Print Email Facebook Twitter KetGPT: Generating Quantum Circuits Using Transformers Title KetGPT: Generating Quantum Circuits Using Transformers Author Apak, Boran (TU Delft Applied Sciences) Contributor Feld, S. (mentor) Bandic, M. (graduation committee) Sarkar, A. (graduation committee) Sebastiano, F. (graduation committee) Vardoyan, G.S. (graduation committee) Degree granting institution Delft University of Technology Programme Applied Physics Date 2023-11-30 Abstract The goal of this thesis is expanding quantum algorithm datasets to enhance our capability to benchmark quantum systems and to open up possibilities for using machine learning techniques in quantum circuit mapping. Both of these areas are currently hindered by the lack of a wide range of useful quantum algorithms. To solve this problem, KetGPT is presented, a model that uses the revolutionary transformer machine learning architecture to generate synthetic, yet realistic looking, quantum circuits. By visual inspection, KetGPT generated circuits are easily distinguishable from random circuits, and show desirable qualities such as structure and human-like programming factors including applying gates in the order of ascending qubits. Consequently, they might be more suitable for certain tasks like benchmarking and training a reinforcement learning compiler. In an attempt to quantify the quality of circuits generated by KetGPT, a separate transformer classifier model was trained on the task of classifying the synthetic circuits generated by KetGPT as either real circuits, or as random circuits. However, although this classifier might capture realistic features of quantum circuits, the classifier has not been unambiguously proven to be reliable, and can therefore not be used as a standalone tool to determine the quality of KetGPT generated quantum circuits. