A central result in the foundations of quantum mechanics is the Kochen-Specker theorem. In
short, it states that quantum mechanics is in conflict with classical models in which the result …

Catalysts open up new reaction pathways that can speed up chemical reactions while not
consuming the catalyst. A similar phenomenon has been discovered in quantum information …

Generative modeling using samples drawn from the probability distribution constitutes a
powerful approach for unsupervised machine learning. Quantum mechanical systems can …

Non-stabilizerness–commonly known as magic–measures the extent to which a quantum
state deviates from stabilizer states and is a fundamental resource for achieving universal …

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 …

We propose efficient algorithms for classically simulating fermionic linear optics operations
applied to non-Gaussian initial states. By gadget constructions, this provides algorithms for …

We describe schemes of quantum computation with magic states on qubits for which
contextuality and negativity of the Wigner function are necessary resources possessed by …

We discuss the role of contextuality within quantum fluctuation theorems, in the light of a
recent no-go result by Perarnau-Llobet et al. We show that any fluctuation theorem …

Quantum computers promise considerable speedups with respect to their classical
counterparts. However, the identification of the innately quantum features that enable these …

We propose a method for classical simulation of finite-dimensional quantum systems, based
on sampling from a quasiprobability distribution, ie, a generalized Wigner function. Our …