Energy Cost of Quantum Circuit Optimisation: Predicting That Optimising Shor’s Algorithm Circuit Uses 1 GWh

Quantum circuits are difficult to simulate, and their automated optimisation is complex as well. Significant optimisations have been achieved manually (pen and paper) and not by software. This is the first in-depth study on the cost of compiling and optimising large-scale quantum circuits with state-of-the-art quantum software. We propose a hierarchy of cost metrics covering the quantum software stack and use energy as the long-term cost of operating hardware. We are going to quantify optimisation costs by estimating the energy consumed by a CPU doing the quantum circuit optimisation. We use QUANTIFY, a tool based on Google Cirq, to optimise bucket brigade QRAM and multiplication circuits having between 32 and 8,192 qubits. Although our classical optimisation methods have polynomial complexity, we observe that their energy cost grows extremely fast with the number of qubits. We profile the methods and software and provide evidence that there are high constant costs associated to the operations performed during optimisation. The costs are the result of dynamically typed programming languages and the generic data structures used in the background. We conclude that state-of-the-art quantum software frameworks have to massively improve their scalability to be practical for large circuits.

Using new computer based techniques to optimise energy consumption in agricultural land levelling

Land levelling is one of the most energy-demanding steps in soil preparation. There are many limiting factors for a specific land levelling operation, such as fertile topsoil conservation, limited allowed slope, specific cut to fill ratio, etc. These limitations make optimisation problems of land levelling even more complicated. In this research, three computational and evolutionary methods including ICA, PSO, GA along with MLS were utilised as optimisation methods to minimise the soil cut and fill volumes and to determine the preferred levelling plane. The results indicated that ICA had the most efficient solution for the energy optimisation in the land levelling among the other investigated methods by saving 29% (17 GJ) of the total energy consumption compared with MLS. This study deals with optimising the energy consumption during land levelling projects using new computer-based techniques and compares them to the MLS method as a benchmark. All in all, ICA, PSO, and GA performed much better than MLS by saving 29, 17, and 10% of the total energy consumption in their best model (number 1 models), respectively. Nonetheless, with these great capacities for saving energy in developing countries, unfortunately, the lack of education and excess subsidies on fossil fuels nullify these potentials.

DAMAGE REDUCTION WITH MAINTAINED COLOUR QUALITY OF ARTWORK UNDER RGB PROJECTOR

Light absorbed by sensitive artwork may cause irreversible damage. Optimising the spectral power distribution of light sources to minimise absorbed light can reduce damage while maintaining the colour appearance of artwork. Previous absorption minimisation studies used optimisation methods without comparing their performance. Here, three channel RGB LED projector spectra was optimised for 24 colour samples by using brute-force (BF) and a multi-objective genetic algorithm (MOGA). BF search and MOGA were similar in achieving optimal results, reducing both light absorption and energy consumption by almost half. MOGA was 2.5 times faster than BF in finding optimal solutions. The results indicate that an LED RGB projector can be used to illuminate museum artifacts to reduce light absorption and energy consumption, with the caveat of perceptible colour shifts in certain samples. Future research will investigate the use of CIECAM02 instead of CIEDE2000 and observers’ subjective evaluations of artwork under optimised lighting.

Active Participation of Buildings in the Energy Networks: Dynamic/Operational Models and Control Challenges

New advances in small-scale generation and consumption technologies have shifted conventional buildings’ functionality towards energy-efficient active buildings (ABs). Such developments drew the attention of researchers all around the world, resulting in a variety of publications, including several review papers. This study conducts a systematic literature review so as to analyse the concepts/factors enabling active participation of buildings in the energy networks. To do so, a relatively large number of publications devoted to the subject are identified, introducing the taxonomy of control and optimisation methods for the ABs. Then, a study selection methodology is proposed to nominate potential literature that has investigated the role of ABs in the energy networks. The modelling approaches in enabling flexible ABs are identified, while the potential challenges have been highlighted. Furthermore, the citation network of included papers is illustrated by Gephi software and analysed using “ForceAtlas2” and “Yifan Hu Proportional” algorithms so as to analyse the insights and possibilities for future developments. The survey results provide a clear answer to the research question around the potential flexibility that can be offered by ABs to the energy grids, and highlights possible prospective research plans, serving as a guide to research and industry.

Swarm Behaviour Optimisation Methods Based on an Original Algorithm

This article presents an authorial swarm algorithm that performs coverage tasks using the Sweep Coverage method. The presented solution assumes stochastic movement of the objects in the swarm which allows them to be simple ones. Our goal was to find an optimal number of objects in the swarm. The main evaluated factors are time and energy consumption. Changing input data allowed us to designate different cases and to examine the influence of varying parameters of a single boid on a whole swarm behaviour.

Methodology for Optimal Placement of Containers in Trains in Case of Cargo Operations Along the Route

A new approach to organisation of container block trains is considered based on the principles of passenger traffic. The technology assumes container train’s traffic subject to the timetable with sale of cargo space in the train. The train is made up at the departure station and follows the established route with stops at intermediate container terminals or stations, where a container for which this station is designated as destination is removed and a new container is placed on the vacated place to be delivered to subsequent points of the route.The objective of this study is to develop a methodology for optimal placement of containers in a block train intended for en route cargo handling operations. The technique involves an iterative search for such an order of placement of packages so that containers assigned to each intermediate point are as close to each other as possible. The technique is an authors’ algorithm based on combinatorial optimisation methods.The implementation of the proposed algorithm makes it possible to reduce the excessive mileage of handlers and loaders at intermediate points and, consequently, to increase speed of cargo operations when rearranging containers, as well as to reduce operating costs of using the loading facilities of the container terminal.The proposed mathematical algorithm as compared to exhaustive search allows significantly reducing the number of iterations in search for a solution and can be implemented as software.