A projection-based model reduction enables exponential state-space reduction for constrained quantum optimization applied to random 3-SAT and agent coordination on graphs.
Journal of Physics A: Mathematical and Theoretical41(49), 493001 (2008)
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Krylov subspace methods efficiently describe quantum evolution, operator growth, and chaos in many-body systems, with metrics like Krylov complexity and applications in open systems, QFT, and quantum computing.
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Constrained Quantum Optimization meets Model Reduction
A projection-based model reduction enables exponential state-space reduction for constrained quantum optimization applied to random 3-SAT and agent coordination on graphs.
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Quantum Dynamics in Krylov Space: Methods and Applications
Krylov subspace methods efficiently describe quantum evolution, operator growth, and chaos in many-body systems, with metrics like Krylov complexity and applications in open systems, QFT, and quantum computing.