Classiq and Hatch Validate Hybrid Quantum-Classical Chemistry Pipeline on AWS Infrastructure
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Classiq and Hatch Validate Hybrid Quantum-Classical Chemistry Pipeline on AWS Infrastructure Software developer Classiq and innovation center Hatch have completed a computational chemistry proof-of-concept (PoC) to estimate molecular binding energy via Amazon Web Services (AWS) and Amazon Braket. Executed through Hatch’s Dimension X open innovation challenge for Singapore’s Home Team and aligned with Singapore’s National Quantum Strategy, the project establishes a functional pipeline for analyzing how small molecules interact with protein target structures. By pairing AWS high-performance classical computing nodes with Classiq’s quantum software platform, the workflow demonstrates an enterprise model for calculating molecular recognition indicators before committing to laboratory testing. Wavefunction-in-DFT Orbit Embedding and Active Space Selection Calculating molecular binding energy requires determining three independent energy profiles: the protein-ligand complex, the isolated binding pocket, and the free ligand. Because complete quantum simulations of protein environments containing over 100 atoms are limited by physical qubit availability, the project implemented a projection-based Wavefunction-in-DFT (WF-in-DFT) embedding framework. This method partitions the molecular system into a chemically active fragment and a surrounding environment: The Surrounding Environment: Handled classically via Density Functional Theory (DFT) loop operations and Electronic Repulsion Integrals (ERI) calculations. This layer captures structural electronic influence without consuming quantum memory.
The Active Fragment: Reduced from the dense protein substrate into a tractably isolated active space of roughly 10 to 14 spatial orbitals. This isolated region, where electronic correlation is densest, is mapped to a qubit Hamiltonian via the Jordan-Wigner transformation. Automated UCC Circuit Synthesis and Optimization The compiled qubit Hamiltonian is processed through a Variational Quantum Eigensolver (VQE) running on the Classiq platform. Rather than requiring engineers to draft gate-by-gate code instructions, the framework utilizes an automated synthesis engine. Researchers define the chemical inputs at a high level—specifying the fermionic Hamiltonian parameters, electron counts, and the targeted ansatz model—and the compiler automatically applies qubit tapering symmetry reductions to compress the physical register size. The compiler generates an optimized Unitary Coupled Cluster (UCC) ansatz circuit implemented via Suzuki-Trotter numerical decomposition. During hybrid execution, the Classiq platform monitors Hamiltonian expectation values, passing parameter updates to a classical COBYLA optimizer loop until the active-space ground-state energy converges.
Parallelized Batch Execution on Amazon Compute Fleets To handle the polynomial processing overhead (O(N4)) characteristic of large-scale DFT data steps, the classical pipeline was parallelized across an Amazon EC2 c6i.16xlarge compute instance featuring 64 vCPUs and 128 GB of memory. The independent matrix calculations for the complex, pocket, and ligand components are mapped directly to AWS Batch and checkpointed onto Amazon EBS and Amazon S3 storage volumes. This distributed architecture enables multi-variable screening campaigns, allowing dozens of distinct ligand configurations to be evaluated simultaneously while constraining the total system runtime to the speed of the single slowest processing task. While validated on cloud-based classical emulators, the high-level Classiq compiler model is designed to transition directly onto quantum hardware providers by routing the backend flag to Amazon Braket-managed processors. The technical software implementation guidelines, mathematical embedding formulas, and algorithmic validation data can be reviewed in the full text available on the AWS Quantum Technologies Blog here, with corporate strategic summaries hosted via the Classiq Insights Portal here. June 24, 2026 Mohamed Abdel-Kareem2026-06-24T18:13:37-07:00 Leave A Comment Cancel replyComment Type in the text displayed above Δ This site uses Akismet to reduce spam. Learn how your comment data is processed.