Quantum for Materials Science
Quantum computing and software for materials science, chemistry, and discovery.
Design With Atoms
The properties that make materials useful often emerge from quantum interactions that are difficult to model classically. Infleqtion combines neutral atom computing, logical qubit techniques, and hardware-aware software to investigate these systems at their native scale. In 2024, Infleqtion and NVIDIA demonstrated a materials science application using logical qubits and CUDA-Q. The Anderson impurity model workload produced six times lower error with logical qubits than the corresponding physical-qubit approach. Building from that result, Infleqtion is developing workflows for electronic structure and strongly correlated materials relevant to batteries, superconductors, rare-earth magnets, catalysts, and energy technologies.
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Featured Video
Logical Qubits Lower Error in a Materials Model
Infleqtion and NVIDIA used logical qubits and CUDA-Q to run an Anderson impurity model, achieving six times lower error than the physical-qubit approach.
- Materials science workload
- Logical qubit error reduction
Work at the Quantum Scale
Neutral atom processors create a flexible environment for representing and controlling quantum systems, while logical qubits suppress errors in deeper application circuits.
The materials demonstration modeled a magnetic impurity interacting with surrounding electrons, a foundational problem in condensed-matter physics.
Optimize the Full Stack
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Hardware-aware compilation translates materials algorithms into operations tuned for the target processor.
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Logical qubit tools help teams construct, analyze, and select error-correcting codes for application needs.
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Hybrid QPU-GPU workflows connect quantum experiments with familiar high-performance computing environments.
Quantum Benefits
Apply Quantum Models to Materials Experiments
Connect materials questions to algorithms, optimized circuits, logical qubits, and hybrid compute workflows.
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Correlated Materials
Investigate models where many interacting particles create behavior that is difficult to capture with classical approximations.
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Lower-Error Circuits
Use logical qubit methods to suppress errors and extend the depth and usefulness of materials simulations.
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Faster Iteration
Optimize circuits for the target processor and compare approaches through one hardware-aware software workflow.
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Hybrid Integration
Connect quantum processors with GPU and high-performance computing environments for practical research workflows.
Case Studies
Quantum Materials Science in Action
Advancing chemistry and materials science with qubits.
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Federal Funding for Innovation
Infleqtion was selected to receive federal funding from DOE’s ARPA-E to advance quantum computing for chemistry and materials science.
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Logical Qubit Materials Design
Infleqtion delivered a material design application using logical qubits and NVIDIA CUDA-Q, with six times lower error on an Anderson impurity model.
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Model to Execution
Build materials workflows across quantum hardware and HPC.
Frequently Asked Questions
Materials are governed by quantum interactions that become extremely difficult to simulate as system size and complexity grow. Quantum computers can represent some of those interactions more naturally, creating a path to better modeling of electronic structure, material properties, and candidate designs. Potential applications include batteries, catalysts, superconductors, semiconductors, alloys, and low-carbon materials.
Infleqtion and NVIDIA demonstrated a materials-science application using logical qubits, Superstaq, and NVIDIA CUDA-Q to study the Anderson impurity model. The 2024 work applied quantum error detection to an application-level materials problem. It was a research milestone that showed end-to-end logical qubit operation, not a claim of commercial quantum advantage over all classical methods.
Physical qubits are prone to noise and loss, which can corrupt a long or complex calculation. Logical qubits encode information across multiple physical qubits so errors can be detected or corrected. Materials simulations may ultimately require deep, precise circuits, making logical qubit performance and efficient error correction central to useful results.
Priority areas include energy-storage materials, catalysts, superconductors, photovoltaic materials, semiconductors, high-performance alloys, and sustainable materials. Quantum computing may help researchers screen candidates or model difficult quantum behavior before costly synthesis and testing. It complements, rather than eliminates, classical simulation, AI, laboratory experiments, and manufacturing validation.
Infleqtion combines neutral atom hardware, logical qubit methods, Superstaq compilation, GPU integration, resource estimation, and application co-design. A project typically starts by defining a material model and business or scientific objective, then determining what can be tested classically, in simulation, or on current quantum hardware. This staged approach builds evidence while identifying the resources needed for future advantage.
News & Insights
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Bring us a materials problem worth solving.