Popular science article on quantum-secured AI (מדע גדול בקטנה)
בינה מלאכותית מאובטחת קוונטית. An article about using quantum optics to guarantee privacy in cloud-based AI computation.
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1 min read
Professional Summary
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 for Electronics, working with Prof. Dirk Englund, and 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.
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
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.
Research Highlights
Quantum Technology
Quantum cryptography, secure computation, and quantum machine learning on integrated photonic platforms. Security guaranteed by physics, not mathematical assumptions.
Learn more →In-Physics Computing
AI 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, and study the fundamental limits of computation.
Learn more →AI for Science
AI makes mistakes. We build AI whose scientific conclusions can be mathematically verified: AI that comes with a proof, not just a prediction. We couple LLMs with the Lean proof assistant for machine-checked discovery, and founded the Center for Verifiable AI at MIT-Technion.
Learn more →Recent Publications
(2026).
Disaggregated machine learning via in-physics computing at radio frequency.
Science Advances.
(2025).
Ax-Prover: A deep reasoning agentic framework for theorem proving in mathematics and quantum physics.
arXiv preprint.
(2025).
Reduced state embedding for error correction in quantum cryptography.
arXiv preprint.
(2025).
Decoy state and purification protocols for superior quantum key distribution with imperfect quantum-dot based single photon sources.
PRX Quantum.
(2025).
Machine intelligence on wireless edge networks.
arXiv preprint.
Latest News
Paper on RF in-physics computing published in Science Advances
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).
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1 min read
Joining the Technion as Assistant Professor
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 …
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1 min read
Quantum-Secure Deep Learning published in Physical Review X
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 …
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1 min read
MIT News covers our quantum security work
Our work on quantum-secure deep learning was featured by [MIT News](https://news.mit.edu/2024/new-security-protocol-shields-data-during-cloud-based-computation-0926), [Tech …
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1 min read