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Python example code for the ACM/IEEE Quantum Computing for Drug Discovery Challenge at ICCAD 2023

What's in this repository?

This repository contains a simple example to illustrate how to train the variational quantum eigensolver on the Qiskit platform and evaluate the comprehensive performances in terms of accuracy, quantum resource cost, and classical computation resource cost.

For this example, we implemented a UCCSD ansatz with a Hartree-fork initial state. You can use different techniques to construct the ansatz and find the initial state for your implementation. We required all participants to read the Example_Code, as we describe details in the .ipynb file, with examples of implementation, how to calculate the accuracy, and how to obtain the circuit duration. Please notice that since participants may use different optimizers, we require all participants to self-report their optimization iterations directly from optimizer output, which will be detailed in the submission requirement that will be released soon.

How do I find the dataset?

You can find the Hamiltonian for the target molecule at Hamiltonian. And we also require participants to fix the seed for algorithmic, transpiling, and measurement in Qiskit; the final results should be the average of 10 different seeds in Seeds.

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