AI Model Development for Quantum Systems

🔗✨ Exploring the Future of Quantum Computing with Physics-Informed Neural Networks (PINNs) ✨🔗 Excited to highlight the pioneering work by Stefano Markidis that dives deep into the potential of Quantum Physics-Informed Neural Networks (Quantum PINNs) for solving differential equations on hybrid CPU-QPU systems! 📘 What’s this about? Physics-Informed Neural Networks (PINNs) have proven their versatility in addressing scientific computing challenges. This study extends PINNs into the quantum realm using Continuous Variable (CV) Quantum Computing, offering a new approach to solving Partial Differential Equations (PDEs) with quantum hardware. Key Highlights: ✅ Quantum Meets Physics: The framework combines CV quantum neural networks with classical methods to tackle PDEs like the 1D Poisson equation. ✅ Optimizer Insights: Traditional optimizers like SGD outperformed adaptive methods in this quantum landscape, highlighting the unique challenges of quantum optimization. ✅ Scalability: Explores batch processing and neural network depth for more effective performance on quantum systems. ✅ Programming Ease: Tools like Strawberry Fields and TensorFlow simplify the integration of quantum and classical computations. 💡 Why it matters: This research doesn't just apply PINNs to quantum computing—it highlights the differences between classical and quantum approaches, paving the way for advancements in quantum PINN solvers and their real-world applications in computational physics, electromagnetics, and more. 📖 Dive deeper: Access the full study here: https://lnkd.in/dZm3F3CR Source code available: https://lnkd.in/dAsXxnbN What are your thoughts on combining quantum computing with AI for scientific breakthroughs? Let’s discuss! 🚀 #QuantumComputing #PhysicsInformedNeuralNetworks #ScientificComputing #HybridAI #PDEsolvers #Innovation

NVIDIA’s launch of "Ising" marks the introduction of the world’s first open-source #AI model family purpose-built for #quantum #computing workflows. The platform targets two of the most critical bottlenecks in quantum systems—processor calibration and real-time error correction—by embedding AI directly into quantum control loops. Released across developer ecosystems (GitHub, Hugging Face) and integrated with CUDA-Q, Ising positions AI as the #orchestration layer for hybrid quantum-classical computing. Early adoption by institutions such as Fermilab and Harvard University signals immediate traction in #research. Strategically, this launch reframes AI not just as an application layer, but as foundational infrastructure for scalable, fault-tolerant quantum systems. Ising is fundamentally differentiated by its dual-model architecture: a 35B-parameter vision-language model for automated quantum calibration and a #3D CNN-based decoder for real-time quantum error correction. This architecture replaces manual calibration workflows with agentic AI pipelines, achieving up to 2.5× faster and 3× more accurate decoding while requiring significantly less training #data. Technically, it integrates tightly with NVIDIA’s CUDA-Q stack and NVQLink interconnect, enabling low-latency coupling between GPUs and quantum processing units (QPUs). Unlike generative AI models, Ising operates as a physics-aware control system, optimized for noisy qubit environments and scalable to millions of qubits, effectively acting as an AI control plane for quantum hardware. The Ising launch materially reshapes the quantum ecosystem by positioning NVIDIA as the control-plane leader in quantum computing, despite not manufacturing quantum hardware. It accelerates commercialization timelines by addressing error correction—widely seen as the primary barrier to the development of useful quantum systems. Market response was immediate, with quantum stocks (IonQ, Rigetti Computing, D-Wave) surging on expectations of faster industry maturation. Strategically, Ising challenges incumbents by shifting value from hardware-centric differentiation to AI-driven orchestration, thereby reinforcing a hybrid architecture in which GPUs and QPUs co-evolve. This positions NVIDIA as a central enabler across competing quantum vendors, potentially standardizing its ecosystem as the de facto operating layer for quantum-AI #convergence. These architectures intensify system autonomy and complexity, requiring dynamic governance models and adaptive #cyber-#ethics to continuously monitor, audit, and recalibrate #risks across hybrid quantum-AI control planes. #strategy #governance #business #investments #technology #future #digital

