Related papers: Learning Best Paths in Quantum Networks
Quantum key distribution (QKD) protocols are proven secure based on fundamental physical laws, however, the proofs consider a well-defined setting and encoding of the sent quantum signals only. Side channels, where the encoded quantum state…
Quantum feedback networks have been introduced in quantum optics as a set of rules for constructing arbitrary networks of quantum mechanical systems connected by uni-directional quantum optical fields, and has allowed for a system theoretic…
We present a novel definition of the reinforcement learning state, actions and reward function that allows a deep Q-network (DQN) to learn to control an optimization hyperparameter. Using Q-learning with experience replay, we train two DQNs…
Financial market prediction and optimal trading strategy development remain challenging due to market complexity and volatility. Our research in quantum finance and reinforcement learning for decision-making demonstrates the approach of…
The purpose of this paper is to provide a brief review of some recent developments in quantum feedback networks and control. A quantum feedback network (QFN) is an interconnected system consisting of open quantum systems linked by free…
Quantum networks (QNs) exhibit stronger connectivity than predicted by classical percolation, yet the origin of this phenomenon remains unexplored. We apply a statistical physics model -- concurrence percolation -- to uncover the origin of…
Quantum Internet in a Nutshell (QI-Nutshell) connects the fields of quantum communication and quantum computing by emulating quantum communication protocols on currently available ion-trap quantum computers. We demonstrate emulations of QKD…
Quantum transport is the study of the motion of electrons through nano-scale structures small enough that quantum effects are important. In this contribution I review recent theoretical proposals to use the techniques of quantum feedback…
Quantum computing holds a great promise and this work proposes to use new quantum data networks (QDNs) to connect multiple small quantum computers to form a cluster. Such a QDN differs from existing QKD networks in that the former must…
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 algorithms. Quantum walks on graphs are fundamentally different from…
Link prediction is one of the fundamental problems in graph theory, critical for understanding and forecasting the evolution of complex systems like social and biological networks. While classical heuristics capture certain aspects of graph…
We present a first cross-validation of two open-source quantum network simulators, QuISP and SeQUeNCe, focusing on basic networking tasks to ensure consistency and accuracy in simulation outputs. Despite very similar design objectives of…
We propose a bottom-up approach, based on Reinforcement Learning, to the design of a chain achieving efficient excitation-transfer performances. We assume distance-dependent interactions among particles arranged in a chain under…
Neural networks enjoy widespread success in both research and industry and, with the imminent advent of quantum technology, it is now a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose…
Owing to its outstanding parallel computing capabilities, quantum computing (QC) has been a subject of continuous attention. With the gradual maturation of QC platforms, it has increasingly played a significant role in various fields such…
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 approaches that can offer advancements in both the…
This paper explores the potential application of quantum and hybrid quantum-classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus and 33-bus test systems. A systematic performance…
We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme, which remains largely unknown due to the non-convex optimization landscape, the measurement error, and the…
Future quantum networks will enable the interconnection of multiple users distributed across vast geographic distances. Due to these large separations and limited physical resources, communication will often rely on multi-path routing…
We develop a novel key routing algorithm for quantum key distribution (QKD) networks that utilizes a distribution of keys between remote, i.e., not directly connected by a QKD link, nodes through multiple non-overlapping paths. This…