Quantinuum’s Post

3a. Quantum Algorithms for Language Model Training Optimization (see https://lnkd.in/gjFYze_n) Introduction: Quantum computing has emerged as a promising frontier in computational science, offering unprecedented capabilities to solve complex problems that are intractable for classical computers. One area where quantum algorithms show significant potential is in optimizing the training of language models. In this chapter, we delve into the principles and applications of quantum algorithms for enhancing the efficiency of language model training. Quantum Parallelism and Superposition Quantum algorithms harness the principles of superposition and parallelism to perform computations at a speed exponentially faster than classical algorithms. Language model training involves large-scale optimization tasks that can benefit from the inherent parallelism in quantum systems. Quantum bits or qubits can exist in a superposition of states, allowing simultaneous computation of multiple possibilities. This property enables quantum algorithms to explore a vast solution space simultaneously, offering a potential speedup in training language models. Quantum Fourier Transform and Gradient Descent Optimization: One key aspect of language model training is the optimization of model parameters through techniques like gradient descent. Quantum Fourier Transform (QFT) can be employed to efficiently implement certain operations involved in optimization processes. QFT allows for the rapid computation of convolutions and correlations, crucial steps in gradient descent algorithms. Quantum gradient descent algorithms could potentially outperform their classical counterparts by exploiting the quantum parallelism inherent in QFT. QFT∣y⟩ = (1/√N) ∑ₓ⁽₌ᷡ₀⁻¹⁾ e⁽²ᣕⁱˣʸ/ᴺ⁾∣x⟩ Quantum Enhanced Feature Mapping: Language models often rely on the extraction of meaningful features from input data. Quantum algorithms can provide enhanced feature mapping through quantum algorithms such as the Quantum Support Vector Machine (QSVM) algorithm. QSVM leverages quantum parallelism to efficiently map high-dimensional input data into a higher-dimensional feature space, facilitating more effective learning of complex patterns in language data. ∣ Φ(x)⟩ = (1/√N) ∑ⱼᷡ₋₁ ∣yⱼ⟩ ∣ϕⱼ(x)⟩ continued over 3b (https://lnkd.in/gasMFbx3)

Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing From the Editor's web site: This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning. https://lnkd.in/dWtfKM2U

The most comprehensive PyTorch learning repository. The most comprehensive GitHub repository focused on content related to the Python-based computational library, PyTorch, is the Awesome PyTorch List. This repository includes libraries related to PyTorch in topics such as natural language processing, computer vision, and probability, various models, as well as educational materials in the form of tutorials, books, and diverse examples. PyTorch and related libraries: [Pytorch & Related Libraries](https://lnkd.in/dn8WSJd3) Tutorials, books, and examples: [Tutorials, Books & Examples](https://lnkd.in/dEBDjsxa) Articles and their implementations: [Paper Implementations](https://lnkd.in/deeKHG4t) To access this comprehensive GitHub repository, simply click on the link below: - Awesome Pytorch - GitHub Repos: [GitHub-Repos](https://lnkd.in/dApYAzKj)

I'm excited to share a PDF document that I had the opportunity to contribute to, which focuses on the mathematical foundations of deep learning algorithms. The document is organized into several key sections: Chapter 1: An Introduction to Learning in Deep Networks Chapter 2 & 3: The Role of Normalization in Learning Efficacy Chapter 4: Investigating the Effects of Using Unconventional Activation Functions like Chebyshev Polynomials Chapter 5: The Influence of Polynomial Expansion in Activation Functions on Learning Remarkably, all the mathematical models and algorithms discussed were implemented in Fortran, despite the absence of specialized deep learning libraries in this language. This showcases the universality and robustness of the mathematical concepts involved. Although the PDF is in Portuguese, the mathematical theories are universally applicable. Feel free to download and explore the document to deepen your understanding of these complex topics. #deeplearning #mathematics

Excited to announce my completion of the Machine Learning Specialization from Stanford University! Throughout the program which consists of 3 different courses, I've explored a wide range of topics, including supervised and unsupervised learning, deep learning, natural language processing, computer vision, and more. Each course provided invaluable insights and hands-on experience, allowing me to understand complex algorithms and apply them effectively. My Advice: This specialization is ranked as the top specialization in Machine Learning. You can complete all the whole specialization in 2 month if you are able to dedicate yourself for 10 hours a week. However, If this not possible you may extend for extra months. As a prerequisite to attend this course you need to have a good understand in any programming language as well as an advanced level on algebra. Otherwise, It will be very difficult for you to pass more than 15 assignments with advanced coding labs and you will not be able to get your certification or benefit from this great course.

Llemma models are 7 billion and 34 billion parameter language models for Mathematics. The Llemma models were initialized with Code Llama weights, then trained on Proof-Pile II, a 55 billion token dataset of mathematical and scientific documents. The resulting models show improved mathematical capabilities and can be adapted to various tasks through prompting or additional fine-tuning.

Quantum Machine Learning Quantum Machine Learning (QML) is an emerging field that combines the principles of quantum mechanics and machine learning to develop new algorithms and models that can process and analyze large amounts of data more efficiently and accurately than classical machine learning methods. In traditional machine learning, data is processed using classical algorithms, which rely on the principles of probability theory and linear algebra. However, these methods can become computationally expensive and inefficient when dealing with large-scale datasets, high-dimensional spaces, or complex relationships between variables. Full article: https://ml-nn.eu/41.html #Learntoday #MachineLearning #NeuralNetworks #Python #AI

New Language Model Breakthrough on Nature: FunSearch Addresses A Longstanding Mathematics Challenge In a recent paper titled "Mathematical Discoveries from Program Search with Large Language Models, a research team introduces FunSearch—a novel approach that elevates LLM-guided evolutionary procedures. FunSearch not only achieves breakthroughs in established open problems but also leads to the discovery of new algorithms. Latest from Synced Review https://lnkd.in/edHNhskV

Absolutely thrilled to announce that mahmoud adel, mohamed tarek, Dina Othman, and I have successfully completed an Image Classification project using Python as part of our Machine Learning course at the Faculty of Computers and Artificial Intelligence, Cairo University. The primary goal of our project was to develop a classification system for handwritten digits, utilizing the MNIST dataset. Our focus was on honing our skills in using the Tensorflow framework and the Scikit-Learn library, gaining proficiency in Artificial Neural Networks (ANNs) and K-Nearest Neighbors (K-NN), and comprehending the intricacies of hyperparameter tuning. You can explore our project on GitHub: GitHub Repo  https://lnkd.in/dHmrUct5 Excited to share this accomplishment with my dedicated team! 🚀 #MachineLearning #Teamwork #ArtificialIntelligence #ImageClassification #PythonProgramming #CairoUniversity

DeepMind FunSearch made the first discoveries in open problems in mathematical sciences using Large Language Models (LLMs) by searching for “functions” written in computer code. Alhussein Fawzi and Bernardino Romera Paredes, FunSearch: Making new discoveries in mathematical sciences using Large Language Models, 14 December 2023, DeepMind, https://lnkd.in/efxs9CAW Romera-Paredes, B., Barekatain, M., Novikov, A. et al. Mathematical discoveries from program search with large language models. Nature (2023). https://lnkd.in/ehM6Zw58 #llms #computerscience #mathematics #computercode