Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

Highlights•Machine learning techniques (MLTs) are progressing the drug discovery
process.•Conventional MLTs require large data, lack transparency and are not …

Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

Quantum metrology is one of the most promising applications of quantum technologies. The
aim of this research field is the estimation of unknown parameters exploiting quantum …

We propose a quantum algorithm to solve systems of nonlinear differential equations. Using
a quantum feature map encoding, we define functions as expectation values of parametrized …

With the advent of real-world quantum computing, the idea that parametrized quantum
computations can be used as hypothesis families in a quantum-classical machine learning …

The ability to prepare a physical system in a desired quantum state is central to many areas
of physics such as nuclear magnetic resonance, cold atoms, and quantum computing. Yet …

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 …

Reinforcement learning has been widely used in many problems, including quantum control
of qubits. However, such problems can, at the same time, be solved by traditional, non …

The quantum alternating operator ansatz (QAOA) is a prominent example of variational
quantum algorithms. We propose a generalized QAOA called CD-QAOA, which is inspired …