Quantum learning theory is a new and very active area of research at the intersection of
quantum computing and machine learning. Important breakthroughs in the past two years …

Quantum neural network (QNN), or equivalently, the parameterized quantum circuit (PQC)
with a gradient-based classical optimizer, has been broadly applied to many experimental …

Differential privacy has been an exceptionally successful concept when it comes to
providing provable security guarantees for classical computations. More recently, the …

We introduce a general statistical learning theory for processes that take as input a classical
random variable and output a quantum state. Our setting is motivated by the practical …

Let H be a binary-labeled concept class. We prove that H can be PAC learned by an
(approximate) differentially private algorithm if and only if it has a finite Littlestone dimension …

A quantum generalized divergence by definition satisfies the data-processing inequality; as
such, the relative decrease in such a divergence under the action of a quantum channel is at …

We revisit the problem of characterising the complexity of Quantum PAC learning, as
introduced by Bshouty and Jackson [SIAM J. Comput. 1998, 28, 1136–1153]. Several …

The eligibility of various advanced quantum algorithms will be questioned if they can not
guarantee privacy. To fill this knowledge gap, here we devise an efficient quantum …

We propose a versatile privacy framework for quantum systems, termed quantum pufferfish
privacy (QPP). Inspired by classical pufferfish privacy, our formulation generalizes and …

Differential privacy provides a theoretical framework for processing a dataset about $ n $
users, in a way that the output reveals a minimal information about any single user. Such …