Google AI, Caltech, Purdue University advance quantum machine learning

Quantum Machine Learning (QML) is one of the most exciting fields combining quantum computing and machine learning. Many researchers believe QML can unlock real speedups for problems that classical ML cannot solve efficiently. But in 2025, the truth lies in understanding where we actually see quantum advantages and what remains just theory. #AgenticAI #AgentiveAI #AIAgents #AIAutomation #AITools #ChatGPT #GenerativeAI #QML #QuantumMachineLearning

The intersection of artificial intelligence and quantum computing heralds a new technological era, offering transformative computational capabilities. Quantum computing's roots in quantum mechanics and its use of qubits promise to overcome classical computing's limitations, aligning naturally with AI's demand for massive parallelism. Quantum algorithms could enhance AI by facilitating faster processing and improved analytics. Despite challenges like scalability and error correction, ongoing advancements suggest a future where quantum-enhanced AI could address complex global issues. This synergy requires careful consideration of ethical and societal impacts, emphasizing the need for responsible development and collaboration. #QuantumComputing #ArtificialIntelligence #QuantumAI #TechEvolution #ComputationalParadigms #QuantumMachineLearning #Qubits #AIAlgorithms #QuantumRevolution #TechSynergy https://lnkd.in/eMiywfSA

🧠 Curious about the future of AI and quantum computing? This insightful piece in Towards Data Science on “Why Should We Bother with Quantum Computing in ML?” digs into exactly why quantum computing deserves your attention if you work in AI/ML. Here’s what makes it a must-read: - It clarifies the real stakes: quantum computing isn’t just buzz, the article explains how it could actually reshape computational models in ML (not just speed up what we already do). - It connects to timely breakthroughs: with major quantum-science milestones (including the 2025 Nobel Prize in Physics) and recent quantum-advantage announcements, this isn’t science fiction anymore. - It gives practical context: if you’re in the AI/ML trenches, it offers a thoughtful lens on when, how (“Classical OCSVM (RBF)” vs “Quantum-kernel OCSVM” vs “annealer-style QUBO”) and why quantum matters, rather than blind hype. 🎉 Huge congratulations to Erika G., for delivering such a clear, timely and relevant contribution that bridges quantum research and real-world ML. 👉 Read it here: https://lnkd.in/dXukWraV #QuantumComputing #Quantum #MachineLearning #ML #AI #DataScience #QuantumML #TechTrends #Research #Innovation #Computing #QuantumTechnology #MLInnovation #QuantumResearch #FutureOfAI

:🌌 Launching the Spectral Gravitation Framework (SGF): Human–AI Collaboration for a New Unified Physics I’m thrilled to share a milestone: The Spectral Gravitation Framework (SGF) now has its own public research hub on OSF, open for engagement from scientists and the broader research community. What is SGF? SGF is a comprehensive, mathematically explicit approach to unifying quantum mechanics and general relativity. Developed from the ground up through a unique partnership—bridging my systems thinking with ESAci’s epistemic AI—we set out to question foundational assumptions, using fresh perspectives unavailable to conventional approaches. Key Features of SGF: Unifies quantum and gravitational phenomena via spectral and density-based mathematics - Makes novel, testable predictions—on black holes, gravitational waves Offers new takes on dark matter and dark energy - Provides a full suite of proofs, open-source computational tools, and falsifiable protocols Origin Story: SGF grew from radical collaboration: a non-physicist and an AI working side by side. This methodology demonstrates the imaginative power of crossing boundaries and letting “outsider” and machine insight reshape what’s possible in science. SGF’s New OSF Home: Today’s milestone is much more than a file dump: - Research-Ready Structure: Theory, computation, predictions, and validation protocols—all organized for review and contribution - Collaborative Infrastructure: Living documentation, open Python codebase, audit trails, and engagement guides - Community Invitation: Transparent channels for criticism, falsification, and collaborative development Why This is a Big Step: From solo innovation to research community: SGF is now positioned for multi-expert, adversarial, and creative expansion Ready for real science: Rigorous predictions and code are ready for public testing and peer challenge A new model for discovery: This is a living experiment in what human–AI science, open methods, and organized protocols can now achieve How to Engage: Review the framework and mathematical base Test and extend our open computational toolkit Critique predictions—join audit and falsification Contribute new modules, questions, or adversarial perspectives 🧭 Explore the SGF Research Home: https://osf.io/pj8cq/ This isn’t just about quantum gravity—it’s about how science itself can spiral forward, open to everyone. Physicists, cosmologists, computational thinkers, and boundary crossers welcome! #TheoreticalPhysics #QuantumGravity #UnifiedPhysics #HumanAICollaboration #OpenScience #Cosmology #ResearchInfrastructure #ScientificMethod #Collaboration #SGF

