Selected work

Representative Papers

Filtered Spectral Projection for Quantum Principal Component Analysis

Mar 13, 2026

Traditional quantum principal component analysis (qPCA) explicitly estimates eigenvalues and eigenvectors, yet many applications only require projecting data onto the principal spectral subspace. This work proposes the Filtered Spectral Projection Algorithm (FSPA), which forgoes explicit eigenvalue estimation and instead centers on direct spectral projection to preserve dominant spectral structure while amplifying initial state overlap. FSPA remains robust in scenarios with small spectral gaps or near-degeneracies without requiring artificial symmetry-breaking perturbations. Leveraging the equivalence among amplitude encoding, density matrices, and covariance matrices, along with eigenvalue interlacing bounds, the method demonstrates stable projection quality and downstream task performance on benchmark datasets such as Breast Cancer Wisconsin and handwritten digits, indicating that spectral projection alone suffices for most qPCA applications.

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Periodic Materials Generation using Text-Guided Joint Diffusion Model

Mar 01, 2025

Existing equivariant diffusion models struggle to jointly model atomic types, fractional coordinates, and lattice parameters within a unified end-to-end framework, and cannot incorporate user-specified semantic constraints (e.g., “high electrical conductivity” or “thermal stability”) expressed in natural language. This work introduces TGDMat, the first text-guided, periodic E(3)-equivariant joint diffusion model for end-to-end generation of 3D periodic crystal structures conditioned on expert textual prompts. TGDMat innovatively integrates a periodic E(3)-equivariant graph neural network, a multivariate joint denoising mechanism, and cross-modal text–structure conditional modeling. Experiments demonstrate that TGDMat consistently outperforms state-of-the-art methods on both structure prediction and inverse design tasks; achieves optimal performance with single-sample sampling; significantly reduces computational overhead; and enhances the physical plausibility and chemical fidelity of generated crystals.

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Recent publications

Latest Papers

Filtered Spectral Projection for Quantum Principal Component Analysis

Mar 13, 2026

Traditional quantum principal component analysis (qPCA) explicitly estimates eigenvalues and eigenvectors, yet many applications only require projecting data onto the principal spectral subspace. This work proposes the Filtered Spectral Projection Algorithm (FSPA), which forgoes explicit eigenvalue estimation and instead centers on direct spectral projection to preserve dominant spectral structure while amplifying initial state overlap. FSPA remains robust in scenarios with small spectral gaps or near-degeneracies without requiring artificial symmetry-breaking perturbations. Leveraging the equivalence among amplitude encoding, density matrices, and covariance matrices, along with eigenvalue interlacing bounds, the method demonstrates stable projection quality and downstream task performance on benchmark datasets such as Breast Cancer Wisconsin and handwritten digits, indicating that spectral projection alone suffices for most qPCA applications.

0 citationsRead paper

Periodic Materials Generation using Text-Guided Joint Diffusion Model

Mar 01, 2025

Existing equivariant diffusion models struggle to jointly model atomic types, fractional coordinates, and lattice parameters within a unified end-to-end framework, and cannot incorporate user-specified semantic constraints (e.g., “high electrical conductivity” or “thermal stability”) expressed in natural language. This work introduces TGDMat, the first text-guided, periodic E(3)-equivariant joint diffusion model for end-to-end generation of 3D periodic crystal structures conditioned on expert textual prompts. TGDMat innovatively integrates a periodic E(3)-equivariant graph neural network, a multivariate joint denoising mechanism, and cross-modal text–structure conditional modeling. Experiments demonstrate that TGDMat consistently outperforms state-of-the-art methods on both structure prediction and inverse design tasks; achieves optimal performance with single-sample sampling; significantly reduces computational overhead; and enhances the physical plausibility and chemical fidelity of generated crystals.

0 citationsRead paper