qpe-toolbox is an open-source Python package designed for the compilation and simulation of Quantum Phase Estimation (QPE) circuits. Built on the tensor-network library quimb, it integrates classical quantum chemistry preprocessing (via PySCF and OpenFermion), quantum circuit construction, tensor-network-based simulation, and post-processing for accurate energy eigenvalue extraction.

Key features include:

• Support for multiple QPE variants: textbook QPE, Trotterized QPE, QPE with Linear Combination of Unitaries (LCU), and Robust Phase Estimation.

• Advanced classical preprocessing and Hamiltonian encoding for molecules and spin models.

• Integration with Density Matrix Renormalization Group (DMRG) and variational circuit optimization.

• Comprehensive tutorials and examples covering circuit building, chemistry-to-qubit mapping, Trotter decomposition, block encoding, and performance optimization.

This toolkit was developed as part of a collaborative project initiated by Director Min-Hsiu Hsieh, researchers Calvin Ku and Yu-Cheng Chen from Hon Hai Research Institute’s Quantum Computing Research Center. The release represents the collaborative achievement between Quobly and Hon Hai Research Institute. It transforms quantum algorithm research into practical simulation tools, significantly enhancing researchers’ efficiency in quantum chemistry, materials science, and other eigenvalue estimation problems on classical hardware, laying the foundation for future quantum computing applications.

Our team plans to continue updating the qpe-toolbox with future enhancements and additional joint publications.

qpe-toolbox is now available at:

👉 https://github.com/quobly-sw/qpe-toolbox