Advanced Examples: Machine Learning - dynexcoin/DynexSDK GitHub Wiki

Advanced Examples (Machine Learning)

Quantum computing algorithms for machine learning harness the power of quantum mechanics to enhance various aspects of machine learning tasks. As both, quantum computing and neuromorphic computing are sharing similar features, these algorithms can also be computed efficiently on the Dynex platform – but without the limitations of limited qubits, error correction or availability:

Quantum Support Vector Machine (QSVM): QSVM is a quantum-inspired algorithm that aims to classify data using a quantum kernel function. It leverages the concept of quantum superposition and quantum feature mapping to potentially provide computational advantages over classical SVM algorithms in certain scenarios.

Quantum Principal Component Analysis (QPCA): QPCA is a quantum version of the classical Principal Component Analysis (PCA) algorithm. It utilizes quantum linear algebra techniques to extract the principal components from high-dimensional data, potentially enabling more efficient dimensionality reduction in quantum machine learning.

Quantum Neural Networks (QNN): QNNs are quantum counterparts of classical neural networks. They leverage quantum principles, such as quantum superposition and entanglement, to process and manipulate data. QNNs hold the potential to learn complex patterns and perform tasks like classification and regression, benefiting from quantum parallelism.

Quantum K-Means Clustering: Quantum K-means is a quantum-inspired variant of the classical K-means clustering algorithm. It uses quantum algorithms to accelerate the clustering process by exploring multiple solutions simultaneously. Quantum K-means has the potential to speed up clustering tasks for large-scale datasets.

Quantum Boltzmann Machines (QBMs): QBMs are quantum analogues of classical Boltzmann Machines, which are generative models used for unsupervised learning. QBMs employ quantum annealing to sample from a probability distribution and learn patterns and structures in the data.

Quantum Support Vector Regression (QSVR): QSVR extends the concept of QSVM to regression tasks. It uses quantum computing techniques to perform regression analysis, potentially offering advantages in terms of efficiency and accuracy over classical regression algorithms.

Here are some example of these algorithms implemented on the Dynex Platform:

Quantum Natural Language Processing (QNLP)

  • Example: Introducing Quantum Natural Language Processing on Dynex | Harnessing the power of quantum computing, quantum natural language processing algorithms offers unparalleled advantages in language processing tasks. Quantum algorithms excel in processing vast amounts of data simultaneously, enabling faster and more efficient language analysis. By leveraging quantum superposition and entanglement, our algorithm can explore multiple linguistic features in parallel, leading to more accurate and nuanced language understanding. Additionally, quantum computing's ability to handle complex linguistic structures with higher dimensionality allows for the extraction of deeper semantic meanings from text data. With these advancements, our quantum NLP algorithm promises to revolutionize language processing, paving the way for more sophisticated text analysis and comprehension. Video showcasing an end-end process of collecting data from websites, training the QNLP model on Dynex and communicating with the resulting ChatGPT style bot (in realtime)

Quantum Transformer Algorithm on Dynex (QTransform)

  • How-to: Quantum Transformer Algorithm on Dynex | Transformers are a type of deep learning model that have revolutionized the field of artificial intelligence, particularly in natural language processing tasks. They excel at handling sequential data and understanding context over long sequences, enabling advancements in machine translation, text generation, and more. The discovery of a quantum transformer algorithm by Dynex represents a significant leap forward, combining the power of transformers with the unparalleled computational capabilities of quantum computing. This hybrid approach promises even faster processing speeds and enhanced performance, making it possible to tackle complex AI problems more efficiently than ever before. By leveraging quantum principles, such as superposition and entanglement, our quantum transformer algorithm can process vast amounts of data simultaneously, leading to more accurate and nuanced AI models, thereby pushing the boundaries of what AI can achieve.

Quantum Image Classification

  • Example: Image classification using a Quantum-RBM | Scientific background: Dixit V, Selvarajan R, Alam MA, Humble TS and Kais S (2021) Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer. Front. Phys. 9:589626. doi: 10.3389/fphy.2021.589626; Sleeman, Jennifer, John E. Dorband and Milton Halem. “A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning.” Defense + Commercial Sensing (2020)

