Optimal tomography of bosonic and fermionic Gaussian states
arXiv:2607.11847
The paper determines the exact sample complexity for learning bosonic and fermionic Gaussian quantum states, showing that a number of copies scaling quadratically with the number of modes suffices, independent of purity or energy constraints.
Abstract
The sample complexity is the minimum number of copies required to learn an accurate classical description of a quantum state. Bosonic and fermionic Gaussian quantum states are families of quantum states that play a key role in quantum science and technology, from quantum optics and many-body physics to quantum chemistry, quantum computing, and quantum information theory. Despite their importance, their sample complexity had not been fully determined. We settle this open problem and show that both bosonic and fermionic Gaussian states can be learned using a number of copies that scales quadratically in the number of modes, regardless of whether the state is pure or mixed, and independently of any energy bound on the state. We derive these results by using the representation theory of Gaussian unitaries and by putting forth a generalization of the random purification channel to this setting and beyond.
61 pages, 1 figure. This paper subsumes and supersedes arXiv:2512.16878 and arXiv:2512.15690