IEEE Quantum Week 2025: Quantum + AI Team's Schedule

Quantum Machine Learning (QML) — The Next Frontier of Intelligence Machine Learning changed how we predict. Quantum Computing will change how we think. We’re entering a new era where data science meets quantum physics — and the result is mind-bendingly powerful. Here’s what makes Quantum Machine Learning (QML) so exciting 👇 💡 1. From Bits to Qubits Traditional ML models process bits — 0s and 1s. Quantum models use qubits, which can exist in multiple states simultaneously thanks to superposition. That means: one quantum operation = exploring many possibilities in parallel. 🚀 ⚡ 2. Massive Speedups (in Theory) Imagine running gradient descent on an optimization landscape where every parameter update happens in quantum parallel. For complex problems — protein folding, portfolio optimization, fraud detection — QML could cut computation time from days to minutes. 🧩 3. Quantum Feature Spaces QML introduces a new way to represent data using quantum kernels, mapping classical data into high-dimensional quantum spaces — potentially revealing patterns invisible to classical models. 🔐 4. The Hybrid Reality Right now, it’s not “Quantum vs Classical.” It’s Quantum + Classical. Hybrid systems use quantum circuits for feature extraction and classical neural nets for prediction — the best of both worlds. 🧭 5. Still Early, But Evolving Fast Frameworks like PennyLane, Qiskit, TensorFlow Quantum are already making QML accessible. Researchers are tackling challenges like noise, decoherence, and barren plateaus — the key blockers to scaling real QML models. 💬 Why this matters for Data & AI Engineers: Tomorrow’s ML stack won’t just run on GPUs — it’ll run on quantum processors. The engineers who understand this convergence will define the future of intelligent systems. So if you’re building data pipelines today, start thinking: “How can I prepare my models for the quantum world?” #QuantumComputing #MachineLearning #QML #ArtificialIntelligence #AI #DataEngineering #DeepLearning #Innovation #FutureTech #QuantumAI #MLOps

TL;DR: The lecture introduces a four-session quantum machine learning (QML) workshop at QERSA 2025, led by Dr. Farad from LUMS. It targets beginners, covering QML basics, focusing on quantum support vector machines and variational circuits. Sessions split 80% theory (e.g., deterministic and probabilistic QML models, data encoding, feature maps) and 20% coding, with code shared on GitHub. Day 1 introduces QML, deterministic/probabilistic models, and a toy variational circuit example. Day 2 covers data encoding, Day 3 focuses on variational quantum classifiers, and Day 4 explores kernel methods and quantum SVMs. QML models are universal, approximating functions via circuit repetitions, akin to classical neural networks. Questions are addressed every 5-10 minutes via chat. https://lnkd.in/gbqUb_b2

The best AI models in the world just took a quantum computing test. They failed spectacularly. Even GPT-4 couldn't crack 40% accuracy on basic quantum code. This isn't just another AI limitation story. Researchers at Kyushu University created QuanBench. A benchmark testing LLMs on 44 quantum programming tasks. The results? Shocking. 🔴 Best models scored under 40% accuracy 🔴 Common failures: outdated APIs, circuit errors, broken logic 🔴 Even advanced models like DeepSeek R1 struggled Quantum computing isn't classical programming. It requires understanding: • Superposition and entanglement • Hardware constraints • Specialized quantum algorithms • Complex mathematical concepts This gap reveals something important. General-purpose AI models hit walls in specialized domains. The quantum computing field is growing fast. But our AI tools aren't keeping pace. The silver lining? QuanBench is now public. Researchers can use it to improve quantum code generation. This isn't just about quantum computing. It's about AI's limits in complex, specialized fields. What other domains might challenge our best AI models? #QuantumComputing #ArtificialIntelligence #TechResearch 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/gu3-K4EW

🚀 Exploring the Future of Quantum Intelligence! An insightful session led by Resource Person Dr. Karthiganesh Durai on Quantum Computing and Machine Learning (QML) 🌌 ✨ Key Highlights: 🔹 Introduced the fundamentals and core principles of Quantum Computing. 🔹 Explained Quantum Machine Learning (QML) and its key differences from Classical ML. 🔹 Provided hands-on experience with basic quantum algorithms for ML applications. 🔹 Explored quantum data encoding techniques and circuit design for ML tasks. 🔹 Inspired research curiosity and career opportunities in the fields of Quantum Computing & AI. 🔹 Encouraged critical thinking on quantum advantages in data processing and optimization. 💡 A truly transformative learning experience that bridges theory, innovation, and future technology — empowering us to think beyond classical boundaries! ⚛️✨ #QuantumComputing #MachineLearning #QuantumMachineLearning #AI #FutureTech #Innovation #QuantumAlgorithms #DataScience #Research #CareerGrowth #QuantumAdvantage #LearningExperience

The best AI models in the world just took a quantum computing test. They failed spectacularly. Even GPT-4 couldn't crack 40% accuracy on basic quantum code. This isn't just another AI limitation story. Researchers at Kyushu University created QuanBench. A benchmark testing LLMs on 44 quantum programming tasks. The results? Shocking. 🔴 Best models scored under 40% accuracy 🔴 Common failures: outdated APIs, circuit errors, broken logic 🔴 Even advanced models like DeepSeek R1 struggled Quantum computing isn't classical programming. It requires understanding: • Superposition and entanglement • Hardware constraints • Specialized quantum algorithms • Complex mathematical concepts This gap reveals something important. General-purpose AI models hit walls in specialized domains. The quantum computing field is growing fast. But our AI tools aren't keeping pace. The silver lining? QuanBench is now public. Researchers can use it to improve quantum code generation. This isn't just about quantum computing. It's about AI's limits in complex, specialized fields. What other domains might challenge our best AI models? #QuantumComputing #ArtificialIntelligence #TechResearch 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/gfgBBJFy