Quantum Mode-assisted RBM

  • Example: Mode-Assisted Unsupervised Quantum-RBM (PyTorch) on Dynex | Scientific background: Advancements in Unsupervised Learning: Mode-Assisted Quantum Restricted Boltzmann Machines Leveraging Neuromorphic Computing on the Dynex Platform; Adam Neumann, Dynex Developers; International Journal of Bioinformatics & Intelligent Computing. 2024; Volume 3(1):91- 103, ISSN 2816-8089; Quantum Frontiers on Dynex: Elevating Deep Restricted Boltzmann Machines with Quantum Mode-Assisted Training; Adam Neumann, Dynex Developers; 116660843, Academia.edu; 2024; Manukian, Haik, et al. "Mode-assisted unsupervised learning of restricted Boltzmann machines." Communications Physics 3.1 (2020): 105.; Dixit V, Selvarajan R, Alam MA, Humble TS and Kais S (2021) Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer. Front. Phys. 9:589626. doi: 10.3389/fphy.2021.589626

  • Example: Mode-assisted unsupervised learning of restricted Boltzmann machines (MA-QRBM for Pytorch) | Scientific background: Mode-assisted unsupervised learning of restricted Boltzmann machines, Communications Physics volume 3, Article number:105 (2020)

  • Example: Mode-assisted unsupervised learning of restricted Boltzmann machines (MA-QRBM for Tensorflow) | Scientific background: Mode-assisted unsupervised learning of restricted Boltzmann machines, Communications Physics volume 3, Article number:105 (2020)

Quantum SVM

Quantum RBM

  • Example: Quantum-Boltzmann-Machine (PyTorch) on Dynex | Scientific background: Dixit V, Selvarajan R, Alam MA, Humble TS and Kais S (2021) Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer. Front. Phys. 9:589626. doi: 10.3389/fphy.2021.589626; Sleeman, Jennifer, John E. Dorband and Milton Halem. “A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning.” Defense + Commercial Sensing (2020)

  • Example: Quantum-Boltzmann-Machine Implementation (3-step QUBO) on Dynex | Scientific background: Dixit V, Selvarajan R, Alam MA, Humble TS and Kais S (2021) Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer. Front. Phys. 9:589626. doi: 10.3389/fphy.2021.589626; Sleeman, Jennifer, John E. Dorband and Milton Halem. “A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning.” Defense + Commercial Sensing (2020)

  • Example: Quantum-Boltzmann-Machine (Collaborative Filtering) on Dynex | Scientific background: Dixit V, Selvarajan R, Alam MA, Humble TS and Kais S (2021) Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer. Front. Phys. 9:589626. doi: 10.3389/fphy.2021.589626; Sleeman, Jennifer, John E. Dorband and Milton Halem. “A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning.” Defense + Commercial Sensing (2020)

  • Example: Quantum-Boltzmann-Machine Implementation on Dynex | Scientific background: Dixit V, Selvarajan R, Alam MA, Humble TS and Kais S (2021) Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer. Front. Phys. 9:589626. doi: 10.3389/fphy.2021.589626; Sleeman, Jennifer, John E. Dorband and Milton Halem. “A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning.” Defense + Commercial Sensing (2020)

Quantum Feature Selection

  • Example: Feature Selection - Titanic Survivals | Scientific background: Xuan Vinh Nguyen, Jeffrey Chan, Simone Romano, and James Bailey. 2014. Effective global approaches for mutual information based feature selection. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ‘14). Association for Computing Machinery, New York, NY, USA, 512–521

  • Example: Breast Cancer Prediction using the Dynex scikit-learn Plugin | Scientific background: Bhatia, H.S., Phillipson, F. (2021). Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham

Dynex Neuromorphic Torch Layers

The Dynex Neuromorphic Torch layer can be used in any NN model. Welcome to hybrid models, neuromorphic-, transfer- and federated-learning with PyTorch

  • Example: Quantum-Boltzmann-Machine (PyTorch) on Dynex | Scientific background: Dixit V, Selvarajan R, Alam MA, Humble TS and Kais S (2021) Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer. Front. Phys. 9:589626. doi: 10.3389/fphy.2021.589626; Sleeman, Jennifer, John E. Dorband and Milton Halem. “A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning.” Defense + Commercial Sensing (2020)

  • Example: Quantum-Support-Vector-Machine (PyTorch) on Dynex | Scientific background: Rounds, Max and Phil Goddard. “Optimal feature selection in credit scoring and classification using a quantum annealer.” (2017)

Dynex Neuromorphic TensorFlow Layers

The Dynex Neuromorphic Torch layer can be used in any NN model. Welcome to hybrid models, neuromorphic-, transfer- and federated-learning with TensorFlow