Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

Abstract Machine learning is a promising application of quantum computing, but challenges
remain for implementation today because near-term devices have a limited number of …

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

Tensor networks, a model that originated from quantum physics, has been gradually
generalized as efficient models in machine learning in recent years. However, in order to …

Tensor networks are efficient representations of high-dimensional tensors with widespread
applications in quantum many-body physics. Recently, they have been adapted to the field …

Quantum many-body control is a central milestone en route to harnessing quantum
technologies. However, the exponential growth of the Hilbert space dimension with the …

Quantum mechanics studies nature and its behavior at the scale of atoms and subatomic
particles. By applying quantum mechanics, a lot of problems can be solved in a more …

Classical probability distributions on sets of sequences can be modeled using quantum
states. Here, we do so with a quantum state that is pure and entangled. Because it is …

We show how a tensor-network-based machine learning algorithm can learn the structures
of generic, non-Markovian, quantum stochastic processes. First, a process is represented as …