🚀 New Paper on arXiv! I’m excited to share our latest work: “Learning to Program Quantum Measurements for Machine Learning” 📌 arXiv: https://lnkd.in/euRhBQJM 👥 With Huan-Hsin Tseng (Brookhaven National Lab), Hsin-Yi Lin (Seton Hall University), and Shinjae Yoo (BNL) In this paper, we challenge a long-standing limitation in quantum machine learning: static measurements. Most QML models rely on fixed observables (e.g., Pauli-Z), limiting the expressivity of the output space. We take this one step further--by making the quantum observable (Hermitian matrix) a learnable, input-conditioned component, programmed dynamically by a neural network. 🧠 Our approach integrates: 1. A Fast Weight Programmer (FWP) that generates both VQC rotation parameters and quantum observables 2. A differentiable, end-to-end architecture for measurement programming 3. A geometric formulation based on Hermitian fiber bundles to describe quantum measurements over data manifolds 🧪 Experiments on noisy datasets (make_moons, make_circles, and high-dimensional classification) show that our dual-generator model outperforms all traditional baselines—achieving faster convergence, higher accuracy, and stronger generalization even under severe noise. We believe this work opens the door to adaptive quantum measurements and paves the way toward more expressive and robust QML models. If you're working on QML, differentiable quantum programming, or quantum meta-learning, I’d love to connect! #QuantumMachineLearning #QuantumComputing #QML #FastWeightProgrammer #DifferentiableQuantumProgramming #arXiv #HybridAI #AI #Quantum

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:

I'm really happy with the rapid development of CUDA-Q QEC, our toolkit for quantum error correction. QEC is an incredibly rich and fast-moving field, and in CUDA-Q QEC we aim to provide a platform with a diverse set of accelerated decoders, AI infrastructure, tools to enable researchers to develop and test their own codes, decoders, and architectures, hopefully even better than our own! As we dig deeper into the problem of scalable QEC, the benefits of GPUs and AI have become much clearer. We started with research tools, for simulation and offline decoding, which is still an important capability. Now with the 0.5.0 release we also provide the infrastructure for real-time decoding, where syndrome processing occurs concurrently with quantum operations. This release also introduces GPU-accelerated algorithmic decoders like RelayBP, a promising approach developed in the past year that aims to overcome the convergence limitations of traditional belief propagation. For scenarios demanding maximum throughput, we have integrated a TensorRT-based inference engine that allows researchers to deploy custom AI decoders trained in frameworks like PyTorch and exported to ONNX directly into the quantum control loop. To address the complexities of continuous system operation, we added sliding window decoders that handle circuit-level noise across multiple rounds without assuming temporal periodicity. These tools are designed to be hardware-agnostic and scalable, supporting our partners across the ecosystem who are building the first generation of reliable logical qubits. Check out the full technical breakdown in our latest developer blog by Kevin Mato, Scott Thornton, Ph.D., Melody Ren, Ben Howe, and Tom L. https://lnkd.in/gvC__zRd

Really happy to see the official publication today of our paper in Nature Machine Intelligence: "Machine Learning for Practical Quantum Error Mitigation" Haoran Liao, Derek S. Wang, Iskandar Sitdikov, Ciro Salcedo, Alireza Seif, Zlatko Minev 🔍 Context: Quantum computers progress to outperform classical supercomputers, but quantum errors remain the primary obstacle. Quantum error mitigation offers a solution but at the high cost of added runtime. 🤔 Key Question: Can classical machine learning help us overcome errors in today's quantum computers by lowering mitigation overheads, in practice, on real hardware, at the 100 qubit+ scale? 🔬 Our Findings: Using both simulations and experiments on state-of-art quantum computers (up to 100 qubits), we find that machine learning for quantum error mitigation (ML-QEM) can: - Significantly reduce overheads. - Maintain or even outperform the accuracy of traditional methods. - Deliver nearly noise-free results for quantum algorithms. We tested multiple machine learning models on various quantum circuits and noise profiles. And, by leveraging ML-QEM, we were able to mimic conventional mitigation results for large quantum circuits, but with much less overhead. 🌟 Conclusion: Our research underscores the potential synergy between classical hashtag#ML and hashtag#AI and quantum computing. We're excited about the prospects and further research! 🙌 Big thanks to the dream team and many folks who contributed! Let’s share and discuss the implications of this exciting work! 🌟👇 📄 Paper: Nature Machine Intelligence https://lnkd.in/dGYzC3fq 🔓 Free access: View the paper here https://lnkd.in/dN222X7D 📚 Preprint on arXiv https://lnkd.in/dGbzjtjA 👩💻 Code Repository: Explore on GitHub https://lnkd.in/dcn-xPtm 🎥 Seminar: Watch hashtag#IBM @Qiskit on YouTube here https://lnkd.in/dEPRcMVK https://lnkd.in/e7JFgc3J