The best AI models in the world just took a quantum computing test. They failed spectacularly. Even GPT-4 couldn't crack 40% accuracy on basic quantum code. This isn't just another AI limitation story. Researchers at Kyushu University created QuanBench. A benchmark testing LLMs on 44 quantum programming tasks. The results? Shocking. 🔴 Best models scored under 40% accuracy 🔴 Common failures: outdated APIs, circuit errors, broken logic 🔴 Even advanced models like DeepSeek R1 struggled Quantum computing isn't classical programming. It requires understanding: • Superposition and entanglement • Hardware constraints • Specialized quantum algorithms • Complex mathematical concepts This gap reveals something important. General-purpose AI models hit walls in specialized domains. The quantum computing field is growing fast. But our AI tools aren't keeping pace. The silver lining? QuanBench is now public. Researchers can use it to improve quantum code generation. This isn't just about quantum computing. It's about AI's limits in complex, specialized fields. What other domains might challenge our best AI models? #QuantumComputing #ArtificialIntelligence #TechResearch 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/dP_4_GDe

🤔Not all skepticism is created equal In a recent convo with a colleague, it hit me how different the skepticism around AI sounds compared to quantum. When people express doubt about AI, the critiques are specific. 🧩 “Training data is biased.” 💰 “Inference costs will crush margins.” ⚖️ “Regulatory and IP risk will slow enterprise adoption.” Even the skeptics know the mechanics. They’re pointing to real levers in the system — cost, compliance, compute, control. But in quantum? The skepticism is usually broad and blurry: 🌀 “We’re years away from Q-Day.” 💸 “Too expensive to ever scale.” ❌ “We’ll never reach enough qubits or fault tolerance.” Notice what’s missing — no mention of the 18+ quantum computing architectures already being explored, or the 30+ SDKs, languages, and frameworks used today. No recognition that quantum annealing, sensing, and comms are in market right now, powering logistics, materials science, and defense applications. Are all 18 architectures “failures”? Are all 30+ developer toolkits “doomed”? Or have we just gotten rusty at qualitatively and quantitatively assessing what’s already here? When you fixate on what’s not there, you stop seeing what is. And in frontier tech — that’s how opportunity passes you by. #QuantumTech #AI #Innovation #FrontierTech #FutureFluency #BarcloVentures #QuantumComputing

[New Paper Out] We applied adversarial attack methods from AI to synchronization control in oscillator networks. Our study shows that small phase perturbations can effectively control synchronization in oscillator networks. This approach may be broadly useful for synchronization control, including power grid stabilization and suppression of abnormal brain synchronization. https://lnkd.in/ghd47Jef

DeepSeek, Claude, GPT-4 - all the coding superstars lined up for the quantum challenge. None could reliably write working quantum circuits. Something fundamental is missing. The QuanBench study from Kyushu University just dropped some sobering results. They tested top LLMs on 44 quantum programming tasks using Qiskit. The verdict? Less than 40% accuracy across the board. Even the best models couldn't crack the 50% mark. We're talking about basic quantum operations here: • Grover's search algorithm • Quantum Fourier transforms • Simple state preparation • Circuit construction The failures weren't random either. Common patterns emerged: 🔴 Outdated API usage 🔴 Circuit construction errors 🔴 Flawed algorithm logic This isn't just about model size or training data. Quantum computing operates on fundamentally different principles. Superposition. Entanglement. Hardware constraints. These concepts don't translate from classical programming patterns that LLMs learned on. The research team made QuanBench publicly available. Smart move. We need benchmarks that expose these gaps before we can fix them. Quantum computing is accelerating fast. But our AI tools aren't keeping pace with the complexity. What does this mean for developers entering quantum computing? Manual coding and deep domain knowledge still rule. Are we expecting too much from general-purpose AI? Or do we need quantum-specialized models? #QuantumComputing #AI #TechResearch 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/gHVtsgNx

The $4.2 Billion Leap: Are you building the bridge between AI and Quantum? 🤯 The Global AI in Quantum Computing market is expected to skyrocket to $4.2 billion by 2033. This is where computational power transcends classical limits, promising to revolutionize cryptography, optimization, and complex system modeling. The AI+ Quantum Certification is the advanced roadmap for Data Scientists, Engineers, and Researchers looking to lead this convergence. You will master the skills driving the next phase of innovation: ▪️Quantum Machine Learning (QML) & QDL: Gaining expertise in the advanced methodologies that accelerate AI processes beyond classical boundaries. ▪️Quantum Algorithms: Understanding the fundamental building blocks (Gates, Circuits) and the algorithms tailored for AI applications. ▪️AI for Quantum: Leveraging AI to solve quantum's biggest hurdles, including Error Correction and Hardware Optimization. ▪️Ethics & Application: Addressing critical Ethical Considerations and applying knowledge through a hands-on workshop and case studies. This program gives you the theoretical knowledge and practical skills necessary to drive innovation in this cutting-edge domain. 👉 Explore the AI+ Quantum Certification today: https://lnkd.in/dBx_gkXU #QuantumComputing #AI #QML #QuantumAlgorithms #AICERTs #AICertifications