Quantum approaches to data-efficient self-supervised learning (SSL) have the potential to change the game of industrial automation, revolutionizing our ability to extract power from large datasets without excessive reliance on data. By employing this strategy, the computing power of quantum computing can be utilized to overcome the difficulties encountered by traditional machine learning paradigms, especially in manufacturing where the number of available data is large but limited data is available. Quantifying information using quantum algorithms leads to enhanced automation processes in many industries, simplified data use, and better decisions. Self-supervised learning is a paradigm in which models can learn from unlabelled data by creating supervisory signals out of the data itself. This is especially important for industrial automation, where data is slow and expensive. Since SSL requires lengthy algorithms and mathematically based multi-step processes, quantum computing significantly speeds up this process by offering better processing, enabling the abundant availability of information to be analysed at once instead of sequentially. This allows for meaningful patterns and insights to be extracted quickly from the large amounts of data produced in the manufacturing process (including sensor readings, operational logs, etc.). SSL is a natural fit for implementing quantum methods, providing a seamless way to automate the quality assurance process and predict failures. As an example, quantum-enhanced SSL can identify anomalies in manufacturing processes by continuously processing data from sensors, and flagging deviations that may signal imminent defects or failures long before they become major problems. By improving product quality and reducing downtime along with maintenance costs it optimizes overall operational efficiency. This is evident due to the operation of quantum algorithms that can be implemented on quantum devices to address the more complex modelling needed to reflect the evolving nature of industrial environments. Historical data is updated continuously and helps in making accurate predictions and adaptive responses to changes on the floor of the factory. This translates to increased agility for organizations to respond to market demands and operational challenges. However high-dimensional data in industrial settings can also be addressed by implementing the learned experience in quantum methods for SSL. Traditional machine learning methods can struggle with this complexity because of the constraints of the computational resources available. Many factors affect production efficiency, including complex datasets; quantum computing can analyze these datasets since it can perform multiple computations simultaneously. However, there are challenges in moving to quantum-enhanced SSL. Technical barriers related to both quantum hardware integration the development of quantum algorithms and the development of models must be navigated by organizations. This risk can be significantly exacerbated when it comes to implementing this technology, but working with specialized providers can help reduce this risk, offer assurance that the deployment is going to be successful, and maximize its return. With industries more adapted to quantum computing, collaborators will play an essential role in quantum computing implementation for these industries. Quantum techniques for data-efficient self-supervised learning are a game changer for industrial automation. Quantum computing harnesses the potential of these new technologies, and by doing so organizations would not only increase the efficiency of data processing but also the quality assurance and predictive maintenance. Industries are being propelled into unprecedented technological manufacturing trajectories with diverse applications, from real-time anomaly detection to adaptive process control, validating the accuracy of these Industry 4.0 buzzwords.