The widespread use of machine learning has raised the question of quantum supremacy for supervised learning as compared to quantum computational advantage. In fact, a recent work shows that computational and learning advantages are, in general, not equivalent, i.e., the additional information provided by a training set can reduce the hardness of some problems. This […]
Key distillation is an essential component of every quantum key distribution (QKD) system because it compensates for the inherent transmission errors of a quantum channel. However, the interoperability and throughput aspects of the postprocessing components are often neglected. In this article, we propose a high-throughput key distillation framework that supports multiple QKD protocols, implemented in […]
The quantum approximate optimization algorithm (QAOA) adopts a hybrid quantum-classical approach to find approximate solutions to variational optimization problems. In fact, it relies on a classical subroutine to optimize the parameters of a quantum circuit. In this article, we present a Bayesian optimization procedure to fulfill this optimization task, and we investigate its performance in […]
Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems, such as classification and identification tasks. A subclass of QML methods is quantum generative adversarial networks (QGANs), which have been studied as a quantum counterpart of classical GANs widely used in image manipulation and generation […]
Noisy intermediate-scale quantum computers are widely used for quantum computing (QC) from quantum cloud providers. Among them, superconducting quantum computers, with their high scalability and mature processing technology based on traditional silicon-based chips, have become the preferred solution for most commercial companies and research institutions to develop QC. However, superconducting quantum computers suffer from fluctuation […]
Variational quantum algorithms (VQAs) are prime contenders to gain computational advantages over classical algorithms using near-term quantum machines. As such, many endeavors have been made to accelerate the optimization of modern VQAs in past years. To further improve the capability of VQAs, here, we propose a quantum distributed optimization scheme (dubbed as QUDIO), whose back […]
Variational quantum algorithms (VQAs) are prime contenders to gain computational advantages over classical algorithms using near-term quantum machines. As such, many endeavors have been made to accelerate the optimization of modern VQAs in past years. To further improve the capability of VQAs, here, we propose a quantum distributed optimization scheme (dubbed as QUDIO), whose back […]
Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving a variety of machine learning tasks. Variational methods that use quantum resources of imperfect quantum devices with the help of classical computing techniques are popular for supervised learning. Variational quantum classification (VQC) is one of such methods with possible quantum advantage in using […]
As quantum network technologies develop, the need for teaching and engineering tools such as simulators and emulators rises. QuNetSim addresses this need. QuNetSim is a Python software framework that delivers an easy-to-use interface for simulating quantum networks at the network layer, which can be extended at little effort of the user to implement the corresponding […]
In distributed quantum computing architectures, with the network and communications functionalities provided by the Quantum Internet, remote quantum processing units can communicate and cooperate for executing computational tasks that single, noisy, intermediate-scale quantum devices cannot handle by themselves. To this aim, distributed quantum computing requires a new generation of quantum compilers, for mapping any quantum […]