Time-dependent quantum machine learning for molecular spectral properties
This solution leverages time-dependent quantum machine learning (QML) methods to improve the accuracy and efficiency of predicting molecular spectral properties, overcoming limitations of traditional approaches like Density Functional Theory.
Overview
This innovative solution utilizes time-dependent quantum machine learning (QML) methods to predict molecular spectral properties with enhanced accuracy and efficiency. By implementing time-dependent extensions of variational quantum algorithms, this approach addresses the limitations of classical methods such as Density Functional Theory (DFT), which often rely on approximations and lack universality. The solution aims to harness the potential of quantum computing to efficiently encode and manipulate many-body quantum states, providing scalable solutions for predicting optical properties.
Technical specifications
- Quantum Machine Learning (QML): Utilizes variational algorithms like VQE to achieve computational advantages and higher accuracy.
- Time-dependent Extensions: Implements time-dependent quantum methods to access optical properties such as excitation energies and oscillator strengths.
- Hybrid Quantum-Classical Algorithms: Leverages these algorithms to extend to larger and more complex systems.
- Quantum Circuits Development: Develops circuits to represent time-evolution and excitation dynamics, optimized for noise resilience and scalability on NISQ devices.
- Collaborative Approach: Involves collaboration with stakeholders to define relevant systems and properties, ensuring practical applicability.
Technology readiness level
The technology is currently at a TRL of 3, indicating that it is in the experimental proof-of-concept stage. The solution involves testing and validating quantum circuits on small molecular systems and iteratively refining methods for practical implementation on quantum hardware.
About Multiverse Computing S.L
Multiverse Computing S.L. is a specialized software company that develops advanced quantum and quantum-inspired AI technologies. Its core products include CompactifAI, a compression technology that significantly reduces the size of Large Language Models and other AI models while maintaining performance, and the Singularity platform, which integrates quantum-inspired algorithms for complex optimization, simulation, and data analytics. By leveraging deep expertise in tensor networks and machine learning, the company creates solutions designed to run secure, efficient, and sovereign AI across diverse environments, including cloud, on-premise, and edge systems. The company is headquartered in San Sebastian, Spain, and maintains an international presence with multiple global offices.
These technologies enable customers across sectors such as finance, energy, manufacturing, and logistics to overcome constraints related to compute costs, hardware requirements, energy consumption, and latency. By allowing high-performance models to be deployed in resource-constrained settings like production lines and OT systems, Multiverse Computing helps industrial and financial partners improve operational efficiency, predictive maintenance, and real-time process automation. The company has secured significant international investment, holds numerous patents, and serves a diverse global client base that includes major organizations like the Bank of Canada, Iberdrola, and Bosch. It is widely recognized for its contributions to the quantum and AI software landscape, including being named a top global AI company by CB Insights.
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