Quantum computers surpass classical in generative learning and sampling

🚀 Generative Quantum Advantage: Unlocking Efficient Learning and Sampling Beyond Classical Limits Recent groundbreaking research demonstrates the first full generative quantum advantage, where quantum models can both learn and sample distributions efficiently that are provably intractable for classical computers. This advance blends theoretical proofs with experimental validation on a 68-qubit superconducting quantum processor, showcasing powerful quantum generative models that push beyond classical computational barriers. 🔸 Key breakthroughs include: • Efficient classical training paired with inherently quantum inference, meaning the model can be trained on classical hardware but requires quantum devices for sampling the learned distribution • Introduction of the sewing technique, which breaks complex quantum circuits into overlapping local subproblems, eliminating common training challenges like barren plateaus and local minima, thus simplifying optimization landscapes • Construction and experimental demonstration of quantum models that represent up to an effective 816 shallow qubits physically sampled by deep circuits, and theoretical scaling up to 34,304 shallow qubits, with potential for further resource optimization • Explicit demonstration that these quantum circuits can produce and sample from probability distributions inaccessible to classical methods, establishing a provable generative advantage This work not only solves longstanding quantum machine learning challenges but also paves the way for applications in quantum circuit compression and quantum-enhanced generative tasks, promising transformative impacts in computational sciences and AI. #QuantumComputing #QuantumMachineLearning #GenerativeModels #QuantumAdvantage #ShallowQuantumCircuits #QuantumAI #thequantumcircle See original article here -> https://lnkd.in/gGQR53qY

𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗔𝗻𝗱𝗵𝗿𝗮 𝗦𝗲𝗿𝗶𝗲𝘀 | 𝗙𝗿𝗼𝗺 𝗕𝗶𝘁𝘀 𝘁𝗼 𝗤𝘂𝗯𝗶𝘁𝘀 Have you ever imagined how a classical computer transforms into a quantum powerhouse? The diagram below beautifully captures this quantum journey — from input to superimposed states, and finally, to a quantum output that reshapes how we perceive computation. 🧠 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁’𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗶𝘀 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗳𝗹𝗼𝘄: It begins with 𝗰𝗹𝗮𝘀𝘀𝗶𝗰𝗮𝗹 𝗶𝗻𝗽𝘂𝘁𝘀, processed through a 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗴𝗿𝗮𝗺. These evolve into 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗶𝗻𝗽𝘂𝘁𝘀 that enter the realm of 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝘀𝘁𝗮𝘁𝗲𝘀, where data exists in 𝘀𝘂𝗽𝗲𝗿𝗶𝗺𝗽𝗼𝘀𝗲𝗱 𝗳𝗼𝗿𝗺𝘀. Inside the 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗶𝗿𝗰𝘂𝗶𝘁, three key components take charge — 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗣𝗨, 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗺𝗲𝗺𝗼𝗿𝘆, and 𝗲𝗿𝗿𝗼𝗿 𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗶𝗼𝗻 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀. These ensure that fragile quantum states remain stable until they are measured — giving us the 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗼𝘂𝘁𝗽𝘂𝘁 that’s converted back into 𝗰𝗹𝗮𝘀𝘀𝗶𝗰𝗮𝗹 𝗿𝗲𝘀𝘂𝗹𝘁𝘀. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: This isn’t just faster computation — it’s a completely new way of thinking. Quantum computing combines physics, mathematics, and computer science to solve problems that classical machines could never touch — from drug discovery and AI optimization to cybersecurity and climate modeling. 🔬 Each layer — from quantum states to error correction — reveals how coherence, entanglement, and superposition come together to redefine logic itself. 𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗾𝘂𝗮𝗻𝘁𝘂𝗺, and it’s unfolding faster than ever before. 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝘁𝗼 𝗺𝘆 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗰𝗵𝗮𝗻𝗻𝗲𝗹 for the latest 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗮𝗿𝗲𝗲𝗿 𝘂𝗽𝗱𝗮𝘁𝗲𝘀, expert sessions, and research insights from around the world. Youtube Channel link: https://lnkd.in/g8fu8CEk Let’s decode the future of computation — together. #QuantumAndhraSeries #QuantumComputing #QuantumCircuits #AI #STEM #Innovation #QuantumMechanics #FutureOfTech #Research #Superposition #QuantumRevolution

Is artificial intelligence getting a quantum upgrade? Or is quantum all hype? Find out in this explainer page that delves into quantum computing and how it can work alongside AI to solve increasingly complex problems. http://2.sas.com/6049AUiEW

