Classiq and AWS Power Quantum-Classical Chemistry Innovation in Singapore with Hatch

This article describes a quantum-classical hybrid pipeline developed by Classiq for predicting ligand-protein binding energy, a key step in drug discovery and biochemical research.

• Combines high-performance DFT calculations on AWS EC2 with variational quantum eigensolver (VQE) for improved accuracy
• Uses projection-based Wavefunction-in-DFT embedding to partition molecules into quantum-treated active fragments and classically-treated environments
• Classiq platform synthesizes optimized quantum circuits from high-level chemistry models without manual gate-level programming
• Parallelizes independent calculations using AWS Batch, with checkpoints stored in Amazon S3
• Validated on benchmark systems (LiH⁺, H₂O) with quantum results matching or exceeding classical DFT precision
• Architecture scales with quantum hardware maturity and can target Amazon Braket for hardware execution

The hybrid approach enables accurate binding energy estimation for drug discovery by leveraging quantum computing's strength in modeling electronic correlations while maintaining classical scalability for large protein systems.