Accurate Quantum Chemistry on Scalable Hardware: Simulating Butyronitrile Dissociation with IQM & Kvantify QDK

Kvantify has demonstrated the replication of a 2023 study simulating the dissociation of butyronitrile using the IQM Resonance Cloud, achieving calculations with up to 20 spin orbitals – exhausting the capacity of the IQM Garnet quantum chip. The research, facilitated by the Kvantify Chemistry QDK, showcases economically viable quantum chemistry calculations on hybrid quantum-classical systems, utilising realistic basis sets and advanced subsystem selection techniques. Experiments conducted on both IQM Sirius and Garnet devices demonstrate smooth convergence towards accurate results, with the QDK offering a pathway for computational chemists to access quantum hardware without specialist quantum algorithmic expertise, and highlighting butyronitrile’s potential as an electrolyte in lithium-ion batteries and dye-sensitized solar cells.

Kvantify Chemistry QDK and IQM Hardware Integration

Researchers at Kvantify and IQM collaborated to advance computational molecular modelling, leveraging their respective expertise in quantum chemistry software and hardware development. This integration facilitates computational analysis of molecular dissociation, a key process for understanding chemical reactions and molecular stability. The Kvantify Chemistry QDK now replicates butyronitrile dissociation curves using quantum hardware, demonstrating integration with IQM devices. Potential energy surface analysis demonstrates the convergence of results obtained using both IQM’s Sirius and Garnet devices, as well as Kvantify’s exact chemistry-specific state-vector simulator.

Enhancements to the original butyronitrile study include utilising a realistic basis set, PCSEG-2, replacing the simpler STO-3G, and implementing even-handed subsystem selection to ensure consistent orbital selection during projective-embedding calculations. These improvements refine the accuracy and reliability of the computational model, providing a more detailed and representative depiction of molecular behaviour. Observed errors, while present, follow a predictable pattern, with the highest errors occurring in the dissociation limit, as expected, confirming the robustness of the methodology. This offers a more robust platform for investigating complex chemical processes and predicting molecular properties.

Dissociation calculations with increasing spin orbitals reveal a clear correlation between computational error and the complexity of the molecular system. As the number of spin orbitals increases, computational demands grow, and the potential for error rises, particularly as the Hartree-Fock state diverges from the actual state at the dissociation limit. Despite these challenges, FAST curves generated by the simulations closely follow CASCI curves, indicating a high degree of accuracy and convergence.

Butyronitrile exhibits properties that make it a promising electrolyte candidate in advanced technologies such as lithium-ion batteries and dye-sensitized solar cells (DSSCs). Its low viscosity at reduced temperatures and chemical stability against cathode oxidation address critical limitations in current electrolyte materials. This potentially improves the performance and lifespan of these energy storage and conversion technologies. Further research will focus on optimising butyronitrile-based electrolytes for specific applications.