Quantum Enhanced Artificial Intelligence

Quantum computing promises to making LLMs more efficient. And it's already working on real hardware. Efficient fine-tuning of large language models remains a critical bottleneck in AI development, with most researchers focused on purely classical computing approaches. A new paper from Chinese researchers demonstrates how quantum computing principles can dramatically reduce the parameters needed while improving model performance. The team introduces Quantum Weighted Tensor Hybrid Network (QWTHN), which combines quantum neural networks with tensor decomposition techniques to overcome the expressive limitations of traditional Low-Rank Adaptation (LoRA). By leveraging quantum state superposition and entanglement, their approach achieves remarkable efficiency: reducing trainable parameters by 76% while simultaneously improving performance by up to 15% on benchmark datasets. Most importantly, this isn't just theoretical - they've successfully implemented inference on actual quantum computing hardware. This represents a tangible advancement in making quantum computing practical for AI applications, demonstrating that even current-generation quantum devices can enhance the capabilities of billion-parameter language models. The integration of quantum techniques into traditional deep learning frameworks might become standard practice for resource-efficient AI development in the future. More on Quantum Hybrid Networks and other AI highlights in this week's LLM Watch:

The first time I saw machine learning in action for quantum computing was during my time at the Niels Bohr Institute, University of Copenhagen. Anasua Chatterjee and colleagues were exploring AI-driven methods to automate the tune-up of spin qubits. To be honest, I didn’t give it much attention at the time. Fast forward to today, and AI feels like the secret sauce accelerating almost every aspect of quantum computing. Think about it: quantum computing is all about mastering exponentially complex systems. AI thrives in high-dimensional, data-rich environments. This pairing? It’s like finding the perfect dance partner. Here’s what’s exciting: AI isn’t just helping to debug or optimize—it’s diving deep into the heart of quantum research. It’s designing qubits, discovering novel error correction codes, and making circuit synthesis more efficient than ever. Tasks that once took teams of researchers weeks to figure out are now becoming automated, adaptive, and scalable. One example I really like? AI-enhanced quantum error correction. Researchers are using neural networks and transformers to achieve error rates below what traditional methods can manage—and they’re doing it at a fraction of the computational cost. Another idea that’s caught my attention is quantum feedback control using transformers. This approach could change how we stabilize and steer quantum systems in real time by leveraging AI models to predict and counteract noise. The question now is: how long before we see more of these theoretical breakthroughs transition to real hardware? Natalia Ares, is quantum feedback control with transformers already in the works? This is such an exciting direction for quantum control and AI! 📸 Credits: Yuri Alexeev et al. (2024)

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

The AI race isn’t just about building smarter models—it’s about redefining what’s possible. Google’s latest quantum computing breakthrough has me convinced they’re not just competing; they’re paving the road ahead. If you missed the news, Google announced their quantum computer solved a problem in minutes that would take a supercomputer thousands of years. This isn’t just a headline—it’s the foundation for the next era of AI. Having spent almost 16 years at Google, I’ve seen how they approach innovation. They don’t just iterate on the present; they build for the future. Quantum computing is their latest move, and it’s a game-changer. Quantum and AI: What’s Next in 10 Years? Here’s how I think quantum computing will transform AI over the next decade: - Exponential Model Growth Today’s AI models are already pushing the limits of traditional compute. Quantum computing will break through these barriers, allowing us to train models at scales we can’t even imagine today. Models with trillions of parameters could become the norm, driving hyper-accurate predictions and complex decision-making. - Revolutionizing Real-Time AI Quantum computing will dramatically accelerate data processing. Think real-time, high-fidelity AI for autonomous vehicles, climate modeling, and even personalized healthcare. Imagine AI systems capable of analyzing and acting on global-scale data streams in seconds. - Breaking Through Optimization Challenges AI has struggled with problems like protein folding, material science, and logistics optimization. Quantum computing’s ability to solve these challenges could lead to breakthroughs in drug discovery, sustainable energy solutions, and next-gen manufacturing processes. - AI Meets General Intelligence As quantum-enabled AI models become faster and smarter, they’ll inch closer to what we dream of as general intelligence. While still speculative, the quantum-boosted ability to simulate and synthesize massive datasets could bring us closer to AI that genuinely understands context and solves problems like humans. At Google, I saw how they consistently pushed boundaries. They don’t just dabble in moonshots—they commit, iterate, and build the ecosystems needed to sustain them (e.g Self Driving). Quantum computing is no exception. It’s not a side project; it’s part of a long-term vision to reshape computing and AI itself. The AI race isn’t just about now—it’s about what’s next. And with quantum, Google just showed us a glimpse of what’s possible. What do you think? How do you see quantum computing shaping the future of AI? #AI #QuantumComputing #Google #Innovation #FutureTech

