- Ramachandran Sekanipuram Srikanthan (Main Contributors)
- Hirmay Sandesara (Main Contributors)
- Yu-Cheng Lin
- YuChao,Hsu
We want to express our sincere gratitude to the Xanadu staff, especially Ivana, for supporting and motivating us throughout the project and patiently guiding us through whatever small or big problems we encountered. In addition, we would like to thank Lauren Prost from Alice & Bob for inspiring us with his intriguing and interesting presentation and for providing us with support and helpful suggestions. Furthermore, we want to thank AWS, Denvr Dataworks, and NVIDIA for providing us the power ups needed to perform further and extensive experimentation of our project.
Our primary goal through this project was to simulate open-quantum sys- tems suitable for the Noisy Intermediate-Scale Quantum (NISQ) era and the Intermediate-Scale Quantum (ISQ) era. Since certain special algorithms are implemented as Universal Quantum Algorithms (UQAs) for Fault Tolerant Quantum Computing (FTQ), which are currently not realized. Furthermore, Quantum Error Correction as mentioned in Laurent Prost’s presentation is not currently quite practical since it requires additional qubits (logical qubit for- mulation), though one can use cat qubits, which we have also incorporated in one of our framework (state preparation), but that too would be feasible in the ISQ era. Hence, there is absence of existing VQA framework for specific problems like “direct estimation of energy difference between two structures in chemistry”. The paper [1], discusses about the need for a UQA inspired frame- work with shallow circuit depth for NISQ devices (essentially a VQA algorithm) for such special problems. In this project, we aim to implement the framework in Python while also introducing our own innovations to enhance its robustness and expand its usability. We will not only utilize these innovations but also test the framework across various scenarios to showcase its usability and discuss the future implications of our work
Our project addresses three categories, namely: "Preparing for Battle," "Seeing the Future," and "The Sound of Silence." A detailed write-up of our work is provided in the shared report. The Submission folder contains the code files and datasets used for generating the results presented in our report. The Approach 1 folder contains all the code files for the simulations used in Subsection 6.2.1, named Approach 1, while the Approach 2 folder contains code files for the simulations used in Subsection 6.2.2, named Approach 2. The Cat Qubits folder includes simulations done using the cat qubit framework with 8 and 12 qubits. The Open Systems Simulation folder contains simulations of example 1, which involve utilizing the state preparation method using Approach 1, computing the evolution unitary, and conducting the simulations. Furthermore, in the Package folder, we have created Python packages for our two approaches. Lastly, the Testing_drug_target_prediction_dataset contains the chEMBL dataset used in the report.