Vojtech Havlicek

We make progress in understanding the relationship between learning models with access to entangled, separable and statistical measurements in the quantum statistical query (QSQ) model. We show the following results. Entangled versus separable measurements: The goal is to learn an unknown f from the concept class C containing functions from 0,1^n to [k] given copies of a uniform superposition over |x,f(x)>. We show that, if T copies suffice to learn f using entangled measurements, O(nT^2) copies suffice to learn f using only separable measurements. Entangled versus statistical measurements: The goal is to learn a function f in C given access to separable measurements or statistical measurements. We exhibit a concept class C based of degree-2 functions with exponential separation between QSQ learning and quantum learning with entangled measurements (even in the presence of noise). This proves the ""quantum analogue"" of the seminal result of Blum et al. that separates classical SQ learning from classical PAC learning with classification noise. QSQ lower bounds for learning states: We introduce a quantum statistical query dimension (QSD), and use it to give lower bounds on the QSQ complexity of learning. We prove superpolynomial QSQ lower bounds for testing purity of quantum states, shadow tomography, learning coset states for the Abelian hidden subgroup problem, degree-2 functions, planted biclique states, and learning output states of Clifford circuits of depth polylog(n). We also show that an extension of QSD characterizes the complexity of general search problems. Further applications: We give an unconditional separation between weak and strong error mitigation and prove lower bounds for learning distributions in the QSQ model. Prior works by Quek et al., Hinsche et al., and Nietner et al. proved analogous results assuming diagonal measurements and our work removes this assumption.