Neutrino thermalization via randomization on a quantum processor
Oriel Kiss, Ivano Tavernelli, Francesco Tacchino, Denis Lacroix, Alessandro Roggero

TL;DR
This paper demonstrates that random quantum circuits can emulate neutrino flavor thermalization in large systems, showing that thermalization time scales with the square root of system size, thus enabling studies beyond classical computational limits.
Contribution
The study introduces the use of random quantum circuits to simulate and analyze neutrino thermalization in large many-body systems, revealing size-independent depth requirements and scaling behaviors.
Findings
Thermalization can be reproduced with depth independent of system size.
Thermalization time scales approximately as the square root of system size.
Random circuits serve as effective tools for emulating complex many-body dynamics.
Abstract
The dynamical evolution of neutrino flavor in supernovae can be modeled by an all-to-all spin Hamiltonian with random couplings. Simulating such two-local Hamiltonian dynamics remains a major challenge, as methods with controllable accuracy require circuit depths that increase at least linearly with system size, exceeding the capabilities of current quantum devices. The eigenstate thermalization hypothesis predicts that these systems should thermalize, a behavior confirmed in small-scale classical simulations. In this work, we investigate flavor thermalization in much larger systems using random quantum circuits as an empirical tool to emulate the non-local dynamics, and demonstrate that the thermal behavior can be reproduced using a depth independent of the system size. By simulating dynamics of over one hundred qubits, we find that the thermalization time grows approximately as the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