🚀 New arXiv Preprint: Automating Quantum-Enhanced Neural Network Design with Differentiable Architecture Search 📍 Quantum-Train meets Differentiable QAS Proud to share our latest research now available on arXiv: “Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation” 🔗 https://lnkd.in/eNct2dwh In this work, we tackle one of the fundamental challenges in Quantum Machine Learning (QML): How do we design powerful Quantum Neural Networks (QNNs) without requiring expert-level quantum circuit knowledge? 🔧 Our solution: We propose a fully differentiable, end-to-end optimization framework—DiffQAS-QT—that automates the design of QNNs used in the Quantum-Train (QT) framework. Instead of manually crafting circuits, our method learns both the circuit architecture and the variational parameters simultaneously. 🧠 QT shifts the quantum role from inference to parameter generation: Quantum models generate classical neural network weights → inference becomes purely classical, enabling real-world deployment today. ⚙️ We benchmarked DiffQAS-QT across: 🖼️ Classification (MNIST, FashionMNIST) 📈 Time-series prediction (Bessel, SHM, Delayed Quantum Control, NARMA) 🤖 Reinforcement Learning (A3C agents in MiniGrid) 💡 Highlights: Up to 380× parameter reduction while maintaining performance Generalizes across learning paradigms: supervised, temporal, interactive Shows superior training stability compared to handcrafted QNNs Demonstrates QT’s capacity to act as a powerful quantum parameter generator This is part of a broader effort toward building scalable, robust hybrid quantum-classical learning systems—a step closer to practical Quantum AI. 🔬 Joint work with: Chen-Yu Liu (NTU), Louis Chen (Imperial), Wei-Jia Huang (Foxconn), Yen Jui Chang (CYCU), Wei-hao Huang (Jij Inc.) #QuantumMachineLearning #DifferentiableQAS #QuantumNeuralNetworks #QuantumAI #QRL #QuantumTrain #arXiv #QAS #QNN #HybridLearning #MetaLearning #DiffQASQT #Quantum #ArtificialIntelligence

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

Former Intel CEO Pat Gelsinger recently stated that “quantum computing will pop the AI bubble” and that GPUs may not survive the decade. His comment highlights a deeper truth: the GPU-driven model powering today’s AI boom is reaching physical and economic limits. Power consumption, heat dissipation, data-center sprawl, and diminishing returns now define the landscape. As we advance the SEALSQ quantum–semiconductor roadmap, it becomes increasingly clear that the next wave of compute will not be built on ever-larger GPU clusters, but on quantum-ready, energy-efficient semiconductor architectures designed for the problems GPUs were never meant to solve. Quantum systems can explore vast computational spaces simultaneously, offering exponential advantages in optimization, cryptography, secure AI, and simulation. Combined with semiconductor integration, they promise radical reductions in energy use and operational cost — something no amount of GPU scaling can compensate for. The future of AI will be defined by hybrid architectures that fuse quantum principles with secure semiconductor design and post-quantum cryptography. This is not about “popping” AI, but maturing it — shifting from brute-force computation to smarter, safer, and more sustainable models. Quantum-enhanced chips will empower nations and industries to build secure, sovereign digital infrastructures, an imperative in a world increasingly exposed to cyber and geopolitical risks. GPUs have taken us far, but the next decade belongs to architectures that merge quantum innovation with trusted semiconductor technology. At SEALSQ, we are building these foundations. The future of AI will not rely solely on scale — it will rely on intelligence, security, and quantum-empowered efficiency. https://lnkd.in/e7DuKB9f

