PROBLEMS
The dimensionality of quantum chemistry applications often grows exponentially, making large-scale simulations computationally challenging. This complexity hinders the analysis and classification of polymers based on specific features, a task crucial for various scientific and industrial applications.
SOLUTIONS
Quandela and Alysophil have developed a hybrid classical-quantum algorithm to cluster polymers given specific features, with the goal of tackle large-scale simulations. We use a pre-trained classical neural network to extract essential features of the data. These features are analyzed by a quantum neural network that classifies the polymers. This transfer learning process leverages the strengths of both classical and quantum computing, creating a powerful hybrid quantum-classical neural network.
BENEFITS
Preliminary results agree with classical simulations, paving the way for further developments of quantum algorithms. The successful implementation of this technique could significantly accelerate research in materials science, potentially leading to innovations in polymer design and applications across various industries.