MIT researchers develop new qubit design for stable quantum computing

👋 Hello LinkedIn community 👋 Google just redefined the pace of innovation. Their quantum research team revealed that the new “Quantum Echoes” algorithm ran 13,000× faster than the world’s most powerful supercomputer — on their 105‑qubit Willow chip. Let that sink in. For years, “quantum advantage” sounded like distant theory. But this is verifiable proof. What’s groundbreaking is that Quantum Echoes achieved this leap on AI‑oriented workloads, not abstract benchmarks. That means we’re witnessing the first intersection where quantum hardware genuinely accelerates AI computation. If this holds, the ripple effects are massive — from how we design model architectures to how we optimize data flows. The very foundations of AI infrastructure could shift overnight. For every AI strategist, engineer, and policymaker, we’ve entered a new layer of the frontier — where AI doesn’t just get smarter, it gets physics‑bending. Are we ready for an era where hardware and algorithms evolve symbiotically faster than ever before? To learn about this read here: https://lnkd.in/d332Key5 #QuantumComputing #AIRevolution #GoogleAI #TechInnovation #FutureOfAI

Discover how researchers at the University at Buffalo developed an improved Truncated Wigner Approximation method enabling complex quantum simulations on laptops, reducing the need for supercomputers and AI. #Quantum #QuantumAI #Computing

Chapter 8 Deep dive ANN In Quantum Circuits- III .8 Limitations and Future Directions Despite the excitement, key limitations include: Noisy circuits: Current hardware suffers from decoherence and gate errors. Data encoding bottlenecks: Efficiently preparing input quantum states remains resource-intensive. Training instability: Gradient vanishing in quantum circuits (barren plateaus) limits scalability. Future directions involve: Designing deeper QNN architectures using error-corrected qubits. Enhancing training algorithms with quantum-aware optimizers. Exploring quantum convolutional networks (QCNNs) and recurrent QNNs. --- 8.9 Personal Experimentation and Insight In my own research, I implemented a simple 2-layer QNN using Qiskit on IBM Quantum hardware for binary classification of synthetic quantum data. The model showed surprising generalization ability despite only using 4 qubits. I found that carefully designed entanglement structures and feature maps significantly boosted accuracy. Going forward, I plan to design a quantum-enhanced autoencoder for compressing high-dimensional quantum simulation data—this could drastically reduce memory and bandwidth usage in my quantum device prototypes. --- Conclusion Quantum circuits offer a radical rethinking of how neural networks are built and trained. Though still in its infancy, ANN on Quantum Circuits holds the promise to redefine learning paradigms, especially in domains where both quantum data and computational speed are critical. This chapter merely scratches the surface—true breakthroughs await as quantum hardware matures.

Google’s “Willow” Chip and the Quantum Shift: A New Era of Science Begins Google’s Quantum AI team has unveiled “Willow,” a 105-qubit processor, along with the Quantum Echoes algorithm, marking a historic milestone in quantum computing. 🔹 What happened? The Quantum Echoes algorithm solved certain computational tasks around 13,000× faster than classical supercomputers. This isn’t just a speed record — it represents the first observation of verifiable quantum behavior. 🔹 Why it matters With the power of quantum mechanics, Willow can model complex molecular structures and atomic arrangements beyond the reach of classical systems. This breakthrough could revolutionize drug design, battery technology, advanced materials, and energy optimization. 🔹 The technical leap • 105 entangled qubits • 99.9% fidelity • Molecular structure analysis in partnership with UC Berkeley • Verifiable quantum advantage 🔹 Why it matters for AI Dubliners At AI Dubliners, we’re closely following this convergence of artificial intelligence and quantum computing. Together, they promise not only faster computation — but a complete redefinition of scientific discovery itself. ⸻ 💭 How do you think the intersection of AI and quantum technology will shape Europe’s research and innovation ecosystem? IG: https://lnkd.in/eXZTX_Xr #AIDubliners #QuantumComputing #GoogleAI #WillowChip #QuantumEchoes #AI #Innovation #IrelandTech #FutureOfScience

