There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Integer programming formulations of discrete hub location problems

Read the fine print.

As supply chain management is becoming demand driven, logistics service providers need to use real-time information efficiently and integrate new technologies into their business. Synchromodal logistics has emerged recently to improve flexibility in supply chains, cooperation among stakeholders, and utilization of resources. We survey the existing scientific literature and real-life developments on synchromodality. We focus on the critical success factors of synchromodality and six categories of enabling technologies. We identify open research issues and propose the introduction of a new stakeholder, which takes on the role of orchestrator to coordinate and provide services through a technology-based platform.

Synchromodal logistics: An overview of critical success factors, enabling technologies, and open research issues

Survivable Networks Algorithms For Diverse Routing

This paper presents the Talavera Manifesto for quantum software engineering and programming. This manifesto collects some principles and commitments about the quantum software engineering and programming field, as well as some calls for action. This is the result of the discussion and different viewpoints of academia and industry practitioners who joined at the first International Workshop on QuANtum SoftWare Engineering & pRogramming (QANSWER

The Talavera Manifesto for Quantum Software Engineering and Programming

System for processing a communication session to be set up via a communication path from an originating location (ASD) to a destination location (TYO) through a communication network. A common Service Level Agreement (SLA) registry contains Quality-of-Service (QoS) related parameters representative for the QoS valid for the domains between the originating location and the destination location. A QoS processor is adapted for processing QoS related parameters which are retrieved from the SLA registry, for predicting the end-to-end QoS values for one communication path between the originating and destination location and for ranking or selecting or both ranking and selecting, based on the predicted end-to-end QoS values, one or more recommended communication paths between the originating location and the destination location. The Quality-of-Service (QoS) processor may be adapted for processing the QoS parameters retrieved from the SLA registry and predicting end-to-end QoS values representative all communication paths between each network domain to each other network domain and for storing said predicted end-to-end QoS values in a QoS registry for selection and/or ranking actions for new session requests in future.

System And Method For Processing Quality-Of-Service Parameters In A Communication Network

The first quantum computers are expected to perform well at quadratic optimisation problems. In this paper a quadratic problem in finance is taken, the Portfolio Optimisation problem. Here, a set of assets is chosen for investment, such that the total risk is minimised, a minimum return is realised and a budget constraint is met. This problem is solved for several instances in two main indices, the Nikkei225 and the S\&P500 index, using the state-of-the-art implementation of D-Wave's quantum annealer and its hybrid solvers. The results are benchmarked against conventional, state-of-the-art, commercially available tooling. Results show that for problems of the size of the used instances, the D-Wave solution, in its current, still limited size, comes already close to the performance of commercial solvers.

Portfolio Optimisation Using the D-Wave Quantum Annealer.

https://scholarlypublications.universiteitleiden.nl/access/item%3A2867972/view

Data granularity and the optimal planning of distributed generation

Everyday computers can be used to solve numerous tasks which are often too difficult for humans to do quickly. Whether it is finding the shortest route between your home and your work or automatically finding the solutions to puzzles such as sudoku’s, computers can solve these problems faster and often better than humans can. On the other hand, there are problems that are easy for humans, but much harder for computers to do automatically, such as face identification or pattern recognition in images. The state-of-the-art technique for solving these problems is machine learning. Different types of machine learning exist, most of them boiling down to supplying data to a computer, which then learns to produce a required outcome. The more data is given, the closer the outcome will be to the actual solution or the higher the probability will be that the correct solution is found.

Frank Phillipson

Papers

The Talavera Manifesto for Quantum Software Engineering and Programming

System And Method For Processing Quality-Of-Service Parameters In A Communication Network

Portfolio Optimisation Using the D-Wave Quantum Annealer.

Data granularity and the optimal planning of distributed generation

Machine learning in the quantum era