Unsupervised machine learning quantum dynamics

Junyong Lee, Shiho Kim

Prianka Ramachandran Radhabai, Sathya Karunanidhi, Shreyanth Srikanth

Machine Learning (ML) is the process of training machines to analyze and learn from provided data. It can be categorized into three main types: supervised, unsupervised, and reinforcement learning. Unsupervised learning, a method that allows the discovery of underlying patterns in data without the need for additional information or labeled targets, uncovers hidden patterns within the given dataset but lacks associated labels or classes. Unsupervised techniques prove particularly useful for exploring data and comprehending complex behaviors that challenge human identification within large datasets. They find applications in various fields, such as text categorization (e.g., news articles), anomaly detection, satellite and spatial image processing, medical image analysis, customer segmentation, and recommendation engines. Unsupervised learning primarily serves three main tasks: clustering, association, and dimensionality reduction. Clustering is a technique that automatically groups similar samples together based on their inherent characteristics. Association is a technique used to discover relationships between different features within a dataset. Dimensionality reduction is a technique employed to decrease the number of features in a dataset, especially when dealing with high-dimensional data.

Huihui Zhu, Wei Luo, Rudai Yan +11 more

Quantum machine learning stands poised as a forefront application for near-term quantum devices, addressing scalability challenges posed by classical computers in handling large datasets. Gaussian boson sampling (GBS), an intricate quantum algorithm deemed computationally infeasible for classical counterparts, represents a substantial leap forward in computational tasks. However, to date, the benefits of GBS-assisted quantum unsupervised machine learning are not experimentally demonstrated. Here, we present the first experimental implementation of quantum unsupervised machine learning using the GBS protocol with a universal programmable integrated photonic chip. The experimental system contains 16 squeezing sources, a universal programmable unitary matrix network of 16 modes, and a multi-channel single-photon detector, producing substantial output data crucial for 2 typical types of unsupervised tasks: feature extraction and generative network. Compared to classical approaches, the study demonstrates quantum-enhanced capability in extracting complex features from high-dimensional spaces and improved performance in generating arbitrary curve points and reconstructing handwritten digit images. This work not only underscores the potential of GBS in expressing high-dimensional features but also charts a path toward practical implementations within scalable, dimension-enhanced quantum unsupervised machine learning frameworks. The quantum unsupervised machine learning paradigm, offering theoretical acceleration and reduced training parameters for high-dimensional datasets, shows significant promise for advancing real-world applications of quantum technologies.

Ivan B. Djordjević

Lucas Lamata, José D. Martín‐Guerrero

Prakhar Shrivastava, Kapil Kumar Soni, Akhtar Rasool

The paper presents necessity of data pre-processing to process the data set through machine learning algorithms, and it filters the raw data to make the data as in compatible format for analysis purpose. The methods such as mean normalization, feature scaling and dimensionality reduction are used for data smoothing and therefore formatted data set gets processed by learning algorithms to predict best possible outcome as per the analysis. Machine learning became famous for such processing purpose and through unsupervised learning we are capable of processing over various data formats. Such algorithms performance is up-to the mark, but as per the availability of inherent parallel processing through quantum machines, computational speedup can be achieved by designing classical equivalent quantum machine learning algorithms. In this paper, we discussed some of the classical unsupervised learning algorithms, and then we propose the equivalent quantum version of algorithms along with the mathematical justification over the complexity analysis and achieved computational speedup and show the betterment by processing such problems over quantum machines.