QC Ware and IonQ test hybrid quantum workflow for drug discovery

QC Ware and IonQ (NYSE: IONQ) announced a technology demonstration of a hybrid quantum-classical chemistry workflow using QC Ware's Promethium platform and IonQ's Forte trapped-ion quantum computer, accessed via Amazon Braket.

The demonstration modeled the heme active site of cytochrome P450nor, an enzyme in the cytochrome P450 superfamily involved in nitric oxide reduction. The same superfamily includes members that carry out most human drug metabolism. The workflow calculated electrostatic interaction energy within 0.5 kcal/mol, or approximately 4%, of classical benchmarks — inside the 1 kcal/mol threshold generally considered chemical accuracy. The companies said the result was more than double the accuracy of the standard classical mean-field method.

The workflow paired GPU-accelerated classical pre-processing in Promethium with quantum measurements on IonQ Forte. Promethium built and preprocessed a 115-atom model of the P450nor active site containing over 1,000 molecular orbitals, automatically isolating a 4-orbital active space mapped onto 8 qubits. IonQ Forte measured those qubits in a single basis before returning results for Promethium to compute final interaction energies classically.

"Running the same hybrid workflow on IonQ's trapped-ion architecture, following our recent demonstration on other quantum hardware, shows that Promethium's approach to combining classical and quantum computing is not tied to a single type of quantum hardware," said Dr. Kin-Joe Sham, Co-Founder and COO at QC Ware.

"QC Ware and IonQ have shown that hybrid quantum-classical workflows can predict certain binding behavior accurately enough for discovery teams to confidently rank candidates and catch toxicity risks early," said Scott Millard, Chief Business Officer at IonQ.

The demonstration was supported in part by Amazon Web Services cloud compute credits. According to the press release, Promethium's GPU-native architecture can run certain calculations up to 20 times faster than conventional CPU-based density functional theory platforms.

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