Insider Brief
- QC Ware and IonQ demonstrated a hybrid quantum-classical chemistry workflow that calculated electrostatic interaction energy for a cytochrome P450nor model within 0.5 kcal/mol of classical benchmarks.
- The workflow combined GPU-accelerated preprocessing through QC Ware’s Promethium platform with quantum measurements on IonQ’s Forte trapped-ion system through Amazon Braket.
- The demonstration used an eight-qubit active-space model and is intended to assess how hybrid quantum chemistry workflows could support drug discovery and other molecular research.
PRESS RELEASE – QC Ware today announced a technology demonstration of a hybrid quantum-classical chemistry workflow using its Promethium® platform and IonQ‘s Forte trapped-ion quantum computer via Amazon Braket.
The demonstration modeled the heme active site of cytochrome P450nor, a nitric oxide reductase in the cytochrome P450 superfamily — the same superfamily whose monooxygenase members carry out most human drug metabolism. By pairing GPU-accelerated classical pre-processing in Promethium with quantum measurements on IonQ Forte, the workflow calculated electrostatic interaction energy within 0.5 kcal/mol (~4%) of classical benchmarks. This falls well inside the 1 kcal/mol threshold for chemical accuracy and delivers more than double the accuracy of the standard classical mean-field method. For the biopharma industry, more accurately calculating electrostatic interaction energy, a major factor in how tightly a drug candidate binds to its target, at complex metal centers such as the iron site in P450nor, could improve candidate ranking and help identify metabolic risks earlier in the drug-discovery process.
“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. “We believe this hardware-agnostic approach gives researchers flexibility as quantum computing continues to mature.”
“Every month spent advancing a drug candidate on flawed metabolic data is wasted time and mounting risk,” said Scott Millard, Chief Business Officer at IonQ. “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. We believe that’s real quantum impact on real health outcomes. I can’t wait to see what this partnership delivers.”
Technical Highlights:
- Promethium Preprocessing: Promethium built and preprocessed a complex 115-atom model of the P450nor active site containing over 1,000 molecular orbitals. It automatically isolated the strongly correlated region down to a 4-orbital active space mapped onto 8 qubits, which IonQ Forte measured in a single basis before returning results for Promethium to compute final interaction energies classically.
- Hardware Architecture: IonQ Forte’s all-to-all qubit connectivity enabled complex two-qubit entangling gates to execute as designed without the routing overhead or additional error typical of limited-connectivity architectures.
- Biopharma Impact: Accurately predicting binding energy at iron sites in enzymes like P450nor could enable pharmaceutical researchers to better rank drug candidates and identify metabolic risks earlier in the R&D pipeline.
Supported in part by Amazon Web Services (AWS) cloud compute credits, the demonstration highlights how QC Ware‘s cloud-native Promethium® platform can potentially allow classical GPU clusters to seamlessly connect with cloud quantum computing resources on Amazon Braket.
Available today, Promethium’s GPU-native architecture enables researchers to apply quantum chemistry across larger molecular systems and more compounds than has traditionally been practical. For selected workloads, Promethium runs demanding calculations up to 20× faster than conventional CPU-based DFT platforms, helping researchers generate molecular-level insights in hours instead of weeks and accelerating decision-making across drug discovery, catalysis, and materials science.
