Advanced Scientific Computing Technologies

Lockheed and IBM Use Quantum Computing to Solve Chemistry Puzzle Once Thought Impossible Introduction: Cracking a Chemical Code with Quantum Power In a breakthrough for quantum chemistry, Lockheed Martin and IBM have successfully used quantum computing to model the complex electronic structure of an “open-shell” molecule—a challenge that has defied classical computing for years. This marks the first application of the sample-based quantum diagonalization (SQD) method to such systems and signals a significant advance in the practical application of quantum computing for scientific research. Key Highlights from the Collaboration • The Molecule: Methylene (CH₂): • Methylene is an open-shell molecule, meaning it has unpaired electrons that lead to complex quantum behavior. • These molecules are notoriously difficult to simulate accurately because electron correlations create exponentially growing complexity for classical algorithms. • The Innovation: Sample-Based Quantum Diagonalization (SQD): • The team used IBM’s quantum processor to implement SQD for the first time in an open-shell system. • SQD is a hybrid algorithm that leverages quantum sampling to solve eigenvalue problems in quantum chemistry, reducing computational burdens. • Why Classical Methods Fall Short: • Traditional high-performance computing (HPC) platforms struggle with electron correlation in multi-electron systems. • Approximation techniques become prohibitively expensive as system size increases, especially for reactive or radical species like methylene. • Quantum Advantage in Practice: • Quantum processors can represent electron configurations using entangled qubits, offering more scalable solutions. • By simulating the electronic structure directly, quantum methods could help scientists design new materials, catalysts, and pharmaceuticals faster and more efficiently. Why It Matters: Pushing Past the Limits of Classical Chemistry • Industrial and Scientific Impact: • Simulating open-shell systems is vital for battery design, combustion processes, and metalloprotein modeling. • The success of SQD opens the door to accurate modeling of previously inaccessible molecules, potentially accelerating innovations in energy, health, and aerospace. • Defense and Aerospace Relevance: • Lockheed Martin’s involvement reflects strategic interest in applying quantum computing to defense-grade materials and mission-critical chemistry. • Quantum Chemistry as a Flagship Use Case: • This achievement underscores how quantum computing is beginning to deliver real results in scientific domains where classical methods hit their ceiling. • As quantum hardware improves, the number of solvable molecular systems will expand exponentially. Quantum computing just helped humanity take a critical step into the chemical unknown, proving its value not just in theory—but in practice. Keith King https://lnkd.in/gHPvUttw

How do materials fail, and how can we design stronger, tougher, and more resilient ones? Published in #PNAS, our physics-aware AI model integrates advanced reasoning, rational thinking, and strategic planning capabilities models with the ability to write and execute code, perform atomistic simulations to solicit new physics data from “first principles”, and conduct visual analysis of graphed results and molecular mechanisms. By employing a multiagent strategy, these capabilities are combined into an intelligent system designed to solve complex scientific analysis and design tasks, as applied here to alloy design and discovery. This is significant because our model overcomes the limitations of traditional data-driven approaches by integrating diverse AI capabilities—reasoning, simulations, and multimodal analysis—into a collaborative system, enabling autonomous, adaptive, and efficient solutions to complex, multiobjective materials design problems that were previously slow, expert-dependent, and domain-specific. Wonderful work by my postdoc Alireza Ghafarollahi! Background: The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Our model overcomes these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of LLMs and the dynamic collaboration among AI agents with expertise in various domains, incl. knowledge retrieval, multimodal data integration, physics-based simulations, and comprehensive results analysis across modalities. The concerted effort of the multiagent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. We demonstrate accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of alloys. Paper: https://lnkd.in/enusweMf Code: https://lnkd.in/eWv2eKwS MIT Schwarzman College of Computing MIT Civil and Environmental Engineering MIT Department of Mechanical Engineering (MechE) MIT Industrial Liaison Program MIT School of Engineering

The last two days have seen two extremely interesting breakthroughs announced in quantum computing. There is a long path ahead, but these both point to the potential for dramatically upscaling ambitions for what's possible in relatively short timeframes. The most prominent advance was Microsoft's announcement of Majorana 1, a chip powered by "topological qubits" using a new material. This enables hardware-protected qubits that are more stable and fault-tolerant. The chip currently contains 8 topologic qubits, but it is designed to house one million. This is many orders of dimension larger than current systems. DARPA has selected the system for its utility-scale quantum computing program. Microsoft believes they can create a fault-tolerant quantum computer prototype in years. The other breakthrough is extraordinary: quantum gate teleportation, linking two quantum processes using quantum teleportation. Instead of packing millions of qubits into a single machine—which is exceptionally challenging—this approach allows smaller quantum devices to be connected via optical fibers, working together as one system. Oxford University researchers proved that distributed quantum computing can perform powerful calculations more efficiently than classical systems. This could not only create a pathway to workable quantum computers, but also a quantum internet, enabling ultra-secure communication and advanced computational capabilities. It certainly seems that the pace of scientific progress is increasing. Some of the applications - such as in quantum computing - could have massive implications, including in turn accelerating science across domains.

