AI and Quantum Computing Synergies

Is Quantum Machine Learning (QML) Closer Than We Think? Select areas within quantum computing are beginning to shift from long-term aspiration to practical impact. One of the most promising developments is Quantum Machine Learning, where early pilots are uncovering advantages that classical systems are unable to match. 🔷 The Quantum Advantage: Quantum computers operate on qubits, which can represent multiple states simultaneously. This enables them to process complex, interdependent variables at a scale and speed that classical machines cannot. While current hardware still faces limitations, consistent progress in simulation and optimization is confirming the technology’s potential. 🔷 Why QML Matters: QML combines quantum circuits with classical models to unlock performance improvements in targeted, data-intensive domains. Early-stage experimentation is already showing promise: • Accelerated training for complex models • More effective handling of high-dimensional and sparse datasets • Greater accuracy with smaller sample sizes 🔷 The Timeline Is Shortening: Quantum systems are inherently probabilistic, aligning well with generative AI and modeling under uncertainty. Just as classical computing advanced despite hardware imperfections, current-generation quantum systems are producing measurable results in narrow but high-value use cases. As these outcomes become more consistent, enterprise adoption will follow. 🔷 What Enterprises Can Do Today: Quantum hardware does not need to be perfect for companies to begin exploring value. Practical entry points include: • Simulating rare or complex risk scenarios in finance and operations • Using quantum inspired sampling for better forecasting and sensitivity analysis • Generating synthetic datasets in regulated or data scarce environments • Targeting challenges where classical AI struggles, such as subtle anomalies or low signal environments • Exploring use cases in fraud detection, claims forecasting, patient risk stratification, drug efficacy modeling, and portfolio optimization 🔷 Final Thought: Quantum Machine Learning is no longer confined to research. It is becoming a tool with real strategic potential. Organizations that begin investing in awareness, experimentation, and talent today will be better positioned to lead as the ecosystem matures. #QuantumMachineLearning #QuantumComputing #AI

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

Meters Closer, Miles Faster: Cryogenic In-Memory Computing Brings AI to the Edge of Quantum HKUST researchers develop low-temperature AI interface that bridges the gap between artificial intelligence and quantum processors Introduction In a breakthrough at the intersection of two of the most transformative technologies of our time—artificial intelligence (AI) and quantum computing—researchers at the Hong Kong University of Science and Technology (HKUST) have introduced a novel cryogenic in-memory computing scheme. By enabling AI computations to occur at the same ultra-low temperatures as quantum processors, this innovation could vastly accelerate hybrid AI-quantum systems and make them far more energy-efficient. The Breakthrough Explained • Cryogenic In-Memory Computing: • Traditional AI chips and quantum processors operate in drastically different environments—AI at room temperature, quantum at near absolute zero. • The HKUST team, led by Prof. Shao Qiming, designed a computing architecture that operates efficiently at cryogenic temperatures, allowing AI hardware to be physically co-located with quantum hardware. • This approach minimizes data transfer delays and mitigates the need for thermal management systems that typically separate AI and quantum components. • Magnetic Topological Insulator Hall-Bar Devices: • The innovation hinges on a special material structure—magnetic topological insulators configured in Hall-bar devices—that allows data to be stored and processed with minimal heat generation. • These materials support robust, low-power in-memory computing operations that are compatible with quantum environments. • This significantly reduces system complexity while maintaining high data throughput. • Integration with Quantum Computing: Why This Matters The convergence of AI and quantum computing has long been seen as a frontier for revolutionary breakthroughs—from faster drug discovery to uncrackable encryption and ultra-efficient logistics. However, a major roadblock has been the physical and thermal disconnect between the two systems. HKUST’s cryogenic computing scheme brings AI physically “meters closer” and operationally “miles faster” to quantum cores. This innovation does more than solve a hardware bottleneck—it lays the foundation for a new class of intelligent quantum systems. These systems could autonomously optimize their own algorithms, interpret noisy quantum outputs in real-time, or rapidly retrain AI models based on quantum-derived insights. As the race to quantum advantage continues, bridging the thermal and architectural gap between AI and quantum computing could be the key to unlocking their full potential—not just as standalone technologies, but as an integrated platform for the next era of computation.

