Quantum algorithm for enhanced vibrational-electronic spectra prediction
A hybrid quantum-classical quantum algorithm using quantum machine learning to predict vibrational-electronic spectra with improved efficiency and precision. The approach targets applications in catalysis and materials science by refining spectral predictions, reducing noise, and learning complex patterns. It employs VQE and QPE with error mitigation on NISQ devices and is designed for implementation on cloud-based quantum platforms.
Overview
A quantum algorithm designed to predict vibrational-electronic spectra of molecules with high efficiency and precision. The method leverages hybrid quantum-classical computation and quantum machine learning (QML) to aim for performance improvements over classical computational methods. The intended impact includes improved modeling of catalytic processes and material properties.
Technical specifications
The algorithm uses Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE) to address molecular spectra by representing molecular wavefunctions in high-dimensional Hilbert space. Quantum Machine Learning (QML) techniques are integrated to refine spectral predictions, reduce noise, and recognize complex patterns. The implementation is planned for cloud-based quantum platforms such as Google Colab, Classiq, and Qbraid to support accessibility without additional infrastructure costs. Error mitigation strategies will be applied to enhance performance on NISQ devices.
Technology readiness level
The project is currently at Technology Readiness Level 2. The development plan spans 18 months, including phases for problem definition, algorithm design, implementation, validation against datasets and experimental data, and final testing.
About Nazarbayev University
Nazarbayev University is an autonomous public research university founded in 2010, with a broad, internationally oriented academic profile and more than 400 researchers and faculty. Its Astana campus is anchored by an 18,400-square-meter Interdisciplinary Research Building with 120 laboratories, major instrumentation, and shared core facilities that support external collaborators and industry users. Centralized research administration, international partnerships, industry-academia programs, and an innovation ecosystem support contract research, startup development, intellectual-property protection, licensing, and commercialization. Research is supported by competitive internal grants, Kazakhstan’s Ministry of Science and Higher Education and other national ministries, plus international funders such as the European Commission, World Bank, NIH, NASA, and NSF. Research administration provides pre- and post-award support for externally funded collaboration and technology translation.
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