Quantum Computing and Machine Learning for Efficiency of Maritime Container Port Operations; Applications and Impacts of Quantum Technology on Fighting Climate Change

Magalhaes, Tiago, School of Engineering and Applied Science, University of Virginia
Baritaud, Catherine, EN-Engineering and Society, University of Virginia
Lambert, James, EN-Eng Sys and Environment, University of Virginia

Emergent technologies such as machine learning and quantum computing provide novel approaches to tackling complex problems ranging from maritime port operations to climate change. The technical research portion of this report focuses on how machine learning, paired with the advanced computing power provided by quantum computing, has the potential to optimize shipping port operations involving primarily the stacking of containers. The efforts behind this task are inspired by the growing demand for more sustainable and environmentally friendly operations at maritime ports, and the belief that operational efficiency represents a realistic option for achieving these objective. The STS research section builds upon the use of emergent technologies to maritime ports and investigates the applications of quantum technology on fighting climate change in order to assess the use of such technologies as a means to tackling the issue. These two studies are therefore tightly coupled given their consideration of quantum computing as an innovative approach to reducing emissions through operational efficiency.
The work for the technical report was carried out with the goal of helping the Port of Virginia achieve their goal of being carbon neutral by 2040. Aware that the Port already employs a series of strategies to meet this objective, such as the implementation of more sustainable energy sources through the use of electric cranes, the capstone team found it necessary to determine alternate approaches not yet in place. Hence, the complex and relatively inefficient operations involving the stacking of containers was targeted with the belief that streamlining this process will lead to emission reductions. The methodology behind the container stacking problem consists of coding the process into a game, which is then run through the Google machine learning program Muzero capable of mastering the game and optimizing the process.
Applying the Muzero algorithm to a simplified version of the container allocation problem proved that this approach has the potential to decrease the average amount of touches per container and improve the process. Therefore, the results are favorable and demonstrate proof of concept, but are not yet applicable given the simplified nature of the simulation. Future steps include running the model on platforms with the processing power capable of solving the problem on a realistic scale, which is where supercomputers and quantum computers become advantageous.
The research question investigated in the STS paper is “what are the applications and impacts of quantum technology on fighting climate change?”. The thesis statement is that the uses of quantum computing will be shaped by society’s greatest needs throughout the technology’s development, and it will therefore provide novel ways to combat climate change. After careful analysis, it was determined that quantum technology has the potential to mitigate climate change by relying on operational efficiency to reduce emissions, and to use its advanced processing power to create more complex and accurate models to predict climate change. To obtain this answer, the paper uses Actor Network Theory, by STS scholars Bruno Latour, Michel Callon, and John Law, to provide a better understanding of the different actors involved in the development of quantum technology. Moreover, the Social Construction of Technology Framework, developed in large part by Wiebe Bijker and Trevor Pinch, is employed to further discuss the specific relationships and interdependencies between the parties and the technology.
The pillars of the analysis are the interactions between quantum computing and other influential agents. Governments and climate change scientists, for example, are constantly pressured to create more accurate climate models in order to enforce the appropriate mitigation measures. This need will therefore guide the development of quantum computing to fill the current technology gap and enable the creation of more accurate predictions. As technologies such as artificial intelligence and machine learning become more complex, their needs for enhanced computing power will also spark investment into quantum as a method of creating synergies among technologies. Industries that are largely dependent on complex operational and logistic problems, such as the maritime ports and aviation industries, may also rely on quantum technology as an approach to optimizing their operations and shape its development to do so.
The technical research therefore serves as a precedent for the STS portion of this paper. A future success in reducing emissions at the Port of Virginia by optimizing operations regarding container stacking may then be expanded to represent a novel approach to mitigating climate change.

BS (Bachelor of Science)
Actor Network Theory, Social Construction of Technology, Quantum computing, Climate change, Optimization

School of Engineering and Applied Science
Bachelor of Science in Systems Engineering
Technical Advisor: James Lambert
STS Advisor: Catherine Baritaud
Technical Team Members: Ibrahim Handy, Sydney Jennings, James Roberts, Maxwell St. John

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