🌍💡 The Future is Converging: Unlocking Value Through #Technology Synergies 🚀 The World Economic Forum’s Technology Convergence Report (June 2025), in collaboration with Capgemini, is a game-changer for understanding how today’s tech landscape is evolving. It’s not just about individual breakthroughs anymore—think #AI , quantum computing, or robotics—it’s about how these technologies combine to reshape industries, create new markets, and drive exponential impact. Let’s dive into the key insights and why this matters for leaders, innovators, and organizations worldwide! 🌐 🔑 The 3C Framework: A Roadmap for Innovation The report introduces the 3C Framework—Combination, Convergence, and Compounding—as a lens to navigate the complex interplay of technologies. Here’s how it works: Combination: Technologies like AI and quantum computing merge at the sub-component level (e.g., machine learning + quantum algorithms) to create novel solutions that tackle problems no single tech could solve. For example, quantum ML blends atomistic and molecular insights to revolutionize material design. Convergence: These combinations reshape value chains, enabling companies to enter new markets or create entirely new product categories. Think of Blue Ocean Robotics, which evolved from hardware manufacturing to offering AI- and spatial computing-powered collaborative solutions, boosting revenue and partnerships. This framework isn’t just theoretical—it’s a practical guide for organizations to identify high-value tech pairings, align them with core strengths, and seize strategic opportunities. 🌟 Eight Transformative Technology Domains The report highlights eight domains driving this convergence revolution: AI, Omni Computing, Engineering Biology, Spatial Intelligence, Robotics, Advanced Materials, Next-Gen Energy, and Quantum Technologies. Each is broken down into 238 sub-components, assessed by maturity (from experimental Genesis to scalable Commodity). The magic happens when technologies at different maturity stages combine—like pairing cutting-edge agentic AI with stable computer vision to power autonomous systems. Compounding: As adoption scales, network effects and economies of scale kick in, driving down costs and accelerating innovation. NVIDIA’s pivot from general-purpose GPUs to AI-specific frameworks like CUDA is a prime example—catapulting its market cap from $300B to $3T in just three years! 💡 Why This Matters for You Organizations must: Bridge Silos: Build cross-domain expertise to combine mature and emerging technologies. Seize Adjacent Opportunities: Identify where tech convergence creates new value chains, like robotics firms moving from hardware to service-based models. Balance Risk and Reward: Invest strategically in high-potential combinations while addressing ethical concerns, like those tackled by the WEF’s AI Governance Alliance or #Quantum Initiative.

NVIDIA doesn’t want to build the biggest quantum computer. They want to build the world that needs one. At GTC 2025, amid the roaring buzz of AI models and robotics demos, NVIDIA’s real long game came into quiet focus. Their quantum strategy isn’t about hardware domination—it’s about infrastructure: accelerated computing, hybrid systems, and the connective tissue that will make quantum useful. In a conversation I had with Sam Stanwyck, Group Product Manager for Quantum Computing at NVIDIA, he painted the picture as: “We don’t build our own quantum computer, but our mission is to bring AI and accelerated computing to help everyone else who does.” This is the NVIDIA model—what they did for autonomous vehicles and AI at scale, they will now do for quantum: Build the tools. Power the systems. Here’s a snapshot of how that strategy is already taking shape: ⚇ NVAQC – Launching NVIDIA’s Accelerated Quantum Research Center in Boston with Massachusetts Institute of Technology, Harvard University, Quantinuum, QuEra Computing Inc., and Quantum Machines ⚇ QC Design – GPU-accelerated full-state fault-tolerance simulation using cuQuantum ⚇ Quantum Machines – Real-time error correction & AI calibration with GH200 chips ⚇ Pasqal – Hybrid quantum-classical development using CUDA-Q and Pulser ⚇ SEEQC – First digital QPU–GPU interface for ultra-low latency error correction ⚇ MITRE – CUDA-Q–powered quantum imaging for neurology and microelectronics ⚇ Quantum Rings – High-performance quantum simulation now integrated with CUDA-Q ⚇ Q-CTRL & Oxford Quantum Circuits (OQC) – speedup in error suppression via GPU-accelerated layout ranking ⚇ QuEra Computing Inc. – AI decoder for quantum errors using NVIDIA’s PhysicsNeMo transformers ⚇ Infleqtion – Contextual Machine Learning for real-time, multi-source AI using CUDA-Q Compute. AI. Quantum. It’s not just convergence—it’s choreography. Full writeup at The Quantum Insider here → https://lnkd.in/gFERCs44

