A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …

In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

The general problem of quantum compiling is to approximate any unitary transformation that
describes the quantum computation as a sequence of elements selected from a finite base …

Realizing the full potential of quantum technologies requires precise real-time control on
time scales much shorter than the coherence time. Model-free reinforcement learning …

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 machine learning promises to revolutionize traditional machine learning by
efficiently addressing hard tasks for classical computation. While claims of quantum speed …

Quantum computing has recently emerged as a transformative technology. Yet, its promised
advantages rely on efficiently translating quantum operations into viable physical …

Inspired by the advancements in large language models based on transformers, we
introduce the transformer quantum state (TQS): a versatile machine learning model for …

Practical distributed quantum computing requires the development of efficient compilers,
able to make quantum circuits compatible with some given hardware constraints. This …