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Calculating faster: Coupling AI with fundamental physics https://lnkd.in/etxzt9Ry

Calculating faster: Coupling AI with fundamental physics https://buff.ly/3WWLxHR

Calculating faster: Coupling AI with fundamental physics https://buff.ly/3WWLxHR

Not All Heroes Wear Capes (or Need Gigabytes of Data)! Quantum mechanics, a field where particles defy our classical intuition with phenomena like non-local interactions, holds immense potential for groundbreaking technologies. However, harnessing this potential hinges on solving a crucial challenge: inverse problems. Inferring the underlying causes behind observed quantum effects is a daunting task. Could machine learning be the key to unlocking the secrets of engineering the quantum world? While Convolutional Neural Networks (CNNs) have become the go-to tool for many tasks, my research suggests a different approach might be more effective for tackling inverse problems in quantum mechanics. The challenge here is the scarcity of data – acquiring it for quantum phenomena can be slow, expensive, and downright difficult. This is where evolutionary search algorithms shine. Unlike CNNs, which require vast amounts of training data, evolutionary search leverages the principles of physics itself to guide the search for solutions. This physics-driven approach makes it a powerful tool for navigating the complexities of the quantum world, even when data is limited. Curious to learn more? Check out the full paper (link below)! #quantum #AI #MachineLearning 🔸 "Machine learning methods for background potential estimation in 2DEGs" https://lnkd.in/gzv-xJ7u

Quantum Machine Learning: The Future of AI 🤖🔮 Let's dive into the exciting world of Quantum Machine Learning (QML) and see how it's transforming AI as we know it! 🚀 What is QML? 🧠💡 - Quantum + Machine Learning: Combining the magic of quantum computing with the power of machine learning. - Qubits: Unlike classical bits, qubits can exist in multiple states simultaneously, making computations super fast! ⚡ Why is QML a Game Changer? 🏆 - Speed & Scale: QML can process massive datasets and solve complex problems at lightning speed. 🚀 - New Algorithms: Innovations like Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs) are paving the way for smarter AI. 🧠 Bridging the Gap 🌉 - Interdisciplinary Synergy: Bringing together quantum physics, computer science, and AI for powerful solutions. 🧩 - Algorithmic Advancements: QML introduces new paradigms that enhance tasks like classification, clustering, and regression. 📊 Scalability & Efficiency 📈 - Parallel Processing: Quantum computers can handle high-dimensional datasets efficiently. 🌐 - Error Mitigation: Techniques like Quantum Error Correction ensure reliable and accurate AI models. ✅ Practical Implementations 🛠️ - Real-World Use: Companies are already using quantum algorithms to improve products and services. 🌍 - Ethical Considerations: Ensuring transparency and security is crucial for harnessing QML advancements. 🔒 Quantum machine learning is opening new frontiers in AI research and application, promising a smarter, more efficient, and secure future. 🌟 What do you think about the potential of QML in everyday applications? 🤔 Share your thoughts and experiences in the comments below! 👇 #QuantumComputing #MachineLearning #AI #Innovation #TechTrends

AI and Quantum Computing. In AI, especially in machine learning, data can be represented as points in a high-dimensional space. A Hilbert Space allows for the representation of these points with well-defined operations like inner products, which are crucial for algorithms like Support Vector Machines (SVMs) and Principal Component Analysis (PCA). Variational inference can be framed as an optimization problem in a Hilbert Space. This involves approximating complex probability distributions with simpler ones by minimizing the divergence between them. The Hilbert Space structure provides a natural setting for these approximations. Quantum algorithms often operate in Hilbert Spaces, leveraging quantum states and operations to perform computations that can be exponentially faster than classical methods. This is an emerging field with significant potential for AI applications. In quantum computing, the state of a quantum system is represented as a vector in a Hilbert Space. Each quantum bit (qubit) corresponds to a vector, and the entire system’s state is a superposition of these vectors. Therefore, Hilbert Spaces provide a robust mathematical framework for various AI techniques, enabling efficient data representation, transformation, and optimization. Quantum computing can be exponentially faster for AI. #HilbertSpace #QuantumComputing

