High-fidelity quantum chemical simulations often require computational accuracy that exceeds the practical capabilities of today’s quantum hardware. This technology provides a hybrid quantum-classical approach that improves the accuracy of quantum simulations by combining quantum-generated results with advanced classical processing. The method enables more precise prediction of chemical and physical properties without increasing quantum hardware requirements, helping organizations obtain higher-quality simulation results while making more efficient use of available quantum computing resources.
Description
Accurate modeling of molecular systems is critical for applications ranging from materials development to chemical process design. However, achieving the highest levels of simulation fidelity typically requires computational approaches that are difficult to implement on current quantum devices due to hardware and resource limitations.
This technology introduces a hybrid workflow that distributes computational tasks between quantum and classical platforms. A quantum computer performs the primary simulation, while a complementary classical process enhances the resulting data to recover important accuracy contributions that would otherwise require substantially greater quantum resources. By leveraging the strengths of both computing environments, the approach improves simulation fidelity while maintaining compatibility with existing quantum computing workflows.
The technology is designed to integrate with quantum chemistry and computational modeling platforms and can be applied to a broad range of molecular and materials simulations. Results demonstrate improved agreement with highly accurate reference methods, providing a practical path toward more reliable quantum-enabled simulation capabilities on current and near-term quantum computing systems.
Additional technical details of the disclosed technologies can be found here:
Analysis of fourth-, fifth-, and infinite-order triple excitations in unitary coupled cluster theory.
Benefits
- Improves simulation accuracy without increasing quantum hardware requirements
- Enhances the value of existing quantum computing workflows
- Reduces computational burden associated with highly accurate molecular simulations
- Supports more reliable prediction of chemical and physical properties
Applications and Industries
- Quantum chemistry and molecular modeling
- Materials discovery and development
- Chemical and process industries
- Pharmaceutical and life sciences research
- Quantum computing software and platform providers
Contact
To learn more about this technology, email [email protected] or call 865-574-1051.
Researchers
Contact
To learn more about this technology, email [email protected] or call 865-574-1051.