What is Quantum Computing? Get ready to explore the fascinating world of Quantum Computing! Quantum computing isn't just a buzzword; it's a revolutionary approach to computation that harnesses the principles of quantum mechanics. Unlike classical computers that use bits (0s or 1s), quantum computers use 'qubits' which can be both 0 and 1 simultaneously through superposition. This allows them to process vast amounts of information and explore multiple possibilities at once, opening doors to solving problems currently intractable for even the most powerful supercomputers. 🚀While still in its early stages, quantum computing holds immense promise across various fields. Here are 5 simple examples of how it could be applied: 1. Drug Discovery: Simulating molecular interactions with unprecedented accuracy to design new medicines. 💊 2. Financial Modeling: Optimizing complex portfolios and predicting market trends with greater precision. 📈 3. Materials Science: Creating new materials with desired properties, like superconductors or more efficient batteries. 🔋 4. Cryptography: Breaking currently uncrackable encryption methods and creating new, secure ones. 🔒 5. Logistics Optimization: Finding the most efficient routes for delivery services, saving time and resources. 🚚These examples barely scratch the surface of quantum computing's potential. From accelerating AI and machine learning to revolutionizing data security and tackling climate change, the possibilities are truly mind-boggling. It's a field that promises to reshape industries and redefine what's possible in the digital age. ✨What industry do you think will be most impacted by quantum computing first? Share your thoughts below! 👇#QuantumComputing #TechInnovation #FutureTech #AI #DeepTech #Science

Pillars of Quantum AI Computing - Qubits, Superposition, Entanglement The revolutionary potential of QAI is rooted in three fundamental principles of quantum mechanics that govern how information is processed at the subatomic level: qubits, superposition, and entanglement.Qubits: The foundational unit of quantum information is the qubit. Unlike a classical bit, which can only exist in one of two definite states—either 0 or 1—a qubit can exist in a combination of both states simultaneously.4 Physically, qubits can be realized in various ways, such as through the spin states of an electron or the energy levels of an atom.2 This ability to hold more information than a classical bit is the first step toward the massive computational power of quantum systems.Superposition: The principle of superposition allows a qubit to be in a linear combination of the 0 and 1 states at the same time.4 When multiple qubits are combined, the number of possible states the system can represent grows exponentially. A system of $N$ qubits can exist in a superposition of all $2^N$ possible classical states simultaneously.11 This property enables what is known as "quantum parallelism," the ability of a quantum computer to perform many calculations at once on a single processor, exploring a vast solution space concurrently.8 A classical computer, by contrast, would need to perform these calculations sequentially or distribute them across a large number of parallel processors.9Entanglement: Perhaps the most counter-intuitive quantum phenomenon, entanglement describes a unique and powerful correlation between two or more qubits.8 When qubits are entangled, their fates are intrinsically linked; the state of one qubit directly influences the state of another, no matter how far apart they are physically separated.8 This "spooky action at a distance," as Einstein famously described it, allows for the creation of highly complex, coordinated computational states that are impossible to replicate in classical systems. Entanglement is a critical resource that enables quantum algorithms to solve certain problems exponentially faster than their classical counterparts by creating intricate computational webs that amplify parallel processing power.10

💡 People usually ask me Will Quantum Computing Replace Classical Ones? “Quantum computers aren’t here to replace classical ones, they’re here to collaborate in solving the impossible.” There’s a common misconception that quantum computing will make classical computers obsolete.That couldn’t be further from the truth. Classical systems excel at what they’ve always done best - deterministic, well-defined computation. They power everything from your browser to deep learning models. Quantum systems, however, shine in a completely different space - problems involving massive probabilities, complex optimization, or molecular simulations where classical computation hits its limits. Think of it like this 👇 🧠 Classical computers = logical, structured problem solvers. 🔮 Quantum computers = creative explorers that see patterns in probability space. The future isn’t about one replacing the other, it’s about both working together. In hybrid architectures, quantum processors will act as accelerators that offload specific, computation-heavy tasks while classical systems handle the orchestration. That’s how we’ll see breakthroughs in drug discovery, logistics optimization, and materials science, not by competition, but by collaboration between the classical and the quantum worlds. We’re not moving toward a replacement era, we’re entering an integration era. 💭 What’s one area you think will benefit most from hybrid quantum-classical systems? 🔖 #QuantumComputing #QuantumTechnology #AI #HybridComputing #QuantumAI #DeepTech #Innovation #FutureOfComputing #QuantumEngineering #MachineLearning #Future

