QC Ware and IonQ (NYSE:IONQ) announced a technology demonstration of a hybrid quantum-classical chemistry workflow combining QC Ware’s Promethium platform with IonQ’s Forte trapped-ion quantum computer, accessed through Amazon Braket.
The demonstration modelled the heme active site of cytochrome P450nor, an enzyme in the cytochrome P450 superfamily involved in nitric oxide reduction. Members of the same enzyme superfamily are responsible for a substantial proportion of human drug metabolism.
According to the companies, the workflow calculated electrostatic interaction energy within 0.5 kcal/mol, or approximately 4%, of classical benchmarks. The result was within the 1 kcal/mol threshold generally described as chemical accuracy and, according to the companies, provided more than twice the accuracy of the standard classical mean-field method.
Workflow combines GPU processing with IonQ Forte
The demonstration combined GPU-accelerated classical pre-processing through Promethium with quantum measurements performed using IonQ Forte.
Promethium constructed and preprocessed a 115-atom model of the P450nor active site containing more than 1,000 molecular orbitals. The platform then isolated a four-orbital active space that was mapped onto eight qubits.
IonQ Forte measured the eight qubits in a single basis before returning the results to Promethium, which performed the final interaction-energy calculations using classical computing resources.
“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.
Companies assess potential applications in drug discovery
IonQ said the demonstration illustrates the potential use of hybrid quantum-classical computing approaches for molecular modelling in drug discovery.
“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 statements regarding potential drug-discovery applications represent the companies’ assessment of the technology. The demonstration itself does not establish clinical effectiveness or broader commercial performance.
AWS supported demonstration with cloud credits
The work was supported in part by cloud computing credits provided by Amazon Web Services.
According to the press release, Promethium’s GPU-native architecture can perform certain calculations as much as 20 times faster than conventional CPU-based density functional theory platforms.
The reported performance figures and accuracy results relate to the specific calculations and demonstration described by the companies and should not be interpreted as establishing equivalent performance across other workloads or applications.
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