Quantum Probability Tree: A New Model for Quantum Systems

"Machine Learning Experimental Proof of Bell’s Inequality" Abstract This study presents a computational experiment demonstrating the violation of Bell’s Inequality through machine learning simulation. By modeling entangled particle correlations and applying data-driven analysis, the research shows how quantum mechanical predictions defy local realism. Using linear regression to approximate quantum correlations, this work replicates the foundational results of Bell (1964) and Freedman–Clauser (1972) in a digital environment, offering an educational bridge between quantum theory and artificial intelligence. #belltheorem #machinelearning #quantumphysics #quamtumentalgelment #inequality #bellsinequality #localrealism #hiddenveriable Bells inequality by machine learning.ipynb - Colab https://lnkd.in/gMEHF6BU

"Machine Learning Experimental Proof of Bell’s Inequality" Abstract This study presents a computational experiment demonstrating the violation of Bell’s Inequality through machine learning simulation. By modeling entangled particle correlations and applying data-driven analysis, the research shows how quantum mechanical predictions defy local realism. Using linear regression to approximate quantum correlations, this work replicates the foundational results of Bell (1964) and Freedman–Clauser (1972) in a digital environment, offering an educational bridge between quantum theory and artificial intelligence. #belltheorem #machinelearning #quantumphysics #quamtumentalgelment #inequality #bellsinequality #localrealism #hiddenveriable

Quantum Computing Series (Part 8) More Quantum Algorithms You Must Know In the last blog, we explored Shor, Grover, VQE, QAOA, and Quantum Simulation. But the world of quantum algorithms is much bigger ,let’s look at a few more must-know ones that are shaping the future. 1. Quantum Fourier Transform (QFT) What it does: Breaks down complex signals into simple wave patterns. Impact: It’s the backbone of many quantum algorithms (including Shor’s!). Simple analogy: Imagine hearing a full orchestra. QFT helps you separate the sound of each instrument clearly. 2. Harrow-Hassidim-Lloyd (HHL) Algorithm What it does: Solves systems of linear equations exponentially faster than classical methods. Impact: Crucial for machine learning, big data, and engineering simulations. Simple analogy: Suppose you’re solving 1,000 equations with 1,000 unknowns. A classical computer would take ages, but HHL can give you answers lightning fast. 3. Quantum Walk Algorithms What they do: Extend the idea of random walks (used in computer science and physics) into the quantum world. Impact: Used for faster network analysis, graph problems, and even search. Simple analogy: Imagine wandering around a maze. A classical “random walker” goes step by step, but a quantum walker explores many paths at once. 4. Quantum Phase Estimation (QPE) What it does: Estimates the “phase” (a kind of fingerprint) of a quantum state. Impact: Key part of algorithms like Shor’s and useful in chemistry & physics simulations. Simple analogy: Like tuning a guitar ,QPE helps you find the exact pitch (phase) of a note. Each algorithm is like a unique tool in a toolbox. Some break codes (Shor), some speed up searches (Grover), some optimize real-world systems (QAOA, Annealing), and some form the backbone of everything else (QFT, QPE). Quantum algorithms aren’t just “cool tricks” ,they’re the reason we believe quantum computers will transform cybersecurity, AI, finance, medicine, and much more. #QuantumComputing #QuantumAlgorithms #QFT #HHL #QuantumAnnealing #FutureTech #MachineLearning

🚀 Physics-Informed Deep Learning for Turbulent Flows Predicting turbulence remains one of the most complex challenges in fluid dynamics. The UCSD Machine Learning Group is bridging the gap between deep learning and CFD physics with a novel hybrid model that combines: ✅ RANS-LES coupling ✅ Trainable U-Net architecture ✅ Multi-scale spectral decomposition ✅ Physics constraints (e.g., divergence-free regularization) 🔍 Their model - TF-Net - not only outperforms traditional deep learning baselines in accuracy, but also maintains physical consistency across predictions of turbulent velocity fields. It learns directly from data, yet respects the governing Navier–Stokes equations. Read more: https://lnkd.in/ex4a9zWa #engineering #artificialintelligence #turbulence

This 250-year-old equation just got a quantum makeover: A team of international physicists has brought Bayes’ centuries-old probability rule into the quantum world. By applying the “principle of minimum change” — updating beliefs as little as possible while remaining consistent with new data — they derived a quantum version of Bayes’ rule from first principles. Their work connects quantum fidelity (a measure of similarity between quantum states) to classical probability reasoning, validating a mathematical concept known as the Petz map. #ScienceDaily #Technology

Is artificial intelligence getting a quantum upgrade? Or is quantum all hype? Find out in this explainer page that delves into quantum computing and how it can work alongside AI to solve increasingly complex problems. http://2.sas.com/6045AdywV

Is artificial intelligence getting a quantum upgrade? Or is quantum all hype? Find out in this explainer page that delves into quantum computing and how it can work alongside AI to solve increasingly complex problems. http://2.sas.com/6041Ac6tF

Is artificial intelligence getting a quantum upgrade? Or is quantum all hype? Find out in this explainer page that delves into quantum computing and how it can work alongside AI to solve increasingly complex problems. http://2.sas.com/6042A5C64

𝐐𝐮𝐚𝐧𝐭𝐮𝐦 × 𝐀𝐈 | 𝐓𝐡𝐞 𝐁𝐫𝐢𝐝𝐠𝐞 𝐖𝐞 𝐀𝐫𝐞 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 When we talk about the convergence of Artificial Intelligence and Quantum Computing, most only imagine raw power. What few consider is the language that must exist between them—the instruction set capable of allowing intelligence itself to call upon the quantum domain as a native extension of thought. Over the last months, I’ve been researching and analyzing every architecture that has attempted this connection—OpenQASM 3, QIR, CUDA-Q, Catalyst, TensorFlow Quantum, and beyond. Each offers brilliance, but each stops short of what the future requires: a truly hybrid system where classical ML graphs and quantum programs coexist, exchange gradients, share cost models, and learn from one another in real time. Our goal now is to engineer that bridge—a new machine language and intermediate representation able to unify these worlds. It must handle gradients and probabilities as seamlessly as memory and time, include provenance and cost awareness at its core, and treat quantum operations not as experiments, but as first-class citizens of intelligence. Innovation in this space isn’t about faster code—it’s about teaching machines why to reach into the quantum, not just how. The era of QAML begins. #CybersecurityInsiders #SingularitySystems #Quantum #ArtificialIntelligence #ChangeTheWorld

Is artificial intelligence getting a quantum upgrade? Or is quantum all hype? Find out in this explainer page that delves into quantum computing and how it can work alongside AI to solve increasingly complex problems. http://2.sas.com/6047Abskd