Encoding proteins as quantum states with approximate quantum state preparation by iterated sparse state preparation
Year: 2025
Type: article
Abstract: Quantum computing holds transformative potential for various domains including cheminformatics through advancements in quantum algorithms. The key to realizing improvements with quantum algorithms in cheminformatics is encoding chemical data like proteins as quantum states with quantum state preparation. In this work, we propose a computational framework to encode proteins as quantum states for efficient downstream quantum processing. Protein data representations are encoded as multi-qubit quantum states with iterative quantum sparse state preparation guided by the classical heuristic search method for optimal gate sequence identification. The validity and efficiency of the proposed method is demonstrated with various computational experiments to encode uniform random states as well as proteins. Several performance comparisons against the baselines of exact and variational state preparation methods, the proposed approach is able to encode proteins with 25% fewer controlled-NOT gates while performing orders of magnitude faster than the variational method.
Source: Quantum Science and Technology
Institution: Cornell University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,895
OpenAlex ID: W4407250243