Quantum Computing for Material Science

Quantum computing is pushing the boundaries of chemical simulations to unprecedented accuracy! In a groundbreaking study recently published in The Journal of Chemical Theory and Computation, researchers from IBM Quantum® and Lockheed Martin demonstrated a significant milestone in quantum chemistry, the application of sample-based quantum diagonalization (SQD) techniques to accurately model "open-shell" molecules. Why is this critical? Open-shell molecules like CH₂ (methylene) have unpaired electrons, resulting in complex electronic structures that classical computational methods struggle to simulate accurately. Methylene is particularly intriguing because its high reactivity and magnetic properties significantly influence combustion processes, atmospheric chemistry, and even interstellar phenomena. By harnessing quantum computing, researchers successfully calculated CH₂’s singlet-triplet energy gap—a notoriously difficult challenge for classical approaches. This advancement paves the way for accurately predicting chemical reactivity and designing novel materials crucial for aerospace, catalysis, and sensor technologies. Quantum computing is becoming a transformative tool in real-world chemical research. Explore the full details of this landmark study below #QuantumComputing #QuantumChemistry #IBMQuantum #LockheedMartin #OpenShellMolecules #AerospaceInnovation #MaterialsScience #ChemicalSimulation

The dirty secret of Quantum Computing… Materials are the limiting factor. Everyone talks about quantum algorithms, error correction, and qubit counts. But the real killer of quantum computing isn’t software, it’s materials. Superconducting qubits don’t decohere because we lack clever code. They decohere because: – Surface oxides introduce two-level system noise. – Impurities and defects act like microscopic time bombs. – Atomic-scale disorder destroys coherence before circuits can compute anything useful. That’s why the biggest breakthroughs aren’t happening in code, they’re happening in materials labs. → Google is building qubits with ultra-clean Al/Si interfaces to suppress noise. → IBM is investing in substrate purification to push coherence times further. → Labs worldwide are chasing epitaxial aluminum films with sub-ppm impurity levels. The “quantum revolution” is being held back by dirt, literally. Until we tame materials noise, scaling qubits is just scaling errors. Quantum doesn’t need another hype cycle. It needs a materials breakthrough. #QuantumComputing #MaterialScience #GrowthAndInnovation #DeepTech

Today in Science Magazine, work from our IBM team, in collaboration with The University of Manchester, University of Oxford, ETH Zürich, EPFL and the University of Regensburg, shows the creation and simulation of a new molecule with an electronic structure that has never existed before — a half‑Möbius topology: https://lnkd.in/eFU5s9qR. The molecule was assembled using scanning probe microscopy at temperatures just above absolute zero — building it one atom at a time using STM, atom manipulation, and AFM. The electronic orbitals of this half‑Möbius molecule twist by 90 degrees with every loop around the ring, completing a full turn only after four revolutions. Why is this also important for quantum computing? This work demonstrates, for the first time, that quantum computing calculations can provide decisive scientific guidance and powerful characterization capabilities to support the discovery of new complex chemical molecules. In close collaboration with leading experimental laboratories, quantum simulations can now contribute directly to interpreting experimental observations and to guiding the design and understanding of novel molecular systems. The calculations performed in this project go well beyond the regime accessible to brute-force classical simulations, although we do not exclude the possibility that approximate classical methods could also provide valuable insights. Nevertheless, the discovery process itself benefited from quantum simulation, and we chose to employ quantum computing because it offers a natural and scalable framework for tackling problems of this kind. In particular, by comparing Dyson orbitals measured with scanning tunneling microscopy (STM) with images reconstructed from electronic structure calculations performed on a quantum computer using the SqDRIFT algorithm, we were able, for the first time, to contribute directly to the discovery and characterization of a new molecule exhibiting entirely novel electronic structure properties. paper: https://lnkd.in/esg9sHqV

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

The new manuscript from Oak Ridge National Laboratory, Cleveland Clinic, and IBM, "Quantum Computations on Fusion Blanket Molten Salts," is worth seeing for how it uses quantum computing, not just what it computes. The problem is modeling how tritium binds in FLiBe, a molten salt candidate for fusion reactor blankets. Ensuring adequate supplies of tritium has long been a barrier to realizing the promise of clean and abundant energy from fusion power plants, and solving this issue is a key objective of the U.S. Department of Energy (DOE) Genesis Mission. The electronic interactions that drive tritium's behavior in these salts are strongly correlated and hard for classical methods to capture accurately, so the team split the work. The embedded wavefunction method isolates the chemically active fragment that benefits from quantum computation. The surrounding environment stays on classical HPC. That fragment gets solved with extended sample-based quantum diagonalization (ExtSQD) on IBM Quantum systems, where the quantum processor produces correlated samples and classical compute handles the diagonalization. Fragment energies came out close to full configuration interaction benchmarks, and the team could see exactly where the remaining error sits. This is again quantum-centric supercomputing in practice, and it maps onto the reference architecture we laid out earlier this year (https://lnkd.in/g5Hp9Fce). It also follows the work Cleveland Clinic, RIKEN, and IBM published in May, where the same approach simulated a 12,635-atom protein, the largest biologically relevant system modeled with quantum hardware to date. Proteins then, fusion blanket materials now. Same pattern every time: isolate the fragment that needs quantum, keep the environment on classical, run it as one pipeline where AI, HPC, and quantum each do what they are best at. The workflow is the key thing I want to emphasize but fusion is the proving ground here. This is quantum computing contributing to a real materials problem today, and it points at how we leverage computing in general for the future. Manuscript: https://lnkd.in/gmT2Ey92 Press release: https://lnkd.in/ghpnTcRG #FusionEnergy #NuclearEnergy #Tritium #CleanEnergy #QuantumComputing #QuantumCentricSupercomputing #HPC #GenesisMission

