Advancing Superintelligence With AI and Quantum Computing

China’s Photonic Quantum Chip Delivers a 1,000-Fold Speed Boost for AI and Supercomputing Introduction China has unveiled a photonic quantum chip that delivers more than a thousandfold acceleration in complex computation, marking a major leap in AI data center performance and quantum-classical hybrid computing. Honored with the Leading Technology Award at the 2025 World Internet Conference, the technology positions China at the forefront of quantum-enabled high-performance computing. Breakthrough Capabilities • The chip, developed by CHIPX and Shanghai-based Turing Quantum, integrates over 1,000 optical components onto a 6-inch wafer using monolithic photonic integration. • It combines photon–electronics co-packaging, wafer-level fabrication, and system integration—an achievement its creators call a world first. • Already deployed in aerospace, biomedicine, and finance, it delivers processing speeds beyond the limits of classical silicon. • Photonic computing reduces power consumption, increases bandwidth, and accelerates AI model training and cloud-scale computation. • The architecture is scalable toward future quantum systems, with a design pathway that could support up to 1 million qubits. Industrialization and Global Competition • CHIPX has built a full closed-loop pilot production line for thin-film lithium niobate photonic wafers, capable of producing 12,000 wafers annually. • Each wafer yields roughly 350 chips—bringing industrial-grade optical quantum computing into real-world deployment for the first time. • Rapid prototyping has improved tenfold, cutting development cycles from six months to two weeks. • China’s progress signals a strategic push into a field historically led by Europe and the U.S., where companies such as SMART Photonics and PsiQuantum are expanding their own photonic manufacturing lines. Implications for AI, Quantum, and National Power • Photonic chips deliver the speed, efficiency, and low latency needed for next-generation AI training, 5G and 6G networks, and secure quantum communication. • Their scalability enables hybrid quantum-classical systems capable of tackling problems in chemistry, finance, and national defense simulation. • With quantum threats rising globally, photonic architectures offer a pathway to resilient, high-throughput compute infrastructure that traditional chips cannot match. Conclusion China’s new photonic quantum chip marks a decisive step toward industrial-scale quantum acceleration. By pairing optical physics with mature semiconductor manufacturing, China has positioned itself to compete aggressively in the race for AI dominance, quantum-secure communication, and next-generation supercomputing infrastructure. I share daily insights with 33,000+ followers across defense, tech, and policy. If this topic resonates, I invite you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

Quantum-inspired machine learning will continue to play a significant role improving Artificial Intelligence performance. This is primarily due to myriad available applications utilizing efficient tensor network approximations that can be applied to complex or quantum systems. [01] Tensor networks, alongside active areas of dequantized algorithms and quantum variational algorithms represent effective 'QiML 2.0' software run on traditional computers. [02-04] In addition, quantum-inspired analogues will likely be further extended akin to Physics-Informed Machine Learning to 'assist machine learning tasks, representation of physical prior, and methods for incorporating physical prior.' [05] Recent ground breaking literature shown below has elevated tensor networks from efficient research tools to now increasing the pace of AI across disciplines. A) Tensor network/neural network hybrid performed better than standalone tensor networks or neural networks by NSF, MIT, and Harvard researchers. [06] B) More explainable and controllable compression of a Generative AI LLM to a fraction of its size by Multiverse Computing. [07] C) Researchers outperformed a leading quantum computer experiment in speed, precision, and accuracy - with scaling now corresponding to an infinite number of quantum bits on traditional hardware by Flatiron Institute, NYU. [08] Leading software platforms ITensor on C++ and Julia, and TeNPy on Python have been featured in a number of 2024 papers, and both maintain discussion forums to assist with tensor network developments. [09-12] In summary, High dimensional data in AI can now be distributed across tensor networks in more informed ways due to recent literature advancements and software library improvements. References [01] Tensor networks: https://lnkd.in/gipfeK_q [02] Ewin Tang: https://lnkd.in/gfgNSfKY [03] VQA: https://lnkd.in/gitb6TSq [04] QiML survey: https://lnkd.in/g97vr3_r [05] Physics-Informed ML: https://lnkd.in/gffmUFSx [06] NSF, MIT, Harvard: https://lnkd.in/gNkXEUtW [07] Multiverse: https://lnkd.in/gjNsqWJu [08] Flatiron, NYU: https://lnkd.in/gZmyJckE   [09] ITensor: https://lnkd.in/gXJWFNCU  [10] TeNPy: https://lnkd.in/g3Ciyyxs [11] ITensor: https://lnkd.in/gwhAp4BE [12] TeNPy: https://lnkd.in/gU5ceMFd

