Badania

We use channel probing to determine the best transponder configurations for spectral services in a long-haul production network. An estimation accuracy better than ±0,7dB in GSNR margin is obtained for lightpaths up to 5738km.

We demonstrate a machine learning-based optical network monitoring system which can integrate fiber monitoring, predictive maintenance of optical hardware, and security information management in a single solution. © 2022 The Author(s)

The paper presents measurements of a Quantum Key Distribution system operating on a cross-border QKD link between Ostrava (CZ) and Cieszyn (PL). The system is part of the Horizon OpenQKD project. The study attempted to determine the maximum attenuation where the QKD system was still functional and also any correlation between the crucial parameters of the quantum bit error rate (QBER), secret key rate (SKR) and link attenuation (dB). Providing a statistical analysis of the measured data, the paper expands our understanding of the behaviour of the QKD link as it approaches its technological limits.

We investigate buffer-aware demand-routing of key-consumption demands in QKD networks and implement a measurement-based framework for path selection. The proposed ML-based algorithm outperforms state-of-the-art heuristics by 22 % and 92 % for networks under test.

We report several use-case demonstrations for quantum key distribution in deployed fiber networks. The tests were carried out under real world conditions at the end-user premises using commercial QKD systems.

Scheduling problems have attracted the attention of researchers and practitioners for several decades. The quality of different methods developed to solve these problems on classical computers have been collected and compared in various benchmark repositories. Recently, quantum annealing has appeared as promising approach to solve some scheduling problems. The goal of this paper is to check experimentally if and how this approach can be applied for solving a well-known benchmark of the classical Job Shop Scheduling Problem. We present the existing capabilities provided by the D-Wave 2000Q quantum annealing system in the light of this benchmark. We have tested the quantum annealing system features experimentally, and proposed a new heuristic method as a proof-of-concept. In our approach we decompose the considered scheduling problem into a set of smaller optimization problems which fit better into a limited quantum hardware capacity. We have tuned experimentally various parameters of limited fully-connected graphs of qubits available in the quantum annealing system for the heuristic. We also indicate how new improvements in the upcoming D-Wave quantum processor might potentially impact the performance of our approach.

In this paper, the problem of charging electric motor vehicles on a motorway is considered. Charging points are located alongside the motorway. It is assumed that there are a number of vehicles on a given section of a motorway. In the motorway, there are several nodes, and for each vehicle, the entering and the leaving nodes are known, as well as the time of entrance. For each vehicle, we know the total capacity of its battery, and the current amount of energy in the battery when entering the motorway. It is also assumed that for each vehicle, there is a finite set of speeds it can use when traveling the motorway. The speed is chosen when entering the motorway, and cannot be changed before reaching the charging station. For each speed, there is given a corresponding power usage; the higher the speed, the larger the power usage. Each vehicle can only use one charger, and when its battery is full, the amount of energy is sufficient for reaching the outgoing node. We look for a feasible solution to the problem, i.e., a solution in which no vehicle has to wait for a charger. The problem is formulated as a problem of scheduling independent, nonpreemptable jobs in parallel, unrelated machines under an additional doubly constrained resource, which is power. Quantum approaches to solve the defined problem are proposed. They use the quantum approximate optimization algorithm and the quantum annealing technique. A computational experiment is presented and discussed. Some conclusions and directions for future research are given.

The Job Shop Scheduling Problem (JSSP) has always been considered as one of the most complex and industry essential scheduling problems. Optimizing the makespan of a given schedule generally involves using dedicated algorithms, local search strategies, or metaheuristics. These approaches, however, heavily rely on classical computational power, which is bounded by the physical limits of microcontrollers and power issues. Inspired by the promising results achieved for Quantum Annealing (QA) based approaches to solve JSSP instances, we propose a new approach that uses gate-model quantum architecture as an alternative to QA. We find that we can make use of the time-indexed JSSP instance representation to build a cost Hamiltonian, which can be embedded into Quantum Approximate Optimization Algorithm (QAOA) to find an optimal solution to a basic JSSP instance. We demonstrate the use of QAOA to solve the JSSP, and we evaluate its efficiency and accuracy for this problem from experimental results, as there is an increased urgency to demonstrate the applicability of quantum optimization algorithms. We also find that optimal variational parameters form patterns that can facilitate computation in bigger quantum circuits. Additionally, we compare the obtained noiseless simulation results of gate-model quantum circuits demonstrating the relationship between two evaluation criteria - makespan and energy. Finally, we analyze and present the overall performance of our approach with the increasing deadline and simulated depth of QAOA circuits.

As indicated in various recent research, there may still be challenges in achieving acceptable performance using quantum computers for solving practical problems. Nevertheless, we demonstrate promising results by using the recent advent of the D-Wave Advantage quantum annealer to train and test a Restricted Boltzmann Machine for the well studied MNIST dataset. We compare our new model with some tests executed on the previous D-Wave 2000Q system and show an improved image classification process with a better overall quality. In this paper we discuss how to enhance often timeconsuming RBM training processes based on the commonly used Gibbs sampling using an improved version of quantum sampling. In order to prevent overfitting we propose some solutions which help to acquire less probable samples from the distribution by adjusting D-wave control and embedding parameters. Finally, we present various limitations of the existing quantum computing hardware and expected changes on the quantum hardware and software sides which can be adopted for further improvements in the field of machine learning.