Quantum neural networks can classify shock and turbulent flow solutions encoded as quantum states, with accuracy strongly dependent on Fourier versus real-space basis choice.
Quantum Multigrid Algorithm for Finite Element Problems
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Quantum linear system algorithms (QLSAs) can provide exponential speedups for the solution of linear systems, but the growth of the condition number for finite element problems can eliminate the exponential speedup. QLSAs are also incapable of using an initial guess of a solution to improve upon it. To circumvent these issues, we present a Quantum Multigrid Algorithm (qMG) for the iterative solution of linear systems by applying the sequence of multigrid operations on a quantum state. Given an initial guess with error e_0, qMG can produce a vector encoding the entire sequence of multigrid iterates with the final iterate having a relative error e'=e/e_0, as a subspace of the final quantum state, with exponential advantage in O( poly log (N/e') ) time using O( poly log (N/e') ) qubits. Although extracting the final iterate from the sequence is efficient, extracting the sequence of iterates from the final quantum state can be inefficient. We provide an analysis of the complexity of the method along with numerical analysis.
fields
quant-ph 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning
Quantum neural networks can classify shock and turbulent flow solutions encoded as quantum states, with accuracy strongly dependent on Fourier versus real-space basis choice.