Litcius/Paper detail

Quantum chaos in the sparse SYK model

Patrick Orman, Hrant Gharibyan, John Preskill

2025Journal of High Energy Physics16 citationsDOIOpen Access PDF

Abstract

A bstract The Sachdev-Ye-Kitaev (SYK) model is a system of N Majorana fermions with random interactions and strongly chaotic dynamics, which at low energy admits a holographically dual description as two-dimensional Jackiw-Teitelboim gravity. Hence the SYK model provides a toy model of quantum gravity that might be feasible to simulate with near-term quantum hardware. Motivated by the goal of reducing the resources needed for such a simulation, we study a sparsified version of the SYK model, in which interaction terms are deleted with probability 1 −p . Specifically, we compute numerically the spectral form factor (SFF, the Fourier transform of the Hamiltonian’s eigenvalue pair correlation function) and the nearest-neighbor eigenvalue gap ratio r (characterizing the distribution of gaps between consecutive eigenvalues). We find that when p is greater than a transition value p 1 , which scales as 1/ N 3 , both the SFF and r match the values attained by the full unsparsified model and with expectations from random matrix theory (RMT). But for p < p 1 , deviations from unsparsified SYK and RMT occur, indicating a breakdown of holography in the highly sparsified regime. Below an even smaller value p 2 , which also scales as 1/ N 3 , even the spacing of consecutive eigenvalues differs from RMT values, signaling a complete breakdown of spectral rigidity. Our results cast doubt on the holographic interpretation of very highly sparsified SYK models obtained via machine learning using teleportation infidelity as a loss function.

Topics & Concepts

PhysicsSykStatistical physicsCHAOS (operating system)Theoretical physicsQuantumQuantum mechanicsQuantum electrodynamicsMathematical physicsMedicineComputer securityInternal medicineComputer scienceReceptorTyrosine kinaseQuantum chaos and dynamical systemsQuantum many-body systemsNonlinear Dynamics and Pattern Formation