Revolutionizing Machine Learning: Max Planck's Optical Breakthrough 🔍 Did you know? Scientists at the Max Planck Institute for the Science of Light have developed a groundbreaking method to implement neural networks using optical systems, making machine learning more sustainable . 🧠*** What's the relevance ? : • Machine learning and AI are booming, powering everything from computer vision to text generation (like ChatGPT). • But these advancements come with a cost , resulting in immense energy consumption. • This has created a need for faster, more energy- and cost-efficient alternatives, sparking the rapidly developing field of neuromorphic computing. ***Neuromorphic computing • The aim of this field is to replace the neural networks on our digital computers with physical neural networks. • These are engineered to perform the required mathematical operations physically in a potentially faster and more energy-efficient way. • Optics and photonics are particularly promising platforms for neuromorphic computing since energy consumption can be kept to a minimum. • Computations can be performed in parallel at very high speeds only limited by the speed of light. ***Major challenges: • The necessary complex mathematical computations requires high laser powers. • The lack of an efficient general training method for such physical neural networks. ***Innovative Solution from Max Planck • Clara Wanjura and Florian Marquardt have developed a new approach, published in Nature Physics, that tackles these challenges. • Instead of imprinting data input on the light field, their method changes the light transmission. • This results in flexible and efficient processing of input signals without the need for high-power lasers. By avoiding complex physical interactions and using simple wave interference, their approach makes it possible to perform the required mathematical functions in a more sustainable way. Check out the paper to gain more insights: https://lnkd.in/gH6TVK4D Follow us to learn more about innovative research studies and breakthroughs in science and technology #AI #MachineLearning #Sustainability #NeuromorphicComputing #OpticalSystems #Innovation #ScienceNews #TechBreakthrough #MaxPlanckInstitute #EnergyEfficiency

Reinforcement learning meets quantum technology: optimizing cold atom experiments Magneto-optical traps (MOTs) are essential to numerous quantum technologies, including quantum simulation and computing. However, optimizing them is challenging due to the complex interplay of laser cooling, optical pumping, and light-matter interactions. Traditional control methods rely on manually crafted experimental sequences, which often require constant adjustment to account for system perturbations or long-term drifts in experimental conditions. Reinforcement learning (RL) offers a promising alternative by framing MOT control as a sequential decision-making problem. Instead of following predefined rules, an RL agent autonomously learns to optimize the experiment based on a reward function that encodes the desired outcomes. In a recent publication, Malte Reinschmidt, József Fortágh, Andreas Günther, and Valentin V. Volchkov applied deep RL to cold atom experiments, introducing a new approach to optimizing MOTs. Their RL agent utilizes artificial neural networks to process live fluorescence images of the atomic cloud, enabling real-time adjustments of key control parameters such as laser detuning and magnetic field gradients. A standout feature of this method is the successful transfer of training from simulations to real-world experiments. By pre-training the RL agent using a neural network-based MOT simulator, the researchers significantly reduced the time required for training on the actual physical system. This innovative application of reinforcement learning in quantum experiments could lead to more advanced uses in quantum technologies, offering a more efficient and flexible approach to control and optimization. Paper: https://lnkd.in/e3rXxaTB #ReinforcementLearning #QuantumTechnologies #QuantumComputing #MachineLearning #AI #ColdAtoms #Automation #DeepLearning #QuantumControl #AIResearch #QuantumPhysics #EmergingTech

🔍 Exploring the Future of Fluid Dynamics with AI Are you curious about the intersection of fluid dynamics and machine learning? My latest blog post dives into the creation of a Physics-Informed Neural Network (PINN) that simulates fluid flow within a 2D closed system. By embedding the Navier-Stokes equations directly into the neural network, we bridge the gap between traditional physics-based models and cutting-edge AI techniques. This approach not only enhances accuracy but also significantly reduces computational costs. In the article, I walk through the mathematical foundations, implementation steps, and potential applications of PINNs in various industries—from aerospace to environmental modelling. Whether you're a researcher, engineer, or AI enthusiast, this comprehensive guide offers insights into the next generation of simulation technologies. Check out the full article here #AI #FluidDynamics #MachineLearning #PhysicsInformedNN #Simulation #Engineering #Innovation #DataScience #TechInnovation #DeepLearning