Advancing
Materials Science
We publish foundational research to advance computational materials discovery. Our work spans quantum algorithms, GPU acceleration, and machine learning for chemistry.
Research Focus Areas
Our research spans multiple disciplines to accelerate materials discovery.
Quantum Annealing
Exploring the dynamics of quantum phase transitions to solve NP-hard optimization problems.
Superconducting Qubits
Advancing coherence times and connectivity in our Pegasus and Zephyr processor topologies.
Material Simulation
Simulating magnetic frustration and topological phases of matter on programmable quantum lattices.
Error Correction
Developing flux-based qubits for scalable, fault-tolerant gate-model quantum computing.
Publications
Selected research papers and ongoing work.
Quantum Critical Dynamics in a 5,000-Qubit Programmable Spin Glass
Published in Nature. Demonstrates that quantum annealing can simulate the dynamics of a 3D spin glass faster than classical Monte Carlo methods.
Computational Supremacy in Quantum Simulation
Published in Science. Reports the observation of a Kosterlitz-Thouless phase transition in a quantum simulator, unachievable by classical means.
Entanglement in a Quantum Annealing Processor
PublishedEvidence of entanglement across hundreds of qubits in the Advantage processor, confirming the quantum mechanical nature of the optimization process.
Reverse Quantum Annealing for Local Refinement
PublishedA novel protocol to refine classical solutions using quantum fluctuations, significantly improving solution quality for local search problems.
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