Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …

This review paper examines state-of-the-art algorithms and techniques in quantum machine
learning with potential applications in finance. We discuss QML techniques in supervised …

Artificial neural networks have been proposed as potential algorithms that could benefit from
being implemented and run on quantum computers. In particular, they hold promise to …

Quantum simulation is a technology of using controllable quantum systems to study new
quantum phases of matter. Certification for quantum simulators is a challenging problem …

Exploiting the utility of near-term quantum devices is a long-standing challenge whereas
hybrid quantum machine learning emerges as a promising candidate. Here we propose a …

We present a new Canonical Multispin-flip Cluster Monte Carlo algorithm for Ising model
and describe efficient implementations for hybrid supercomputer. Our method takes …

The noisy binary linear problem (NBLP) is known as a computationally hard problem, and
therefore, it offers primitives for post-quantum cryptography. An efficient quantum NBLP …

Motivated by recent advances in neuroscience, in this work, we explore the emergent
behaviour of quantum systems with a dynamical biologically-inspired qubits interaction. We …

Group theory is extremely successful in characterizing the symmetries in quantum systems,
which greatly simplifies and unifies our treatments of quantum systems. Here we introduce …

We present a bit encoding scheme for a highly efficient and scalable representation of
bosonic Fock number states in the restricted Boltzmann machine neural network …