NVIDIA CEO Jensen Huang recently claimed that practical quantum computing is still 15 to 30 years away and will require NVIDIA #GPUs to build hybrid quantum/classical supercomputers. But both the timeline and the hardware assumption are off the mark. Quantum computing is progressing much faster than many realize. Google’s #Willow device has demonstrated that scaling up quantum systems can exponentially reduce errors, and it achieved a benchmark in minutes that would take classical supercomputers countless billions of years. While not yet commercially useful, it shows that both quantum supremacy and fault tolerance are possible. PsiQuantum, a company building large-scale photonic quantum computers, plans to bring two commercial machines online well before the end of the decade. These will be 10,000 times larger than Willow and will not use GPUs, but rather custom high-speed hardware specifically designed for error correction. Meanwhile, quantum algorithms are advancing rapidly. PsiQuantum recently collaborated with Boehringer Ingelheim to achieve over a 200-fold improvement in simulating molecular systems. Phasecraft, the leading quantum algorithms company, has developed quantum-enhanced algorithms for simulating materials, publishing results that threaten to outperform classical methods even on current quantum hardware. Algorithms are improving 1000s of times faster than hardware, and with huge leaps in hardware from PsiQuantum, useful quantum computing is inevitable and increasingly imminent. This progress is essential because our existing tools for simulating nature, particularly in chemistry and materials science, are limited. Density Functional Theory, or DFT, is widely used to model the electronic structure of materials but fails on many of the most interesting highly correlated quantum systems. When researchers tried to evaluate the purported room-temperature superconductor LK-99, #DFT failed entirely, and researchers were forced to revert to cook-and-look to get answers. Even cutting-edge #AI models like DeepMind’s GNoME depend on DFT for training data, which limits their usefulness in domains where DFT breaks down. Without more accurate quantum simulations, AI cannot meaningfully explore the full complexity of quantum systems. To overcome these barriers, we need large-scale quantum computers. Building machines with millions of qubits is a significant undertaking, requiring advances in photonics, cryogenics, and systems engineering. But the transition is already underway, moving from theoretical possibility to construction. Quantum computing offers a path from discovery to design. It will allow us to understand and engineer materials and molecules that are currently beyond our reach. Like the transition from the stone age to ages of metal, electricity, and semiconductors, the arrival of quantum computing will mark a new chapter in our mastery of the physical world.

🚀 Excited to share that my latest paper “Quantum AI: Harnessing the Power of Quantum Computing for Scalable and Adaptive Learning” has now been officially published in the proceedings of IEEE International On-Line Test Symposium (IOLTS) 2025 🎉 In this work, I present a unified framework for building scalable and adaptive Quantum AI systems, with a focus on: 1. Quantum Long Short-Term Memory (QLSTM) for sequential learning 2. Quantum Federated Learning (QFL) for privacy-preserving distributed intelligence 3. Quantum Reinforcement Learning (QRL) for dynamic decision-making 4. Quantum Fast Weight Programmer (QFWP) for meta-learning and rapid adaptation 5. Differentiable Quantum Architecture Search (DiffQAS) for automated circuit design Despite challenges such as noise, decoherence, and limited qubits, this paper outlines strategies—hybrid training, error-aware optimization, and scalable architectures—that push us toward trustworthy, generalizable, and future-ready Quantum AI. I’m grateful for the opportunity to contribute to IEEE IOLTS and the broader quantum computing community. Looking forward to continuing this journey toward making Quantum AI a practical reality. 🌌✨ 📄 Read the paper here: https://lnkd.in/eNMnVcjt You can get the full text also here: https://lnkd.in/e5HKx-qH #QuantumAI #MachineLearning #ReinforcementLearning #FederatedLearning #QuantumComputing #IOLTS2025

Would #Quantum maximize the impact of #AI in scientific discovery? On its own, AI is already reshaping scientific discovery by compressing timelines and uncovering insights in ways once thought impossible. But speed without reliability is not enough. We still see AI struggle to distinguish true breakthroughs from rediscoveries, and its predictions often lack explainability. The “black box” predictions you get from AI systems simply lack the scientific foundation that researchers rely on to conduct their work. We believe that’s where quantum, even in its current state, could make a difference in certain domains. Grounded in the laws of physics, quantum methods bring the rigor and depth that AI alone cannot provide yet. They can validate AI’s predictions, generate higher-quality data, and even open up new areas where no datasets exist. The real opportunity lies in integration. Combining these two technologies can transform the way we conduct research, expanding what is scientifically and technologically possible. Our research teams have been hard at work exploring this concept further, and our latest publication by Phalgun L. explores how this could redefine the future of materials science, structural biology, and beyond. We’d love to get your views on our perspective: https://lnkd.in/eMrRbBd4 Franck Greverie Julian van Velzen Nicolas Gaudilliere Sally Epstein Martin Brock

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

Quantum AI is a topic I have followed with curiosity for some time, but what strikes me today is how quickly it is shifting from theoretical promise to practical experimentation. In today's infographic I have shown how businesses can already explore quantum‑powered AI today using cloud APIs from IBM, Amazon Braket, Azure Quantum and Google’s Quantum Engine. These providers enable hands‑on experimentation with hybrid architectures, high‑dimensional data handling, model accuracy enhancement and faster training workflows. Some benefits are already within reach, especially in areas like model training speed, dimensionality reduction, and algorithm precision. What remains essential is building the right skills and understanding how classical and quantum elements can coexist in real workflows. We are still early in this journey, but the foundations are already in place. It is a moment to reflect not only on the technology itself, but on how we prepare to integrate it meaningfully into business and research contexts. #QuantumAI #QuantumComputing #EmergingTechnologies #CloudComputing #AI