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

💡AI Can Now Feel Surfaces -- via Quantum Mechanics💡 AI technologies have already advanced in seeing, conversing, calculating, and creating. However, one area that AI hasn't mastered yet is touch—the ability to "feel" and discern surface textures. That's changing thanks to the research from the Center for Quantum Science and Engineering (CQSE) at Stevens Institute of Technology. 🔬 Marrying Quantum Mechanics and AI Physics professor Yong Meng Sua, along with CQSE Director Yuping Huang and doctoral candidates Daniel Tafone and Luke McEvoy, have developed a quantum-lab setup that combines photon-firing scanning lasers with advanced AI algorithms. This system allows AI to accurately detect and measure surface topography by interpreting speckle noise—normally considered detrimental in imaging—as valuable data. "This is a marriage of AI and quantum," explains Tafone. ⚙️ How It Works Photon Firing Scanning Laser: Pulses a specially created beam of light at a surface. Speckle Noise Utilization: Reflected photons carry speckle noise, which the AI interprets to discern surface texture. High Precision: Achieved an accuracy within 4 microns—comparable to the best industrial profilometers. 🚀 Potential Applications 1️⃣Medical Diagnostics: Enhances the detection of skin cancers by measuring tiny differences in mole roughness, aiding in distinguishing benign conditions from malignant melanomas. "Tiny differences in mole roughness, too small to see with the human eye but measurable with our proposed quantum system, could differentiate between those conditions," explains Huang. 2️⃣ Manufacturing Quality Control: Detects minuscule defects in components that could lead to mechanical failures, ensuring product reliability and safety. 3️⃣ Enhanced LiDAR Technology: Improves devices like autonomous cars, smartphones, and robots by adding precise surface property measurements at very small scales. 🌐 Enriching AI's Sensory Capabilities Since LiDAR technology is already widely used, this method could significantly enhance its functionality. "Our method enriches their capabilities with surface property measurement at very small scales," Huang concludes. 📄 Original Paper: https://lnkd.in/gSxfYaK3 Thomas J. White IV #AI #QuantumTechnology #MachineLearning #SurfaceMeasurement #Innovation #Research #LiDAR #MedicalDiagnostics

Quantum whispers in the GPU roar For Wall Street, more AI means more GPUs, more datacenters, more cloud contracts. And OpenAI–NVIDIA $100B deal locks it in. But quieter signals from research point to a second axis of scaling: not just more metal, but smarter math. It’s about quantum. Let me give you some notable examples from the last week research: 1. Compression: QKANs and quantum activation functions Paper: Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks Offers replacing fixed nonlinearities with single-qubit variational circuits (DARUANs). These tiny activations generate exponentially richer frequency spectra → so we get same power with exponentially fewer parameters. Quantum KANs (QKANs), built on this idea, already outperformed MLPs and KANs with 30% fewer parameters. 2. Exactness: Coset sampling for lattice algorithms Paper: Exact Coset Sampling for Quantum Lattice Algorithms Proposes a subroutine that cancels unknown offsets and produces exact, uniform cosets, making subsequent Fourier sampling provably correct. Injecting mathematically guaranteed steps into probabilistic workflows means precision: fewer wasted tokens, fewer dead-end paths, less variance in cost per query. 3. Hybridization: quantum-classical models in practice Paper: Hybrid Quantum-Classical Model for Image Classification These models dropped small quantum layers into classical CNNs, showing that they can train faster and use fewer parameters than classical versions. ▪️ What does this mean for inference scaling? Scaling won’t only mean bigger clusters for bigger models. It might also be about: - extracting more from each parameter - cutting errors at the source - and blending quantum and classical strengths. Notably, this direction is not lost on the companies like NVIDIA. There are several signs: • NVIDIA's CUDA-Q – an open software platform for hybrid quantum-classical programming. • NVIDIA also launched DGX Quantum, a reference architecture linking quantum control systems directly into AI supercomputers.  • They are opening a dedicated quantum research center with hardware partners. • Jensen Huang is aggressively investing into quantum startups like PsiQuantum (just raised $1B, saying it’s computer will be ready in two years), Quantinuum, and QuEra through NVentures - a major strategic shift in 2025, validating quantum's commercial timeline. ▪️ So what we will see:  GPUs will remain central. But quantum ideas will be slipping into the story of inference scaling. They are still early, but it's the new axis worth paying attention to. What do you think about it?