Our principal scientist. A theoretical condensed matter physicist with a PhD from the Weizmann Institute, his research combines physics, information theory, and machine learning. Maciej held postdoctoral appointments at ETH Zurich, and as a Marie Curie Fellow at the University of Chicago and University of Zurich. In his free time he enjoys mountaineering and studying Japanese.

Antonio has over a decade of experience in quantum computing, with roles at Intel, HRL, and most recently Microsoft, where he was Principal PM for the Majorana program. His work spans the full stack, from materials and device engineering to systems and applications. He holds a PhD from the University of Illinois Urbana-Champaign in computational materials science and was a postdoctoral researcher at Cornell University.

Weaponized extroversion personified. Sam spent 3 years managing quantum startups at the Creative Destruction Lab, where he worked with an obnoxiously large fraction of the quantum ecosystem. He puts his expertise and his unrelenting urge to talk to people to use as our Head of Growth. He has a Masters from the University of Toronto and does comedy. He’s never told a quantum joke out of principle.

Yura builds things at Haiqu and makes sure what’s already built still works. Except this website—if it lags, it’s not his fault. Before Haiqu, he productized deep learning in computer vision at his own AR startup and worked on reinforcement learning back when it required engineering, not vibes. He led the first open-source implementation of DeepMind’s Agent57, outperforming the original on its own Atari games.

Mariana prevents feature roadmaps and user journeys from becoming uncontrolled experiments. With a PhD in the physics of complex networks and critical phenomena, she applies the same intuition to quality-attribute scenarios and feature interactions, hunting for bottlenecks and single points of failure while ensuring every requirement has an owner, a testable definition, and a clear path to shipping.

Dishant has 4+ years in the industry building what others only theorize—scalable error mitigation across any hardware and any application. He is published in Nature, has trained non-quantum professors & postdocs in Singapore and Australia, and mentors Qiskit advocates for fun. At Haiqu, Dikshant is co-architecting the libraries that make quantum practical now.

Vlad believes that to run on hardware, you need to think like hardware. Before joining Haiqu, Vlad did research on quantum machine learning at the Bavarian NMR center and at the QAR-Lab (Ludwig-Maximilians University of Munich). With a background in quantum optimal control from RWTH Aachen University and the University of Waterloo, he works on enabling frontier applications with error mitigation and correction.

Maxence worked on the theory of high-temperature superconductors and other strongly correlated systems during his PhD and postdoc. At Haiqu, he tackles the same fundamental problems using quantum computers, designing algorithms and building applications across quantum chemistry, quantum machine learning, and quantum optimization.