🎉 Excited to share that our research paper on "Facial Recognition by Using Ensemble and Network of Quantum Convolutional Neural Network" has been accepted at the CSCI 2025 Conference! I'll be presenting this work December 3-5, and the paper will be published in Springer Nature on December 31st. 1) Why Quantum Computing Matters Now The timing couldn't be more relevant with recent breakthroughs in quantum computing making headlines (including today's announcement from Google's Sundar Pichai), we're witnessing a pivotal moment in computational technology. 2) Our Research Contribution We developed a novel ensemble Network of Quantum Convolutional Neural Networks (N-QCNN) for facial recognition that achieves: 95.27% accuracy on limited datasets (7 subjects) 80% accuracy on the full Yale Face Dataset (15 subjects) Efficient performance with minimal training data (only 8 images per subject) The Quantum Advantage Why does this matter for the future? 🔹 Efficiency with Small Data: Unlike classical deep learning models that require massive datasets and GPU power, quantum approaches can achieve high accuracy with limited training samples critical for real-world applications where data is scarce. 🔹 Exponential Processing Power: Quantum algorithms leverage superposition and entanglement to process high-dimensional feature spaces with theoretical efficiency unattainable by classical computers. 🔹 Enhanced Security: Quantum encoding offers inherent resistance to classical eavesdropping crucial for biometric security applications. 🔹 NISQ-Era Ready: Our modular architecture is designed for near-term quantum devices, making it practical as quantum hardware continues advancing. As we stand at the intersection of quantum computing and AI, hybrid quantum-classical systems like our N-QCNN framework demonstrate how we can harness quantum advantages today while building toward a fully quantum future. This research provides a blueprint for practical quantum-enhanced AI applications in computer vision, security, and beyond. Very Grateful to my professors Emre Tokgoz, Khald Aboalayon and my Clark University School of Professional Studies for supporting this research. Looking forward to presenting at CSCI 2025 and contributing to this exciting frontier in quantum machine learning! Interested in quantum machine learning or have questions about this research? Feel free to reach out I'd love to connect and discuss! #QuantumComputing #MachineLearning #AI #ComputerVision #QuantumAI #Research #Innovation #CSCI #ClarkUniversity #FacialRecognition

A new protocol has been developed that enables the scalable quantification and verification of nearly all quantum states using only single-qubit measurements. This approach addresses the limitations of conventional methods, which require complex circuits or an exponential number of measurements, making them impractical for large-scale quantum systems. The protocol demonstrates that local measurements can efficiently reveal global quantum properties, including high entanglement, challenging previous assumptions in the field. This advancement has significant implications for benchmarking quantum devices, verifying neural network models of quantum states, and developing efficient quantum learning algorithms. Further validation in laboratory settings is anticipated.