🚀 Big news in the world of Quantum Computing! Today, Google Quantum AI has made history with a breakthrough published in Nature Magazine 🧠💡 Their new algorithm — Quantum Echoes — running on the Willow quantum processor (105 qubits), has achieved what scientists call a verifiable quantum advantage. In simple terms, it means the computation is not only 13,000x faster ⚡ than the world’s best supercomputers but also verifiable — meaning other quantum computers or physical experiments can reproduce it. 🌍 Why this matters: This is the first time ever a quantum computer has outperformed supercomputers with verifiable accuracy. It opens pathways for real-world applications in drug discovery, materials science, and molecular chemistry. Using nuclear magnetic resonance and quantum “echo” techniques, the algorithm can now compute molecular structures with precision not possible before. According to Google, this milestone may bring practical quantum computing applications within the next 5 years — a timeline shorter than many expected. 📊 A quick perspective: The Quantum Echoes algorithm models how information spreads in quantum systems — effectively acting like a “quantum sonar” reflecting real-world physics rather than abstract problems. 🌌 From understanding atomic interactions to simulating molecular structures — this moment marks when quantum computing truly steps out of theory and into applied science. 🔗 Read the full paper here via Nature Magazine: https://lnkd.in/eQ8PsFsh 💬 As technology, finance, and research industries prepare for this next leap — one question stands out: Courtesy - Sundar Pichai Google What new industries and innovations will quantum computing unlock by 2030? Let’s discuss 👇 Follow ISHAN DILIP PAHADE #QuantumComputing #GoogleAI #Innovation #TechnologyLeadership #FutureOfComputing #AI #QuantumPhysics #DigitalTransformation

Google’s Willow quantum chip achieved “verifiable quantum advantage” - running an algorithm 13,000× faster than the world’s fastest supercomputer. “Cool, but I’m still trying to organize my Playwright tests.” Here’s why you should care: 1. The Problems We All Face Your test suite has 10,000 tests. You have 50 parallel runners. Some are flaky. Some depend on others. Today, you throw more runners at it. You write smarter scripts. You use AI to analyze logs. But you’re solving these with the same computational approach we’ve used for decades. 2. What Just Changed Google proved quantum computers can solve certain problems fundamentally differently - and VERIFY the results. For test automation, this means: a) Combinatorial optimization - 20 fields × 5 values = 95 trillion combinations. Today: pick a “good enough” subset. Tomorrow: find the OPTIMAL subset that catches the most bugs. b) Flaky test analysis - Test fails 30% of the time. Could be timing, memory, network, browser, CPU, or how these interact. Finding the root cause means analyzing hundreds of variable combinations. Classical computers do this sequentially. Quantum does it simultaneously. c) Test scheduling - 10,000 tests, complex dependencies, limited resources. Today’s schedulers are “smart.” Quantum could be OPTIMAL. 3. “But That’s Years Away” Google says real-world applications: within 5 years. Not 20. Not 10. Five. The molecular simulations they demonstrated? Same complex correlation problems we face in test automation. 4. What This Means I’m not saying stop learning Kubernetes(I’m in fact preparing myself for learning K8) nor pursuing all the current certifications for knowledge and job opportunities . I’m saying: while we’re perfecting yesterday’s tools, tomorrow’s tools are arriving faster than we think. The question isn’t “should I learn quantum computing?” It’s: “Am I building test automation that can adapt when quantum becomes accessible?” Here’s what I’m doing: • Studying which problems map to quantum algorithms • Designing “quantum-agnostic” systems - work classically today, ready for quantum tomorrow • Exploring how AI test analysis could be quantum-augmented (Working on something interesting in screenshot analysis + BDD generation 😉) 5. Summary We spend so much time in the current weeds that we forget to look up. The best engineers aren’t the ones who know every kubectl flag. They’re the ones who see the shift coming and position themselves ahead of it. Google just proved quantum computing works for real problems. Verifiably. Repeatedly. The game just changed. 🔗 https://lnkd.in/ghkbmTij #QuantumComputing #TestAutomation #QA #Innovation #FutureOfTech #kubernetes