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 error mitigation has been proposed as a means to combat unwanted and
unavoidable errors in near-term quantum computing without the heavy resource overheads …

Parametrized quantum circuits (PQCs) represent a promising framework for using present-
day quantum hardware to solve diverse problems in materials science, quantum chemistry …

While quantum state tomography is notoriously hard, most states hold little interest to
practically minded tomographers. Given that states and unitaries appearing in nature are of …

Scrambling of quantum information is an important feature at the root of randomization and
benchmarking protocols, the onset of quantum chaos, and black-hole physics. Unscrambling …

Generalisation in machine learning often relies on the ability to encode structures present in
data into an inductive bias of the model class. To understand the power of quantum machine …

This article examines the current status of quantum computing (QC) in Earth observation and
satellite imagery. We analyze the potential limitations and applications of quantum learning …

While recent breakthroughs have proven the ability of noisy intermediate-scale quantum
(NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the …

We study the complexity of learning quantum states in various models with respect to the
stabilizer formalism and obtain the following results: We prove that Ω (n) T-gates are …

In recent proposals of quantum circuit models for generative tasks, the discussion about their
performance has been limited to their ability to reproduce a known target distribution. For …