One of the first papers in the World to outline quantum and agentic AI? This paper explores the intersection of quantum computing and agentic AI by examining how quantum technologies can enhance the capabilities of autonomous agents, and, conversely, how agentic AI can support the advancement of quantum systems. We analyze both directions of this synergy and present conceptual and technical foundations for future quantum-agentic platforms. Our work introduces a formal definition of quantum agents and outlines potential architectures that integrate quantum computing with agent-based systems. As a proof-of-concept, we develop and evaluate three quantum agent prototypes that demonstrate the feasibility of our proposed framework. Furthermore, we discuss use cases from both perspectives, including quantum-enhanced decision-making, quantum planning and optimization, and AI-driven orchestration of quantum workflows. By bridging these fields, we aim to chart a path toward scalable, intelligent, and adaptive quantum-agentic ecosystems. Eldar Gunter Sultanow, Dr. Mark Tehrani, Siddhant Dutta, Muhammad Shahbaz Khan https://lnkd.in/eDDmTWtQ

Exploring Quantum AI advantages this AI Appreciation Day!   At Terra Quantum AG, we’re pioneering the next wave of innovation through hybrid-quantum computing solutions. 💡Here’s how we’re harnessing this power to revolutionize #AI: ‣We integrate hybrid-quantum techniques with classical methods to achieve better quality answers with less data, transforming industries that rely on precise and efficient data analysis. ‣Our Parallel Hybrid Networks (#PHNs) combine the strengths of classical multi-layered perceptrons with variational quantum circuits to handle complex datasets more effectively. ‣Our unique Quantum Encoding (#QuEnc) method tackles complex optimization problems and paves the way for more advanced AI applications. ‣In addition, our Tetra-AML toolbox automates neural architecture search and hyperparameter optimization, significantly enhancing the performance and efficiency of AI models through tensor network techniques. ‣Our tensor-network-based simulation approach allows us to generate complex phenomena, providing deeper insights and fostering innovation in AI research. The Tetra-AML framework compresses large neural networks, such as ResNet-18, by up to 14.5 times with minimal accuracy loss, making AI deployment more resource-efficient. 🤝Advancing AI through quantum isn’t just about staying ahead in the tech race, it's about building a future where these technologies work together to solve real-world problems. Let's celebrate AI Appreciation Day by embracing the future of AI and quantum technology. #QuantumIsNow #QuantumComputing #ArtificalIntelligence #QuantumAI #AIAppreciationDay

Sometimes, it feels like the future is creeping up faster than we can process. Back in 2001, researchers used a 7-qubit quantum computer to factor the number 15- a symbolic, an important demo that proved physical qubits could run real algorithms. Two decades later, we’ve gone from theoretical proofs to verifiable performance. The pace of progress in quantum computing isn’t linear anymore, it’s compounding. Researchers from Google, MIT, Stanford, and Caltech just achieved what they call a verifiable quantum advantage using Google’s new Willow processor. It performed a specific physics simulation ~13,000× faster than today’s top supercomputers. Days later, Nvidia announced NVQLink- a system designed to connect quantum processors (QPUs) with AI/GPU supercomputers. Jensen Huang called it “the Rosetta Stone connecting quantum and classical supercomputers.” If Willow shows the engine works, NVQLink builds the road network it can run on. For investors and enterprises, this dual breakthrough matters because it de-risks the full compute stack. We’re entering the Hybrid Compute Era where AI and quantum don’t compete, they co-evolve. AI will stabilize qubits, interpret noisy outputs, and orchestrate workloads. Quantum will solve problems that today’s AI models can’t solve such as molecular design to next-gen cryptography. The line between them is going to blur. And when it does, the real advantage won’t lie in algorithms it’ll lie in orchestration: who controls the layer that makes both worlds talk. #QuantumComputing #AI #DeepTech #Nvidia #GoogleAI #Innovation #Supercomputing #TechInvesting #FutureOfTech #QuantumAdvantage Image: An illustration showing three NVQLinks connecting quantum processors and classical supercomputers. NVIDIA