A significant inflection point for U.S. manufacturing is here. Google's recent "verifiable quantum advantage" breakthrough isn't a distant theory—it's a present-day reality with immediate strategic implications for industry leaders. Their Willow chip executed the Quantum Echoes algorithm 13,000x faster than a top supercomputer, moving quantum from abstract science to a verifiable engineering tool for solving real-world problems. What does this mean for your business? Key takeaways from our deep-dive analysis: 🔹 Materials Science: The paradigm shifts from slow, empirical discovery to rapid, predictive design. Imagine engineering stronger, lighter alloys or more efficient catalysts in silico, slashing R&D cycles from decades to months. 🔹 Supply Chain & Logistics: Go beyond static efficiency. Quantum optimization enables dynamic, real-time resilience, allowing supply chains to adapt to disruptions instantly—a powerful competitive differentiator. 🔹 Talent Metamanagement: The most critical bottleneck isn't hardware access; it's the severe quantum skills gap. Building a quantum-ready workforce through strategic upskilling and talent management is now a core competitive necessity, not just an HR function. The race for a first-mover advantage has begun. The question for leaders is no longer if quantum will have an impact, but how they will build the strategic roadmap and talent pipeline to lead the charge. #QuantumComputing #USManufacturing #Innovation #TechStrategy #SupplyChain #FutureOfWork #MaterialsScience #Leadership

Don't let this paper go under your radar. A single molecule can now function as an individually controllable qubit with a native photonic interface. What I find especially exciting about this new work from NVision Quantum Technologies is that it brings chemistry directly into quantum engineering. For years, the conversation around quantum computing has focused on how quantum computers may eventually help chemistry and drug discovery. This work points in the opposite direction: chemistry itself may help build quantum computers. For decades, quantum technologies have largely depended on discovering the "right" physical systems in nature — defects in diamond, trapped ions, superconducting circuits, rare atomic transitions. In many of these systems, the material gives you what the material gives you, and progress comes from engineering around difficult constraints. Chemistry offers something fundamentally different: the possibility of designing quantum systems molecule by molecule. That is a profound shift. This work demonstrates a chemically engineered quantum system in which spin properties, optical behavior, and the photonic interface are embedded directly into the molecular design itself. Because these systems are designed with native photonic interfaces, they may offer a fundamentally new route toward scalable quantum architectures based on photonic interconnectivity. Coming from the world of molecular systems and computational drug discovery, I find something uniquely elegant in the possibility that chemistry may not only become one of the greatest applications of quantum computing, but also one of the ways quantum computers themselves are built. A future in which chemically engineered quantum systems are used to design new medicines would represent a remarkable convergence of chemistry, physics, computation, and biology. Highly recommend following the work coming out of NVision as this field develops. 📄 Paper: https://lnkd.in/einmV_YS 📰 Science Magazine coverage: https://lnkd.in/ewkZ4WKi #QuantumComputing #Chemistry #DrugDiscovery

Solving the many-electron Schrödinger equation with Transformers Every material property, in principle, comes from solving the many-electron Schrödinger equation. But the math is brutal: the Hilbert space grows exponentially, and even the best methods—DFT, coupled-cluster, DMRG—hit hard limits when strong electron correlation or large active spaces appear. Honghui Shang and coauthors present QiankunNet, a neural-network quantum state inspired by large language models. At its core is a Transformer wavefunction ansatz, where attention captures long-range electron correlations directly. Instead of slow Markov chains, it uses autoregressive sampling—generating uncorrelated electron configurations one by one, guided by Monte Carlo tree search. Physics-informed initialization from truncated CI keeps the model close to physical reality from the start. The result is striking: QiankunNet recovers 99.9% of FCI correlation energy for molecules up to 30 spin orbitals, handles N₂/cc-pVDZ (56 qubits, 14 e⁻) within 3.3 mHa of a DMRG reference, and even tackles the Fenton reaction with a CAS(46e,26o) active space—capturing complex multi-reference chemistry around Fe(II)/Fe(III) oxidation. Compared to previous NNQS, it is both faster (∼10× at 30 orbitals) and more accurate. This points toward a future where attention models don’t just process words, but represent quantum wavefunctions—bringing LLM-inspired architectures into the heart of quantum chemistry. Paper: https://lnkd.in/disnvEVi #QuantumChemistry #ArtificialIntelligence #MachineLearning #DeepLearning #Transformers #NeuralNetworks #QuantumPhysics #ComputationalChemistry #QuantumMaterials #AIforScience #QuantumComputing #Physics #Chemistry #SchrodingerEquation #ScientificInnovation

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.