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
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 3years
2026 3roles
background 1polarities
background 1representative citing papers
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.
Hamiltonian-encoded quantum reservoir computing achieves ~98% MNIST accuracy with 5-6 qubits on both analog and digital platforms, with dissipation constructively suppressing scrambling at long times.
citing papers explorer
-
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)).
-
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.
-
Robust Quantum Learning through Hamiltonian Reservoir Computing
Hamiltonian-encoded quantum reservoir computing achieves ~98% MNIST accuracy with 5-6 qubits on both analog and digital platforms, with dissipation constructively suppressing scrambling at long times.