Training machine learning models on classical computers is usually a time and compute
intensive process. With Moore's law nearing its inevitable end and an ever-increasing …

A scalable model for a distributed quantum computation is a challenging problem due to the
complexity of the problem space provided by the diversity of possible quantum systems, from …

Quantum computers utilize the fundamentals of quantum mechanics to solve computational
problems more efficiently than traditional computers. Gate-model quantum computers are …

The uncertainty principle imposes a limitation on the measurement accuracy of two
incompatible observables and has potential applications in quantum information science …

We implement an all-optical setup demonstrating kernel-based quantum machine learning
for two-dimensional classification problems. In this hybrid approach, kernel evaluations are …

In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio
optimization with minimal holding period. Our algorithm is based on sampling the near …

Every quantum algorithm is represented by set of quantum circuits. Any optimization scheme
for a quantum algorithm and quantum computation is very important especially in the arena …

In the century following its discovery, applications for quantum physics are opening a new
world of technological possibilities. With the current decade witnessing quantum supremacy …

Secret sharing is a widely-used security protocol and cryptographic primitive in which all
people cooperate to restore encrypted information. The characteristics of a quantum field …

We define metrics and measures to characterize the ratio of accessible quantum
entanglement for complex network failures in the quantum Internet. A complex network …