AI whose scientific conclusions are checked by a proof assistant. Our team recently produced a Lean-verified proof of the decade-old FGG quantum-optimization conjecture (led by Uri Kol) and an end-to-end machine-checked security proof of BB84 (led by Maor Ben-Shahar), and we co-founded the Center for Verifiable AI at MIT-Technion.

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Recent Publications

Maor Ben-Shahar, Jonathan Morag, Dirk Englund, Kfir Sulimany (2026). Quantum Cryptography and Autoformalization in Lean. arXiv preprint arXiv:2610.17286.

arXiv Lean documentation Lean library Agentic framework (loopy)

Uri Kol, Maor Ben-Shahar, Kfir Sulimany, Dirk Englund (2026). A machine-verified proof of a quantum-optimization conjecture. arXiv preprint.

arXiv Lean library

Amit Kam, Kfir Sulimany, Shai Tsesses, Uzi Pereg (2026). Reduced-state embedding for quantum cryptography. Newton, 100597 (2026).

arXiv DOI

Zhihui Gao, Sri Vadlamani, Kfir Sulimany, Dirk Englund, Tingjun Chen (2026). Disaggregated machine learning via in-physics computing at radio frequency. Science Advances.

Kfir Sulimany, Sri Vadlamani, Ryan Hamerly, Prahlad Iyengar, Dirk Englund (2025). Quantum-secure multiparty deep learning. Physical Review X.

arXiv MIT News Tech Briefs DOI

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