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This research presents a novel quantum algorithm for the Non-Uniform Quantum Hermite Transform, achieving near-linear gate complexity through adaptive sampling techniques. By converting uniform-grid samples into weighted Hermite-root samples with polylogarithmic width, the method offers significant efficiency gains for simulating harmonic oscillator dynamics and solving partial differential equations on quantum hardware. The approach maintains rigorous error bounds across arbitrary superpositions, marking a substantive advance in practical quantum simulation protocols.
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Efficient Non-Uniform Quantum Hermite Transform through Adaptive Sampling

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arXiv:2609.20739v1 Announce Type: new Abstract: On the span of the first $N$ oscillator modes, Gauss--Hermite quadrature gives an exact change of basis between mode coefficients and $N$ weighted position space samples. We implement this transform with $O(N\operatorname{polylog}(N,1/\varepsilon))$ logical gates and polylogarithmic quantum width. The operator-error bound $\varepsilon$ holds on arbitrary superpositions and includes all auxiliary registers. The construction uses signed averages on adaptive windows to convert uniform-grid samples into weighted Hermite-root samples. Their varying widths control the amplification cost, giving the near-linear bound.

Abstract:On the span of the first $N$ oscillator modes, Gauss--Hermite quadrature gives an exact change of basis between mode coefficients and $N$ weighted position space samples. We implement this transform with $O(N\operatorname{polylog}(N,1/\varepsilon))$ logical gates and polylogarithmic quantum width. The operator-error bound $\varepsilon$ holds on arbitrary superpositions and includes all auxiliary registers. The construction uses signed averages on adaptive windows to convert uniform-grid samples into weighted Hermite-root samples. Their varying widths control the amplification cost, giving the near-linear bound.

Quantum Physics (quant-ph); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET)

arXiv:2609.20739 [quant-ph]

(or arXiv:2609.20739v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.20739 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

cat_coefficients.npy

coherent_coefficients.npy

frft_density.npy

frft_evolved_coefficients.npy

hermite_demonstrations.py

hermite_demonstrations_validation.json

noise_populations.csv

noise_simulation.py

noise_validation.json

qho_classical_positions.npy

qho_evolved_coefficients.npy

requirements.txt

test_tail_squared.py

(14 additional files not shown) You must enabled JavaScript to view entire file list.

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