How OPTEX Quantum AI combines quantum computing and AI for optimization

Quantum computing and artificial intelligence are two transformative technologies that, when combined, create quantum AI. This integration leverages the unique properties of quantum mechanics, such as superposition and entanglement, to enhance AI's capabilities to solve complex problems, optimize processes, and analyze massive data sets. Quantum computing operates on qubits, which can represent multiple states simultaneously, unlike classical bits that are binary (0 or 1). This allows quantum computers to perform calculations at exponentially faster speeds, making them ideal for AI tasks such as machine learning, optimization, and data analysis. OPTEX Quantum AI is the version of OPTEX that produces algorithms that can be solved on quantum computers; It inherits all the AI features of OPTEX optimization expert system. We can affirm that OPTEX Quantum AI is the state of the art of optimization, the reasons are: - Generative Artificial Intelligence to produce the codes of the algorithms without programmers, and - Access to the world's fastest algorithms.

Quantum computing and artificial intelligence are two transformative technologies that, when combined, create quantum AI. This integration leverages the unique properties of quantum mechanics, such as superposition and entanglement, to enhance AI's capabilities to solve complex problems, optimize processes, and analyze massive data sets. Quantum computing operates on qubits, which can represent multiple states simultaneously, unlike classical bits that are binary (0 or 1). This allows quantum computers to perform calculations at exponentially faster speeds, making them ideal for AI tasks such as machine learning, optimization, and data analysis. OPTEX Quantum AI is the version of OPTEX that produces algorithms that can be solved on quantum computers; It inherits all the AI features of OPTEX optimization expert system. We can affirm that OPTEX Quantum AI is the state of the art of optimization, the reasons are: - Generative Artificial Intelligence to produce the codes of the algorithms without programmers, and - Access to the world's fastest algorithms.

The future of AI isn’t just faster chips—it’s QUANTUM. We’ve reached the ceiling with classical AI: Bigger GPUs. Larger datasets. Heavier models. But what about the problems that are simply too complex for any classical computer? That’s where Quantum Artificial Intelligence (QAI) comes in. Instead of brute force, QAI leverages superposition & entanglement to explore millions of possibilities at once. Real-world breakthroughs already happening: Pharma: Quantum AI can simulate molecules with atomic precision, accelerating drug discovery and cutting years off R&D. Finance: Banks are experimenting with quantum-enhanced risk models, portfolio optimization, and even ATM network design. And here’s the kicker: Major players (IBM, Google, IonQ) expect fault-tolerant quantum computing by 2030. That means the true quantum advantage is less than a decade away. 👉 The question isn’t “if Quantum AI will change industries.” The real question is: Are you and your organization building Quantum Readiness today? I just published a 5-minute explainer on Medium to help students and professionals get started: https://lnkd.in/gRZrDuJM 💭 What’s one problem in YOUR field you’d love to see Quantum AI tackle? #QuantumAI #ArtificialIntelligence #MachineLearning #QuantumComputing #DeepTech #Innovation #FutureOfWork #AI #TechTrends #STEM #Pharma #Finance #DrugDiscovery #Optimization #QuantumAdvantage #EmergingTech #DigitalTransformation #NextGenTech #Research #Leadership

Last year, I sat down with Brian Greene at World Science Festival to talk about AI and quantum computing. At the time, I said these technologies were moving faster than most of us expected. Today, that feels even more true and more urgent. A few themes stand out from that conversation: 🔹 Democracy is at risk. When AI-generated images and videos are indistinguishable from reality, misinformation can undermine and erode trust. We need authentication and strong safeguards before this becomes an existential problem. 🔹 Safety must come first. Current systems already show the capacity to enable cyber or biological attacks. Industry and government need to work together to ensure this technology stays in the right hands. 🔹 Education and healthcare can be transformed. AI can expand access to education and healthcare. Imagine an AI tutor or doctor in every pocket that is free, personalized, and available anywhere in the world, changing the trajectory of billions of lives. 🔹 Innovation will be democratized. We’re heading toward a world where anyone can say “build this for me” and AI will assemble the tools, code, or simulations. That changes not just what we build, but who gets to build. 🔹 Classical computing is nearing its limits. As we hit physical boundaries in chip design, advances in 3D packaging and breakthroughs in quantum computing will define the next era. 🔹 The opportunity is extraordinary. AI can double productivity, quantum can open new frontiers of discovery, and together they can expand human potential. But we can’t be passive observers. We need to shape these technologies responsibly or risk weakening the very systems that hold society together. #ArtificialIntelligence #QuantumComputing https://lnkd.in/eNGysij7

Why are quantum computers poised to solve some of AI's biggest problems? AI models require significant time, data, and energy to operate, and quantum computing offers a solution. Quantum computing's multi-state connections and entanglement capabilities facilitate speed enhancements that classical computing methods cannot match. Quantum computing may offer significant computational advantages over traditional methods. #quantumcomputing #AI #technology #innovation #futuretech