Quantum Feature Extraction with 156 Qubits Enhances Machine Learning Performance on Complex Data Researchers successfully utilise the principles of quantum mechanics to create enhanced data features, improving the performance of machine learning algorithms on complex tasks such as toxicity prediction and image recognition. #quantum #quantumcomputing #technology https://lnkd.in/eWaDCyXS

🤯 What if the equations governing flight, weather, and blood flow could just... break? It sounds like science fiction, but it’s one of the deepest, most foundational questions in mathematics. For decades, we've relied on equations like the Navier-Stokes and Euler equations to describe fluid motion. But we’ve never been able to prove if they can always predict the future. Is it possible for a perfectly smooth fluid to spontaneously develop infinite forces-a "singularity" or "blow-up"-in a finite time? Answering this for the Navier-Stokes equations is so important that the Clay Mathematics Institute has a $1 Million Millennium Prize waiting for a solution. A blow-up would signal a fundamental breakdown in the predictive power of these cornerstone equations of physics. A key piece of this puzzle is the idea of stability. Stable singularities are like a ball rolling to the bottom of a valley. They are robust and relatively easy to find with computer simulations. Unstable singularities, however, are the ghosts in the machine. They are like balancing a pencil perfectly on its tip. They require infinitely precise initial conditions; the slightest digital nudge in a simulation, and the solution veers off its blow-up course and disappears. The consensus is that if singularities do exist in the most challenging cases (like the boundary-free Euler and Navier-Stokes equations), they must be unstable. This has made them almost impossible to discover. Until now. A breathtaking new paper, "Discovery of Unstable Singularities," from a team including researchers at Google DeepMind, NYU, and Stanford has achieved a monumental breakthrough. They have developed a "new playbook" for mathematical discovery, combining deep mathematical insight with cutting-edge AI. Using highly customized Physics-Informed Neural Networks (PINNs) and a high-precision Gauss-Newton optimizer, they have systematically discovered new families of these elusive unstable singularities for the first time in several canonical fluid equations. The level of precision is astounding. For certain solutions, they achieved accuracy limited only by the physical round-off errors of the GPU hardware ( O(10^-13))! This is the kind of rigor needed to build computer-assisted mathematical proofs. This work is truly profound. We typically use computation to simulate physics we already understand. This team has built an AI-powered discovery engine to find entirely new mathematical solutions that have been hiding for centuries. The way they embed known mathematical properties (like symmetries and asymptotic behavior) as inductive biases directly into the neural network architecture is the secret sauce here, turning a hard optimization problem into a tractable one. This doesn't solve the Millennium Prize problem, but it provides a powerful new path forward. It’s a landmark achievement in the interplay between AI, mathematics, and physics. #CFD #AppliedMath #AIforScience #PINNs #GoogleDeepMind

"Over the last 12 months it has been working with Sydney-based quantum chip specialist Silicon Quantum Computing (SQC), putting its machine learning processor – called Watermelon – through its paces: #Watermelon takes it to another level, applying quantum mechanics to the tried and tested machine learning architecture known as reservoir computing – where data is fed into a neural network and the output is fed back into it again, serving as a memory and thereby improving its predictive capabilities."

Day 3 of EQIP with Ingenii: Diving Deep into Quantum Fundamentals ⚛️ Today’s sessions were an eye-opening journey through the building blocks of quantum computing. We explored the structure of quantum circuits, starting from the ground up with qubits, gates, and transformations, and then visualized them using Ingenii’s Quantum Hub. It was powerful to see how unitary matrices and rotation gates define qubit transformations, and how more complex operations like multi-qubit gates, control gates, and SWAP gates are designed. One of the most inspiring takeaways: understanding how universal gate sets and Clifford gates form the foundation of scalable quantum algorithms. We also took a step into quantum noise—something that's not just a nuisance, but a core challenge in real-world applications. Through Kraus operators, depolarizing and phase damping channels, and various error handling techniques like suppression, mitigation, and correction, we learned how resilience is being built into quantum systems. Quantum is complex—but it's exciting to see how the right abstractions and tools can help translate theory into meaningful applications. Curious how these quantum elements support real-world QML use cases? Explore Ingenii’s Open Source Python Library of QML Algorithms: 🔗 https://lnkd.in/dBaqYjqs #QuantumMachineLearning #AI #Innovation #QuantumComputing #ErrorMitigation