Exponential de Finetti Theorems for Fermionic Gaussian States
Jędrzej Burkat, Michał Studziński, Sergii Strelchuk
Abstract
We prove an exponential variant of the Gaussian de Finetti theorem: the subsystems of permutation-invariant, free-fermionic Gaussian states are well-approximated by convex combinations of almost-i.i.d. states that are Gaussian on subsets of their parts. Our result provides an error bound between the original state and its approximants that decays exponentially in the number of unconstrained parts, becoming super-exponential when the subsystem under consideration is small. The dimensional penalty of our bound is polylogarithmic in the local Hilbert space dimension, an exponential improvement over the standard de Finetti theorem of [Nat. Phys. 3, 645-649]. In the fully i.i.d. limit, our bound recovers the Gaussian de Finetti theorem of [arXiv:2603.12392]. Previous works considered Gaussian-symmetric states, which are supported on the trivial irrep of the tensor matchgate representation. We extend these to a broader class of Gaussian-invariant states containing, for example, i.i.d. copies of single-replica mixed Gaussian states. We show that Gaussian-invariant states are precisely the partial traces of Gaussian-symmetric states on locally enlarged replicas, and always admit a purification into a larger Gaussian-symmetric state. This extends de Finetti theorems to the full set of Gaussian-invariant states, with only a polynomial overhead in the dimensional penalty of the error bound.
Create a lesson
Related papers
Trading Circuit Depth for Pulse Sparsity in Chromatic Dynamical Decoupling
Amy F. Brown, Daniel A. Lidar
Optimal spectrum estimation
Ainesh Bakshi, Apoorv Vikram Singh, Xinyu Tan
Non-Abelian sheaf quantum LDPC codes: good and magical
Zimu Li, Fuchuan Wei, Zhengyi Han et al.
Learning and interpreting policies for simultaneous entanglement requests in quantum networks
Leon Rode, Sumeet Khatri, Supartha Podder
Sharp universal death of entanglement threshold for Pauli Hamiltonians
Bobak T. Kiani
Proper Agnostic Learning of Matrix Product States and Tree Tensor Networks
Constantin Cedillo Vayson de Pradenne, Jordan Cotler