Quantum Algorithms Scientist at Phasecraft | Computational physicist

Gian Gentinetta

I am a quantum algorithms scientist at Phasecraft in London, working on hybrid quantum-classical methods for simulating quantum many-body systems. My research combines quantum computing algorithms with modern classical methods, including machine learning and variational Monte Carlo.

News

Recent updates

August 2026
Joined Phasecraft as a Quantum Algorithms Scientist
I have joined Phasecraft in London as a Quantum Algorithms Scientist, working on quantum algorithms for quantum chemistry and materials science.
June 2026
QFTLM paper published in Phys. Rev. Research
Our work on the Quantum finite-temperature Lanczos Method, with Friederike Metz, William Kirby, and Giuseppe Carleo, has been published in Phys. Rev. Research. We combine real-time quantum Krylov methods with efficient preparation of typcial states to compute thermal expectation values of quantum many-body systems. Check it out!

Bio

About me

Born and raised in Zurich, Switzerland, I obtained my B.Sc. and M.Sc. degrees in Physics from ETH Zurich. In my masters I focused on machine learning and quantum computing, culminating in a thesis on quantum machine learning performed at IBM Research Zurich. After an internship at IBM, I joined EPFL in 2022 to pursue a Ph.D. in computational quantum physics under the supervision of Prof. Giuseppe Carleo, focusing on hybrid quantum-classical algorithms for simulating quantum many-body systems. After a summer internship at Phasecraft in London during my Ph.D., I joined the company as a Quantum Algorithms Scientist, where I now work on quantum algorithms for quantum chemistry and materials science. When I'm not in the lab, I am usually found on Swiss trains, on top of a mountain, or swimming in a lake.

Research profile

Interests and methods

Quantum computation Quantum simulation Quantum many-body systems Near-term quantum algorithms Neural quantum states Tensor networks Variational Monte Carlo Machine learning