pyChemiQ simplifies the conversion between molecular structure inputs and quantum circuits, minimizing the domain expertise required to enter the field, making it more convenient for interested researchers to solve and study electronic structure problems on quantum computers.
pyChemiQ provides a rich and flexible set of interfaces for generating Hamiltonians, customizing ansatz circuits, and optimizing circuit parameters. In addition to using pyChemiQ's basic interfaces for computation, you can also set up a configuration file for direct execution. More advanced features require a license and can be invoked through the configuration file after authorization. You can shorten quantum circuits and reduce runtime by using built-in optimization methods. Additional interfaces are available for functions such as slice number configuration, ansatz truncation, and MP2 initial parameter settings.
pyChemiQ can be used to solve quantum chemistry problems by simulating molecules on quantum computers using mean-field and post-mean-field methods.
from qmolecule import *
multiplicity=1
charge=0
basis="sto-3g"
geom="H 0 0 0, H 0 0 0.74"
mol=Molecules(
geometry=geom,
basis=basis,
multiplicity=multiplicity,
charge=charge
)
energy1=mol.calculate_energy(mode='HF')
energy2=mol.calculate_energy(mode='MP2')
print(energy1, energy2)
pyChemiQ uses qubit superposition and coherence to efficiently model electron wavefunctions, naturally suited for molecular systems and overcoming the "exponential wall" in classical methods.
Highly encapsulated and beginner-friendly, with streamlined workflows for quick start. Paired with the ChemiQ client for modeling and result visualization.
Supports core quantum chemistry features, integrates classical-quantum modules, and allows custom extensions for diverse needs.
Offers detailed docs, tutorials, and examples. Easy to use without coding, ideal for teaching and research.
Simulates fermionic systems representing electronic structures with high speed and accuracy, outperforming traditional methods.
Built-in presets and customizable parameters enable easy secondary development and algorithm tuning.
Covers the full drug discovery pipeline: virtual screening, affinity/selectivity optimization, and multi-attribute optimization to boost efficiency and success rate.
Supports reaction mechanism studies, path optimization, and transition state prediction to enhance catalysts and increase yields.
Enables multiscale modeling from electronic to microstructure level, suited for functional and energy materials innovation.
Accurately captures electron clouds, orbitals, and wavefunctions to support reactivity site identification and molecular structure analysis.