Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

Parameterized quantum circuits are a promising technology for achieving a quantum
advantage. An important application is the variational simulation of time evolution of …

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 …

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 …

We calculate the energy levels and corresponding eigenstates of an interacting scalar
quantum field theory on a lattice using a continuous-variable version of the quantum …

As quantum technology rapidly advances, the need for efficient scalable methods to
characterize quantum systems intensifies. Quantum state tomography and Hamiltonian …

Thermal properties of nanomaterials are crucial to not only improving our fundamental
understanding of condensed matter systems, but also to developing novel materials for …

We introduce a method to perform imaginary time evolution in a controllable quantum
system using measurements and conditional unitary operations. By performing a sequence …

Quantum state tomography (QST) is a crucial ingredient for almost all aspects of
experimental quantum information processing. As an analog of the “imaging” technique in …

The variational quantum eigensolver (VQE) stands as a prominent quantum-classical hybrid
algorithm for near-term quantum computers to obtain the ground states of molecular …