“Before you can use a quantum computer, you first need to be able to turn it on.” Research that I carried out during my PhD at Oxford has brought us closer to that goal. I'm pleased to share that our paper titled “Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning” has been published in Nature Scientific Reports. We developed CATSAI (pronounced: Cats-eye), an algorithm capable of tuning three different semiconductor quantum devices—silicon finFET, Ge/Si nanowire, and Ge/SiGe heterostructure— to double quantum dots, using a single approach Forming double quantum dots in these devices is a key step towards creating qubits, the essential building blocks of quantum computers. Not long ago, it was thought that each device type would need its own specialized algorithm. CATSAI changes that by tuning different devices and revealing the complex hypersurfaces that separate regions where current flows from those where it’s blocked. In some cases, finding a double quantum dot is like finding a needle in a haystack—sometimes in just 0.002% of the search space. CATSAI does this on the order of minutes —far quicker than what would typically be possible manually. I remember when I first tried to tune a double quantum dot at the start of my PhD - it took me two weeks. That became the last time I tried to do it by hand. CATSAI relies on two key strategies: 1. Training a machine learning model to recognize single quantum dot features. 2. Leveraging reliable data on where these single dots are located in voltage space to narrow down the search for double quantum dots. This work wouldn’t have been possible without the support of our co-authors and collaborators at IST Austria and the University of Basel. Special thanks to Natalia Ares, who supervised my PhD research and provided invaluable guidance and support throughout this project. I’m also grateful for the opportunity she gave me to work with such an amazing team and technology. Interested in learning more? You can read the full paper here: https://lnkd.in/e7Vz8We9 The possibilities ahead are vast, and I’m eager to see where AI software for semiconductor quantum devices takes us next!

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.

We just released the world's first paper on Quantum Agentic AI, focusing on the new, LLM-driven notion of agentic AI that currently transforms how we build autonomous agents! Our team including Prof Bill Buchanan OBE FRSE, Dr. Mark Tehrani, Muhammad Shahbaz Khan and Siddhant Dutta dove deep into the intersection of quantum computing and the new, LLM-driven notion of agentic AI—the concept of autonomous agents that leverage large language models to plan, decide, and act intelligently. Key highlights of our work are: ● The first formal definition of Quantum Agents based on LLM-inspired agentic principles ● New architectures that tightly integrate quantum processors with agent-based reasoning ● Three working prototypes: from Grover-based decision-making to adaptive quantum encryption ● Use cases spanning quantum-enhanced edge AI, chemistry, defense, and hybrid optimization What is the amazing and relevant big thing? Agentic AI is redefining autonomy and decision-making. By bringing quantum computing into the loop, we unlock entirely new horizons—systems that combine the best of classical and quantum intelligence. We’d love to hear your thoughts: Where do you see the biggest impact of Quantum Agents? What real-world problems could they solve today? Wir haben das weltweit erste Paper zu Quantum Agentic AI veröffentlicht, und zwar mit einem Fokus auf den neuen, von LLMs geprägten Agentic-Begriff, der aktuell die Entwicklung von autonomen Agenten neu definiert! Unser Team hat sich an der Schnittstelle von Quantencomputing und dem neuen, von LLMs geprägten Agentic-Begriff positioniert, also Agenten, die mit großen Sprachmodellen eigenständig planen, entscheiden und handeln. Highlights aus unserer Arbeit: ● Die erste formale Definition von Quantum Agents, basierend auf LLM-inspirierten agentischen Prinzipien ● Neue Architekturen, die Quantenprozessoren nahtlos mit Agenten-Logik verknüpfen ● Drei funktionierende Prototypen: vom Grover-basierten Entscheidungsagenten bis zur adaptiven Quantum Image Encryption ● Anwendungsfelder von Quanten-Edge-AI über Chemie bis Verteidigung und hybride Optimierung Was ist das Geniale daran? KI-Agenten verändern derzeit, wie wir über Autonomie und Entscheidungsfindung denken. Mit Quantum Agents kombinieren wir diese neue Form von Intelligenz mit den Fähigkeiten des Quantencomputings – für Anwendungen, die bisher undenkbar waren. Deine Meinung interessiert uns: Wo siehst du die größten Potenziale für Quantum Agents? Welche Probleme könnten Quantum Agents schon heute lösen? #QuantumAI #AgenticAI #QuantumComputing #LLM #AIResearch #Innovation #FutureOfWork #QuantumAgents