Exploring Quantum Computing Applications in Maritime Logistics Working on an interesting theoretical framework that applies quantum optimization to port management challenges. The core concept: Transform the berth allocation problem into a time-dependent Hamiltonian that quantum annealers can solve more efficiently than classical algorithms. Key insights from our model: Traditional port optimization faces O(2ⁿ) complexity Quantum approach reduces this to polynomial time for specific structures Simulations suggest 15-20% improvement in berth utilization The math combines: QUBO formulations for discrete optimization Neural networks for congestion prediction Pareto optimization for multi-objective trade-offs Real-world implementation challenges remain significant - current quantum hardware is limited and noisy. But as quantum computing matures, applications like this could transform how we manage complex logistics networks. The potential: reducing global shipping delays by even 10% could save billions annually and significantly reduce emissions. What other industries could benefit from quantum optimization approaches? #QuantumComputing #MaritimeLogistics #SupplyChain #OperationsResearch #Innovation

A major breakthrough in AI-accelerated quantum simulations ⚡🔬 . Researchers have unveiled a revolutionary reservoir graph neural network integrated with resistive memory hardware, slashing computational costs by up to 1,000,000× while maintaining high accuracy. . This innovation paves the way for real-time modeling of ionic and electronic interactions at unprecedented scales — transforming quantum chemistry, materials discovery, and large-scale simulations. 👉 Read the full article on Quantum Server Networks: https://lnkd.in/eJYG7kMx #QuantumComputing #MaterialsScience #MachineLearning #GraphNeuralNetworks #ReservoirComputing #DFT #QuantumChemistry #ScientificAI #InMemoryComputing #EnergyEfficientComputing #QuantumServerNetworks #ComputationalMaterials #AIInnovation #NatureComputationalScience #HPC

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

Quantum x AI: Where the Next Computing Revolution Begins The conjunction of Quantum Computing and Artificial Intelligence is not just a distant visionary it is happening now. Google's just recently launched Willow Quantum Chip has executed error-correction exponentially and carried out calculations that were once infeasible for example, calculations that would normally take a supercomputer a period as long as 10 septillion years are now feasible. At the same time, NVIDIA's AI-led quantum research is showing how machine learning will learn to train quantum processors to interpret qubit noise accelerating quantum breakthroughs in areas like molecular modelling and materials science. The synergy of the developments paints a picture of an AI-Quantum future where AI is optimizing quantum behavior, and quantum computing takes us to a level of reasoning that outstrips classical computing limitations. At Uranus Tech Labs, we see this convergence as the birth of a new intelligence infrastructure where computation, physics, and cognition converge to provide us with a new definition of "possible". How soon before Quantum-AI systems become the backbone of real-world innovation? #AI #QuantumComputing #Innovation #TechTrends #UranusTechLabs #NVIDIA #Google #FutureOfCompute #DeepTech

Quantum Breakthrough Alert! A new milestone has been achieved in the path toward practical quantum computers. Published in Nature (24 Sept 2025), the paper titled “Low-overhead transversal fault tolerance for universal quantum computation” by Hengyun Zhou, Chen Zhao, and Mikhail D. Lukin’s team at Harvard introduces a revolutionary concept — Transversal Algorithmic Fault Tolerance (AFT). ✓ What does it mean in simple terms? Quantum computers are extremely powerful but fragile — even tiny errors can ruin computations. To fix this, scientists use quantum error correction, which typically needs many repeated rounds of checking (called syndrome measurements). This new research shows that it’s possible to perform logical operations fault-tolerantly with only a constant number of error-checking rounds, instead of many. The result: • Faster and more efficient quantum algorithms • Much lower hardware overhead • More practical fault-tolerant quantum computation ✓ Why it matters: This approach could reduce the space–time cost of quantum computation by more than 10×, accelerating the timeline for achieving useful, scalable quantum advantage. It combines transversal quantum gates with correlated decoding strategies, keeping reliability exponentially high while cutting down resource needs. ✓ This work opens a new chapter in how we think about reliable quantum logic — making real-world quantum processors far more feasible. ✓ Read the full article in Nature here: https://lnkd.in/ggWzKCd8 #QuantumComputing #AI #Research #Innovation #NovumLabs #QuantumErrorCorrection #Nature #MikhailLukin #QuantumTechnology

Still thinking about AI as GenAI..... Is artificial intelligence getting a quantum upgrade? Or is quantum all hype? Find out in this explainer page that delves into quantum computing and how it can work alongside AI to solve increasingly complex problems. http://2.sas.com/6046Afazj

Is artificial intelligence getting a quantum upgrade? Or is quantum all hype? Find out in this explainer page that delves into quantum computing and how it can work alongside AI to solve increasingly complex problems. http://2.sas.com/6048AbmAG