Introduces a dilation framework for quantum simulation of linear DAEs, applied to structure-preserving discretizations of unsteady Stokes flow yielding simulation cost scaling as O(h^{-2} sqrt(t)).
Challenges and opportunities in quantum machine learning.Nature computational science, 2(9):567–576
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QAP-Router models qubit routing as dynamic QAP and applies RL with a solution-aware Transformer to cut CNOT counts by 12-30% versus industry compilers on real circuit benchmarks.
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Quantum Simulation of Differential-Algebraic Equations with Applications to Unsteady Stokes Flow
Introduces a dilation framework for quantum simulation of linear DAEs, applied to structure-preserving discretizations of unsteady Stokes flow yielding simulation cost scaling as O(h^{-2} sqrt(t)).
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QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning
QAP-Router models qubit routing as dynamic QAP and applies RL with a solution-aware Transformer to cut CNOT counts by 12-30% versus industry compilers on real circuit benchmarks.