AMD, UNSW Sydney & Pawsey: Redefining Real-Time Genomics with Slorado A major milestone for open science and high-performance genomics. AMD, UNSW Sydney, and the Pawsey Supercomputing Research Centre have introduced Slorado — the world’s first fully open-source, real-time nanopore DNA basecaller designed for AMD GPUs and powered by the ROCm open software platform. This breakthrough removes long-standing vendor lock-in and dramatically accelerates genomic workflows, empowering researchers with speed, scale, and flexibility. 🔬 What Slorado Enables + Fully open-source basecalling pipeline for nanopore sequencing + Runs on AMD GPUs via ROCm and supports hybrid GPU environments + Scales across multi-GPU and HPC infrastructures + Delivers performance parity with proprietary alternatives while improving accessibility ⚡ Performance Highlights on Pawsey’s Setonix Supercomputer Powered by AMD Instinct GPUs: + Full human genome decoded in: + 2.3 hours on MI250X GPUs + Just 0.8 hours on next-gen MI300X GPUs + High-accuracy models (HAC & SUP) also show significant acceleration without compromising data quality This level of performance transforms what once took days into hours — or even minutes — enabling faster research cycles, real-time pathogen surveillance, and scalable population genomics. 🌍 Why This Matters ✅ Democratizes access to high-performance genomics ✅ Accelerates discovery and clinical research ✅ Strengthens reproducibility through open-source transparency ✅ Expands AMD’s role as a trusted platform for scientific computing and AI ✅ Bridges HPC, AI, and bioinformatics into a unified ecosystem Slorado is more than a tool — it’s a signal of where the future of genomics is heading: open, accelerated, and accessible at global scale. AMD continues to push the boundaries of what’s possible in scientific computing – from AI to genomics and beyond. 🔗 Explore more: https://lnkd.in/ghSRHX7S #AMD #Genomics #OpenScience #HPC #AIinHealthcare #ROCm #InstinctGPUs #Supercomputing #Innovation #Bioinformatics #FutureOfScience #AMDBrandAmbassador

🔗✨ 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

✅ AI, sovereign supercomputing and translational immunology are converging to reshape what is scientifically possible, with foundation models moving from pattern recognition to hypothesis generation and adaptive discovery. 🚀 In the UK, we are applying this power to cancer vaccines, training immunology-specific AI models on deeply curated neoantigen and immune response data, and linking prediction to biological validation to make vaccines safer, more precise and more effective. Follow us The Cancer AI Scientist Project 🌍 Globally, Europe’s Frontier AI Grand Challenge, major sovereign compute investments across India and the Middle East, China’s National Supercomputing Network, and the UK’s new National Compute Resources signal a decisive shift toward AI-enabled scientific infrastructure. 🇬🇧 Our UK Cancer Vaccine AI Scientist programme continues to advance with a validated baseline model, a global supercomputing review under submission, and strong engagement from researchers, funders and patient leaders committed to accelerating discovery for people affected by cancer.

Quantum-centric supercomputing is a new architecture where both a classical and quantum computer are used together to investigate a computation problem. Sample-based Quantum Diagonalization (SQD) has emerged as one of the leading algorithm for this architecture and it allows the simulation of the electronic structure. It has been used to look at electronic structure of iron sulfides (https://lnkd.in/eK8jW-Wp) and water and methane dimers (https://lnkd.in/epgUJeD8) and in this work (https://lnkd.in/eqh8J96M) our team working with Lockheed Martin have explored how SQD can be used to study molecular dissociation for both open-shell ground states and closed-shell excited states across different symmetry sectors. The study uses a CH2 molecular system, which is relevant for both interstellar and combustion chemistry. The circuits used are LUCJ ansatz and are executed on quantum hardware at a scale of 52 qubits and 3000 two-qubit gates. The results for the CH2 singlet state showed close alignment with Selected Configuration Interaction (SCI) calculations, with deviations of only a few mEh, while triplet state results also maintained reasonable accuracy within a few mEh at equilibrium. This work also marks the first SQD analysis of quantum phase transitions resulting from level crossings, expanding SQD’s applicability to new quantum phenomena. While there is still a lot of fundamental research to be done, given these results we can see a future in modeling larger radicals, transient species, and complex combustion reactions which will have Implications to the aerospace industry and beyond. If you want to get started with SQD check out https://lnkd.in/e6TuS5AZ.

I am pleased to share our new publication in Computer Methods in Applied Mechanics and Engineering: “Physics-Informed Latent Neural Operator for Real-Time Predictions of Time-Dependent Parametric PDEs.” This work introduces PI-Latent-NO, a physics-informed latent neural operator framework that achieves real-time, scalable, and data-efficient prediction of high-dimensional PDE systems. A key contribution of this architecture is its inherent separability in space and time, which leads to near-constantruntime and memory usage, even as the spatial discretization grows by orders of magnitude. As shown in our scalability studies, while conventional physics-informed neural operators (PI-Vanilla-NO) experience steep increases in cost (often becoming intractable), PI-Latent-NO maintains essentially flat computational complexity. Full article: https://lnkd.in/eFp8bg55 This work was made possible through a highly rewarding collaboration with Sharmila Karumuri, who conducted this research as a postdoctoral fellow under me and Lori Graham-Brady. We are also grateful for the support of the U.S. Department of Energy (DOE), whose funding enabled this effort. we are excited about the broader implications for real-time scientific computing and operator learning. #MachineLearning #ScientificML #NeuralOperators #PhysicsInformedLearning #ScalableML #PDEs #ComputationalMechanics #SurrogateModeling #ScientificComputing