Originally developed in the context of condensed-matter physics and based on
renormalization group ideas, tensor networks have been revived thanks to quantum …

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

Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …

We extend the concept of transfer learning, widely applied in modern machine learning
algorithms, to the emerging context of hybrid neural networks composed of classical and …

Recent advances in classical reinforcement learning (RL) and quantum computation point to
a promising direction for performing RL on a quantum computer. However, potential …

Due to the exponential growth of the Hilbert space dimension with system size, the
simulation of quantum many-body systems has remained a persistent challenge until today …

Effectively compressing and optimizing tensor networks requires reliable methods for fixing
the latent degrees of freedom of the tensors, known as the gauge. Here we introduce a new …

Variational quantum algorithm (VQA) accesses the centralized data to train the model, and
using distributed computing can significantly improve the training overhead; however, the …

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 …