🔬 𝐀𝐈 × 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠: 𝐀 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐂𝐨𝐦𝐢𝐧𝐠 𝐃𝐞𝐜𝐚𝐝𝐞 The intersection of artificial intelligence and quantum computing isn’t just a buzzword. It’s rapidly becoming a sweet spot for breakthroughs that promise to reshape industries, research, and how we solve “impossible” outcomes. 🤔 Why This Matters 🔹 Tackling Hard Problems: Quantum computing offers ways to handle combinatorial/exponential complexity far beyond what classical machines can do. 🔹 AI Helping Quantum: Training better hardware, improving error correction, managing noisy qubits, optimizing resource allocation. 🔹 Hybrid and Generative Models: We’re seeing hybrid systems (classical + quantum) that can do things neither alone can efficiently manage. 🔹 Sustainability & Efficiency: Quantum approaches may one day reduce computational energy and resource demands for massive AI models. 📍Read this white paper for more details 👉🏼 https://lnkd.in/gADrwBcm What do you think about collaboration of these two state-of-the-art technologies would reshape innovation landscape in near future? Drop your comments👇 Follow Soumava Dey for more thought-provoking contents on AI and technology. #ai #quantumcomputing #research #innovation #technology

Machine Learning Detects Measurement-induced Entanglement in Arrays of Qubits, Enabling Characterisation of Long-range Quantum Effects Researchers successfully detect long-range entanglement created by measuring qubits, using artificial intelligence to model the complex post-measurement states and revealing a connection to a fundamental shift in how accurately classical systems can predict quantum behaviour #quantum #quantumcomputing #technology https://lnkd.in/eUdqxvbC

📌 Day-19 Quantum Computing Challenge (Qohort 3) – QuCode ✨🧠 “What if neurons weren’t silicon switches, but qubits weaving probability and entanglement into thought?” 🧠✨ ⸻ 🔑 Key Themes • Quantum Data Encoding → Classical features mapped into qubit states (angle, amplitude, or binary embedding). • Quantum Neural Networks (QNNs) → Layers of quantum rotations + entanglement, mirroring classical NN structure. • Training Loop → Quantum circuit processes → measure outputs → classical optimizer updates parameters. • Non-linearity Source → Emerges from measurement, not from activation functions. • Challenges → Barren plateaus (flat landscapes), noise and scalability issues in deeper circuits. ⸻ 🥡 Quick Takeaways • QNNs mimic classical neural networks in structure but harness quantum effects like superposition and entanglement. • Encoding strategies (angle, amplitude, binary) define how effectively data enters the quantum world. • Classical optimizers remain essential—keeping learning practical while quantum circuits add expressivity. • QML = a hybrid frontier where machine learning meets quantum physics, promising unique correlations and new possibilities for AI. ⸻ #QuantumMachineLearning #QNN #QuantumDataEncoding #HybridAI #QuantumAI #QuantumNeuralNetworks #QuantumOptimization #QuantumAlgorithms #Qubits #QuantumAdvantage #QuantumComputing #FutureOfAI #QuantumTech #MachineLearning #QuCode

#Day-76 Traditional computers run on bits (0 or 1), but qubits can exist as 0, 1, or both at the same time (thanks to quantum superposition). This allows quantum computers to process millions of possibilities simultaneously — making them far more powerful for complex AI tasks. Imagine AI models that don’t just analyze patterns but explore entire solution spaces instantly. From drug discovery to financial forecasting, quantum AI could unlock breakthroughs classical systems can’t achieve. McKinsey (2023) reports that quantum AI algorithms could run 100x–1000x faster than classical computing, with the global quantum AI market expected to hit $1.8B by 2030. #nexvistech #QuantumAI #QubitsExplained #FutureOfComputing #AIRevolution #QuantumTech #NextGenAI

Is classical computing hitting a wall with #AI? The immense energy and data required to train today's LLMs are unsustainable in many ways. Pretty soon we are reaching the physical limits of the current hardware. Like Sisyphus eternally pushing his boulder up the mountain, all major corporations build ever-larger data centers only to find that each new facility demands an even greater one to follow. So, what's next? Enter the world of Quantum Computing. By harnessing the principles of quantum mechanics, we're on the verge of a computational revolution that will redefine AI. This isn't just about doing things faster; it's about doing things that were previously thought impossible. NOW: The Hybrid Era We're already using Noisy Intermediate-Scale Quantum (NISQ) processors as powerful co-processors, tackling the hardest parts of LLM training and optimization tasks alongside classical machines. NEAR-TERM: Quantum-Inspired Dominance The next big breakthroughs will come from quantum-inspired algorithms. These are classical techniques, supercharged by quantum principles, that will dramatically compress model sizes and accelerate inference without needing a full-scale quantum computer. FUTURE: The Fault-Tolerant Revolution The ultimate goal: large-scale, fault-tolerant quantum computers. These machines will unlock entirely new AI architectures, leading to models that are exponentially more powerful and efficient than anything we have today. The convergence of Quantum + AI isn't a distant dream; it's a strategic roadmap being executed now. The future of intelligence is being built one qubit at a time. #QuantumComputing #ArtificialIntelligence #LLMs #FutureOfTech #Innovation #MachineLearning #BigData