TipQC’s Post

🚀 Excited to share a new blog post on Feature Importance and Explainability in Quantum Machine Learning! Understanding why a prediction is made is critical for transparency and trust in ML, especially in sensitive domains like healthcare and finance. This article explores insights into feature importance and explainability in Quantum Machine Learning (QML) vs. Classical ML models, utilizing the unique capabilities of quantum computing. Check out the full article here: https://bit.ly/3UHYKTI #quantumcomputing #machinelearning #explainability #QML #transparency

𝐑𝐚𝐭𝐞 𝐀𝐈 𝐈𝐝𝐞𝐚𝐬 #𝟏𝟓 ֎ Faster, Easier and Dumber or Better? A QUANTUM COMPUTING idea 🧩 "AI-Enhanced Quantum Computing Simulator 💻 🔍 Quantum Algorithm Discovery: AI explores and identifies new quantum algorithms that could solve complex problems faster than classical algorithms. 🧠 Quantum Machine Learning: AI leverages quantum computing to enhance machine learning models, leading to breakthroughs in data analysis and pattern recognition. 🌐 Quantum Network Optimization: AI optimizes quantum communication networks, ensuring secure and efficient data transfer across quantum systems. 🔬 Quantum Error Correction: AI develops advanced error correction techniques to improve the stability and reliability of quantum computations."

This Paper from LMU Munich Explores the Integration of Quantum Machine Learning and Variational Quantum Circuits to Augment the Efficacy of Diffusion-based Image Generation Models https://flip.it/vYFoyD

FVLLMONTI: The 3D Neural Network Compute Cube (N2C2) Concept for Efficient Transformer Architectures Towards Speech-to-Speech Translation https://lnkd.in/eEmtXZkS

Delve into the future with "Unveiling the Quantum Frontier: A Deep Dive into Quantum Machine Learning." Explore the powerful synergy between quantum computing and machine learning, speeding up optimisation problems and unlocking drug discovery and finance applications. Discover the challenges and potential as quantum machine learning revolutionises computational capabilities. Embrace the quantum revolution with us!

Machine Learning on QC couldn’t have been explained better than this. If you’re new to quantum computing and experienced ML practitioner check this video IBM: https://lnkd.in/gbFaJqTS Discovered earlier but sharing it now ;)

Hi everyone! Exciting news: the TensorKrowch paper has finally been published! 🎉 https://lnkd.in/d6E5Jjzg I know it's been a while since my last update, so let me share the latest advancements in the library: The latest releases have introduced several powerful functionalities to tensorize neural networks, train generative TN models, interpret TNs through reduced densities and entanglement entropies, and tackle physics-inspired problems like energy minimization. - MPS classes have been rebuilt, keeping the original functionality almost intact, but adding many new possibilities. One very useful feature is that now you can select which sites of the MPS are for input/output, and marginalize the output sites upon contraction. - This makes it straightforward to compute marginal distributions, reduced densities and norms. In fact, all these functionalities have also been added as methods of MPS models. Besides, these additions enable to train generative models out of the box. - Also, with the option to marginalize output nodes and the support for complex numbers, you can use TensorKrowch for energy minimization of MPS models, given a Hamiltonian in MPO form. - MPS methods now have the option to renormalize, which allows to train models with more flexible initializations, still getting manageable results. - There are also new initializations and embeddings, so you can try the combination that best fits your problem for training. - New models have been added, like MPO and MPSData. These models, together with the new decompositions vec_to_mps and mat_to_mpo, allow for the seamless tensorization of neural networks. - There is also a new page of examples in the documentation, where you can see how to train different TN models. It includes training MPS models (with and without DMRG), hybrid tensorial neural network models, tensorization of neural networks, and more to come! One of the goals of TensorKrowch is to allow researchers to easily reproduce results/experiments from papers. If you make your own implementations, I encourage you to contribute to the project by adding them to the examples page! I am very happy with the stage TensorKrowch is in right now. It's a friendly tool with which you can explore most of the applications of TNs in machine learning and even use it for physics applications! I hope you find it useful as well! 😊

Graph neural network (GNN) can utilize the network topology and thus information from neighboring nodes of each given node as additional input features to improve the inference accuracy. In this work, we utilized GNN to estimate network congestion and identify lines that may be overloaded and need to be monitored in OPF, resulting in a size-reduced OPF (ROPF) model as compared to a full OPF model that monitors all the lines. Two benchmark models, DNN and CNN, are also implemented for comparison, which demonstrates the effectiveness of the GNN model. Both node features and edge features are utilized for all three models. Simulation results show it can reduce the computing time by 10-20%. Source codes and data can be downloaded here: https://lnkd.in/gnPPSCFX   Credits go to Thuan Pham, MS, P.E. #ML #GNN #OPF #reduced_optimization #size_reduction #network_congestion #powersystems

I am happy to share that I have received the Womanium Global Quantum + AI Project certificate after completing a 6-week industry project on Quantum Machine Learning for Conspicuity Detection in Production. The project aims to optimize production by identifying improvement measures through conspicuity detection using process data analysis. It explores the potential of hybrid quantum computing to accelerate this process by implementing and benchmarking hybrid quantum algorithms against classical methods such as machine learning and statistical approaches. Proud to have been part of this experience and grateful for the opportunity offered by Womanium to grow in this cutting-edge field! #QuantumComputing #QuantumAlgorithms #QuantumMachineLearning #QuantumOptimization #AIResearch WOMANIUM