Quantum Enhanced Artificial Intelligence

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

🚀 Exploring the Frontier of AI with Quantum Machine Learning (QML) 🚀 Let me share this comprehensive 260-page tutorial designed to bridge the gap between classical #machine #learning and #quantum computing. This resource dives deep into Quantum Machine Learning (QML), an evolving field with the potential to reshape the future of AI. 🔍 What is covered inside : • Foundational principles of QML • Representative algorithms and their potential applications • Critical insights on trainability, generalization, and computational complexity • A special focus on Quantum Transformers, the backbone of future quantum #LLMs • Practical code demos for hands-on learning: qml-tutorial.github.io Whether you’re an AI enthusiast, researcher, or quantum computing explorer, this tutorial is a new possible gateway to understanding how quantum technologies can elevate machine learning.✨ #QuantumMachineLearning #AI #QuantumComputing #QML #QuantumLLM #TechInnovation #ML

One of the first papers in the World to outline quantum and agentic AI? This paper explores the intersection of quantum computing and agentic AI by examining how quantum technologies can enhance the capabilities of autonomous agents, and, conversely, how agentic AI can support the advancement of quantum systems. We analyze both directions of this synergy and present conceptual and technical foundations for future quantum-agentic platforms. Our work introduces a formal definition of quantum agents and outlines potential architectures that integrate quantum computing with agent-based systems. As a proof-of-concept, we develop and evaluate three quantum agent prototypes that demonstrate the feasibility of our proposed framework. Furthermore, we discuss use cases from both perspectives, including quantum-enhanced decision-making, quantum planning and optimization, and AI-driven orchestration of quantum workflows. By bridging these fields, we aim to chart a path toward scalable, intelligent, and adaptive quantum-agentic ecosystems. Eldar Gunter Sultanow, Dr. Mark Tehrani, Siddhant Dutta, Muhammad Shahbaz Khan https://lnkd.in/eDDmTWtQ

We just released the world's first paper on Quantum Agentic AI, focusing on the new, LLM-driven notion of agentic AI that currently transforms how we build autonomous agents! Our team including Prof Bill Buchanan OBE FRSE, Dr. Mark Tehrani, Muhammad Shahbaz Khan and Siddhant Dutta dove deep into the intersection of quantum computing and the new, LLM-driven notion of agentic AI—the concept of autonomous agents that leverage large language models to plan, decide, and act intelligently. Key highlights of our work are: ● The first formal definition of Quantum Agents based on LLM-inspired agentic principles ● New architectures that tightly integrate quantum processors with agent-based reasoning ● Three working prototypes: from Grover-based decision-making to adaptive quantum encryption ● Use cases spanning quantum-enhanced edge AI, chemistry, defense, and hybrid optimization What is the amazing and relevant big thing? Agentic AI is redefining autonomy and decision-making. By bringing quantum computing into the loop, we unlock entirely new horizons—systems that combine the best of classical and quantum intelligence. We’d love to hear your thoughts: Where do you see the biggest impact of Quantum Agents? What real-world problems could they solve today? Wir haben das weltweit erste Paper zu Quantum Agentic AI veröffentlicht, und zwar mit einem Fokus auf den neuen, von LLMs geprägten Agentic-Begriff, der aktuell die Entwicklung von autonomen Agenten neu definiert! Unser Team hat sich an der Schnittstelle von Quantencomputing und dem neuen, von LLMs geprägten Agentic-Begriff positioniert, also Agenten, die mit großen Sprachmodellen eigenständig planen, entscheiden und handeln. Highlights aus unserer Arbeit: ● Die erste formale Definition von Quantum Agents, basierend auf LLM-inspirierten agentischen Prinzipien ● Neue Architekturen, die Quantenprozessoren nahtlos mit Agenten-Logik verknüpfen ● Drei funktionierende Prototypen: vom Grover-basierten Entscheidungsagenten bis zur adaptiven Quantum Image Encryption ● Anwendungsfelder von Quanten-Edge-AI über Chemie bis Verteidigung und hybride Optimierung Was ist das Geniale daran? KI-Agenten verändern derzeit, wie wir über Autonomie und Entscheidungsfindung denken. Mit Quantum Agents kombinieren wir diese neue Form von Intelligenz mit den Fähigkeiten des Quantencomputings – für Anwendungen, die bisher undenkbar waren. Deine Meinung interessiert uns: Wo siehst du die größten Potenziale für Quantum Agents? Welche Probleme könnten Quantum Agents schon heute lösen? #QuantumAI #AgenticAI #QuantumComputing #LLM #AIResearch #Innovation #FutureOfWork #QuantumAgents

A new theoretical study from Google Quantum AI shows that quantum computers could learn certain neural networks exponentially faster than classical algorithms when data follows natural patterns like Gaussian distributions. The researchers developed a quantum algorithm that outperforms classical gradient-based methods in learning “periodic neurons,” a function type common in machine learning. https://lnkd.in/eCpkmdkX

The convergence of AI and quantum computing is no longer a futuristic concept—it is underway, with the potential to transform entire industries. 2025 is being called the International Year of Quantum Science and Technology, with major breakthroughs already demonstrated. Startups in Europe are leading the way, developing quantum-inspired AI solutions that deliver greater efficiency and performance at a lower cost. Tensor networks, for example, are helping large language models run faster and leaner, without sacrificing accuracy. We are also seeing the emergence of purpose-built research centers where AI and quantum teams are working side by side. Their mission? To unlock new possibilities by integrating quantum processors with AI-based supercomputing—reducing latency, enhancing processing efficiency, and pushing boundaries once thought to be out of reach. What could this mean for the future of technology and business? A lot—and it is just beginning. https://lnkd.in/eaaMx59Y