Physics-informed ML for battery voltage prediction

End-to-end machine-learning pipeline for predicting average voltages of alkali-ion battery cathode materials, combining DFT-derived physicochemical descriptors with neural network models.

• Input: DFT-derived structural and atomic descriptors
• Output: Voltage predictions with model interpretability
• Audience: Battery materials researchers

Quantum Computing Workflows and Benchmarking

Reproducible quantum computing workflows for quantum chemistry simulations, focusing on reliable ground- and excited-state calculations for realistic molecular systems. The work emphasizes benchmarking, validation, and method development for problems relevant to areas such as energy storage, molecular design, and drug discovery, with particular attention to accuracy, scalability, and computational efficiency.

• Input: Molecular Hamiltonians
• Output: Ground and excited-state energies
• Audience: Quantum chemistry researchers

High-throughput DFT analysis and automation toolkit

Modular toolkit for automating density-functional theory workflows, including output parsing, convergence diagnostics, and generation of publication-ready figures.

Designed to support large-scale studies and to serve as a reliable data-generation backend for machine-learning and quantum simulation tasks.

• Input: Raw DFT and AIMD calculation outputs
• Output: Curated datasets and reproducible analyses
• Audience: Computational materials researchers

Spectral filtering and subspace quantum algorithms

Development of quantum algorithms for resolving eigenvalues with exponentially small gaps and low spectral weight using filtering-enhanced phase estimation and quantum subspace methods.

This work targets algorithmic scalability and noise robustness, with applications to molecular excitation spectra and correlated quantum systems.

• Input: Hamiltonians with dense or near-degenerate spectra
• Output: Isolated eigenvalues and reduced subspaces
• Audience: Quantum algorithms and theory researchers

Exponential Circuit Depth Compression via Spectral Gap Amplification

Spectral preconditioning framework for reducing quantum phase estimation (QPE) precision overhead by amplifying effective boundary gaps prior to phase estimation.

Demonstrates exponential circuit-depth compression proportional to \(2^{\Delta m}\), with verified principal-subspace preservation and hardware-level validation across molecular, classical, and near-degenerate synthetic systems.

• Input: Hermitian operator \(\rho\) (molecular density matrices or covariance matrices)
• Output: Reduced QPE precision (\(m \to m_f\)) and exponential depth compression
• Audience: Quantum algorithms, QPE, and quantum linear algebra researchers

Quantum computing simulation platform

A research initiative building a cloud-based platform for running VQE, qEOM, and SQD workflows on real molecular systems directly from a browser, paired with a growing benchmark database of results validated against classical electronic-structure methods.

The goal is to turn the reproducible quantum-simulation workflows behind my published research into shared infrastructure that any researcher can use, rather than one-off scripts tied to a single paper.

• Input: Molecular structures and Hamiltonians
• Output: Benchmarked ground- and excited-state energies via a browser-based interface
• Audience: Quantum chemistry and materials researchers