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 …

Quantum random walk is the quantum counterpart of a classical random walk. The classical
random walk concept has long been used as a computational framework for designing …

Designing efficient and secure cryptosystems has been a preoccupation for many scientists
and engineers for a long time wherein they use chaotic systems to design new …

Applications of quantum walks can depend on the number, exchange symmetry and
indistinguishability of the particles involved, and the underlying graph structures where they …

Map**s between fermions and qubits are valuable constructions in physics. To date only a
handful exist. In addition to revealing dualities between fermionic and spin systems, such …

The quantum circuit model is the de-facto way of designing quantum algorithms. Yet any
level of abstraction away from the underlying hardware incurs overhead. In this work, we …

The classical random walk formalism plays an important role in a wide range of applications.
Its quantum counterpart, the quantum walk, is proposed as an important theoretical model …

Quantum walks are at the heart of modern quantum technologies. They allow to deal with
quantum transport phenomena and are an advanced tool for constructing novel quantum …

Quantum dot (QD) based nanomaterials are very promising materials for the fabrication of
optoelectronic devices like solar cells, light emitting diodes (LEDs), and photodetectors as …

Particle transport in quantum systems, which can be modeled by quantum walks on graphs,
demonstrates a faster propagation advantage over the corresponding transport in classical …