If you thought the AI hype was settling into a quiet rhythm, NVIDIA GTC 2026 just proved we are only at the starting line. We aren't just talking about generating text or images anymore. We are stepping fully into the era of Agentic AI, Quantum computing, and Physical AI, the next frontier of robotics. What really struck me during Jensen Huang’s keynote wasn't just the hardware; it was the fundamental shift in how we measure computing value. We are no longer just processing data. Tokens are becoming a true unit of production. Inference is becoming a form of throughput. Entire infrastructures are now being designed around continuous intelligence generation. The sheer scale of the ecosystem required to support this connected future is staggering. As Jensen Huang made clear, no one builds this alone. We are handing the keys to a global community of developers and startups who are on the front lines, turning these massive compute platforms into the enterprise applications, autonomous networks, and intelligent robots of tomorrow. Here are the shifts from this week that I believe are fundamentally rewiring our industry: ✅ Telecom's Autonomous Leap: We are seeing the AI-RAN vision jump from the whiteboard into the real world. By bringing physical AI and edge computing directly to our cell towers, we are turning passive telecommunications infrastructure into intelligent, active networks. This is the critical foundation for fully autonomous networks and our next generation of connectivity. ✅ The Age of Agents: Agentic AI is driving the next massive wave of computing demand. With the new Vera Rubin platform and the momentum behind open-source agent frameworks, developers finally have the horsepower to build AI that can act, execute, and run long-term tasks safely. ✅ The Quantum-AI Convergence: This was the quiet powerhouse announcement of the week. By bridging classical GPU supercomputing with quantum processors through CUDA-Q, we are moving toward a unified, hybrid infrastructure. We are building the foundation for AI-accelerated quantum factories. ✅ Powering the Intelligence Factory: Continuous intelligence generation requires massive power. The innovations we are seeing in energy management, advanced cooling, and grid orchestration show that the physical constraints of AI are being solved in real-time. We are building the resilient energy backbone required to run sustainable data centers. The gap between digital intelligence and physical infrastructure is officially closing. We are watching a complete rewiring of how our data centers, networks, and computational models interact. If you want a full breakdown of everything NVIDIA shared this week, this is a great place to start: https://lnkd.in/evMegTyJ #NVIDIAGTC #ArtificialIntelligence #AgenticAI #Telecom #AIRAN #QuantumComputing #Robotics #EdgeComputing #FutureOfTech #ThoughtLeadership

I’ve always thought that when quantum computing matures—still a few years away—and fully integrates with AI, really astounding tech advances will take place. My conversation with Murray Thom, Vice President at D-Wave, shifted my viewpoint. First, he argued, quantum computing is much closer to mainstream adoption than is widely known. As for AI, yes, quantum and AI have a bright future together—he called them complementary technologies (see his comments in the short video clip.) Most interesting, he talked about the “hybrid combination” of quantum and classical computing: “The winning pattern is hybrid. Let classical algorithms do what they do best, and call out to the quantum computer when you need to make big coordinated moves in the solution space. That’s why we deliver quantum through an API and a platform that orchestrates classical and quantum resources together. “From an enterprise lens, you need reliability and governance. Our cloud service runs with sub-second responses, ~99.9% availability, and SOC 2 compliance. That means teams can plug quantum into existing workflows and CI/CD just like any other high-availability service—no reinvention required.” Full conversation: https://lnkd.in/gEy_K46m #quantum #AI #QCaaS D-Wave

Anyone interested in the future of computing — and the intersection of supercomputers and quantum computing — should take a close look at Nvidia’s latest announcement, which the The Wall Street Journal’s Isabelle Bousquette covered under the headline: “Nvidia Connects Quantum with AI.” While NVIDIA isn’t developing its own quantum computers, the company is creating a breakthrough solution for linking quantum processors with AI supercomputers, called NVQLink. As NVIDIA CEO Jensen Huang explains: “NVQLink is the Rosetta Stone connecting quantum and classical supercomputers.” This innovation addresses the critical need to integrate quantum computers with high-performance Super Computers that can perform calculations they can’t — and correct the natural errors that occur in quantum processes (a challenge known as error correction). The future of computing increasingly points toward hybrid infrastructure, combining quantum processors (QPUs) and AI chips like Nvidia’s GPUs. As so often in life, progress doesn’t come from choosing one path over another — but from combining two powerful technologies to create something truly superior. Read more here: https://lnkd.in/gVgDT-kS

The next breakthrough in AI won’t come from larger models alone - it will come from smarter workflows. And that’s exactly where quantum machine learning (QML) is poised to change the game. As enterprises race toward agentic AI-systems that reason, act, and continuously learn, one challenge becomes clear: Traditional compute won’t scale fast enough to support the complexity of these autonomous workflows. Quantum machine learning offers a new path. 🔹 Faster pattern discovery for agents that must make real-time decisions 🔹 Enhanced optimization for complex, multi-step workflows 🔹 Richer embeddings and feature spaces that unlock insights classical models can’t reach 🔹 Massive acceleration of training cycles that currently bottleneck enterprise AI adoption But the real catalyst isn’t the hardware. It’s the enterprise itself. By embedding QML into existing AI pipelines - from agent orchestration to control towers, governing frameworks, and data-driven automation, businesses become the force that pulls the quantum era into the present. Not as an experiment, but as a competitive advantage. The quantum + AI era won’t arrive on its own. Enterprise adoption is what will ignite it. The organizations that lean in now, piloting hybrid quantum-AI workflows, investing in talent, and building the integration muscle, will define the next decade of intelligent systems. The opportunity is here. The catalyst is ready. The question is who will lead. #QuantumAI #Future #Innovation #DigitalTransformation

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