Quantum computing, a field utilizing the principles of quantum mechanics, promises great advancements across various industries. This survey paper is focused on the burgeoning intersection of quantum computing and intelligent transportation systems, exploring its potential to transform areas such as traffic optimization, logistics, routing, and autonomous vehicles. By examining current research efforts, challenges, and future directions, this survey aims to provide a comprehensive overview of how quantum computing could affect the future of transportation.

Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier. However, its production, especially green hydrogen generated from renewable sources, is hindered by low efficiency and limited yield, primarily due to the performance of the catalysts used. Developing efficient catalysts typically involves extensive experimental work and trial-and-error processes. For instance, screening for effective catalysts still heavily relies on human-lab-work, a process that is time-consuming. Facing this critical challenge, machine learning (ML) emerges as a promising solution. ML, a core component of data mining and analysis that uses statistical algorithms without explicit instructions, can rationalize the design of catalysts through the use of big data, including DFT results. This approach makes a significant shift from traditional trial-and-error approaches to more computationally driven strategies, offering a more effective path to uncovering vital methodologies for catalyst development. This review aims to capture and evaluate the impact of ML algorithms that have driven progress in catalyst research over the past three years. It presents an overview of the existing ML algorithms, exploring their specific functionalities, benefits, and limitations. Besides, this review also considers prospective solutions and future directions for applying ML to enhance the efficiency of green hydrogen production, particularly through electrochemical and biological processes.

The spiking neural network (SNN) is the third generation of neural networks, valued for its biological interpretability, hardware compatibility, and low power consumption. While field programmable gate array (FPGA), based SNN accelerators show promise with their intelligent design and low power consumption, existing models lack a crucial spiking encoding module. This leads to increased pre-processing time on the CPU. To address these issues, this paper proposes an FPGA-based SNN accelerator with a spiking encoding module. This innovation reduces CPU pre-processing time by threefold without adding FPGA processing time. Achieving 92.62% accuracy on the MNIST dataset using LIF spiking neurons at 0.675 W, the accelerator outperforms CPU and GPU, being 14× faster and 2.5× faster, respectively, and more power-efficient with minimal resource usage. Given its pixel input, it can be seamlessly embedded in real-time classification systems for efficient classification and detection.

Global warming is the most significant challenge to society due to the increasing carbon dioxide level in the atmosphere nowadays. Accelerating the deployment of zero-carbon fuel is imperative in the transportation field, one of the major sources of carbon emissions. The effort on clean energy represented by hydrogen has been made by showing good breakthroughs in some applications; however, many technical and economic challenges exist for large-scale commercial applications. Ammonia, as another zero-carbon fuel, is an efficient storage and transportation medium for hydrogen component and shows greater application potential. Compared to hydrogen, ammonia has more mature storage and transportation technologies, superior related infrastructure, and a further systematic transportation network. In the review, the current mainstream technology routes of ammonia synthesis, the storage and transportation, and the future development trends are summarized. Carbon emissions and costs for the processes are brought into focus. Additionally, the combustion and emission generation mechanisms with technical routes for ammonia as a direct fuel are discussed. The current opportunities and challenges are then analyzed for ammonia as a zero-carbon fuel. The prospect of utilizing ammonia both as a direct fuel and a carrier for hydrogen in transportation is being explored, along with an architecture of the zero-carbon energy system for transportation based on the ammonia fuel.