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 is driven by a single conviction: the physics of information holds the key to solving AI’s biggest challenges, from securing data with quantum mechanics to building AI that can prove its own correctness.
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
Quantum cryptography, secure computation, and quantum machine learning on integrated photonic platforms. Security guaranteed by physics, not mathematical assumptions (Phys. Rev. X, 2025).
Learn more โAI faces an energy crisis: data centers consume more electricity than many countries. 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.
Learn more โAI makes mistakes. We build AI whose scientific conclusions can be mathematically verified: AI that comes with a proof, not just a prediction. Our agentic provers recently settled the decade-old FGG quantum-optimization conjecture, and we co-founded the Center for Verifiable AI at MIT-Technion.
Learn more โ
We resolved the FarhiโGoldstoneโGutmann conjecture on the optimal performance of QAOA on the ring of disagrees, open since 2014. The proof was proposed by a large language model โฆ
Popular-science feature (in Hebrew) on our quantum-secured AI work โ using quantum optics to guarantee privacy in cloud-based AI computation. ืืื ื ืืืืืืชืืช ืืืืืืืช ืงืืื ืืืช.
Our paper on disaggregated machine learning via in-physics computing at radio frequency is published in Science Advances. [Read the paper](https://doi.org/10.1126/sciadv.adz0817).
Our paper on quantum-secure multiparty deep learning is published in Physical Review X. We show how quantum mechanics can provide information-theoretic security for cloud-based AI โฆ
Excited to announce that I will be joining the Technion's Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering as an Assistant Professor. I'm building a new lab and โฆ