Quantum Machine Learning (QML) represents a paradigm shift in computational approaches, offering the potential to surpass classical machine learning algorithms in complex data analysis. This paper investigates the application of QML to the identification of non-linear patterns, focusing on leveraging quantum states to uncover hidden correlations and anomalies within datasets. We propose a novel framework that utilizes quantum entanglement and superposition to enhance pattern recognition capabilities. The core mechanism centers on harnessing the inherent properties of quantum states to provide a fundamentally different approach to data analysis, specifically designed to address the challenges posed by non-linear data. This research explores the potential of quantum algorithms to improve pattern detection and anomaly detection, and investigates the influence of quantum characteristics on pattern recognition processes. The paper will detail the implementation of a quantum model for pattern recognition, discuss its performance compared to classical models, and explore the fundamental principles underpinning its effectiveness. We argue that this work introduces a new perspective on pattern recognition, offering the possibility of enhanced accuracy, robustness, and the ability to identify subtle patterns within complex datasets. The core claim posits that quantum machine learning can effectively identify and interpret non-linear patterns, offering significant advantages over conventional methods.
Single photon sources can be used to generate and control highly non-classical states of light. Here, we consider the possibility of generating bi-partite entanglement between two separate multi-photon states by splitting the input modes of the single photon sources into a mode distributed to
A and a mode distributed to
B. If multiple photons are combined at
A and at
B, the large variety of photon patterns observed after linear optics transformations of the local multi-mode systems can be used to characterize the non-classical correlation of this entangled state. However, the transformation between different photon number states in linear optics is a complicated problem, and further analysis is needed to understand the possible applications of this scalable quantum system. To obtain maximal complementarity between two measurements, we consider the unbiased interference of all input modes represented by a discrete Fourier transform of the multi-mode field. We identify characteristic patterns that can be used to identify non-classical correlations between the two multi-photon systems at
A and
B and derive a quantitative criterion for entanglement verification. Our results indicate that the large scale quantum operations can be achieved by a comparatively simple quantum network interfering the outputs of multiple single photons, where the size of the system mainly depends on the number of available single photon sources.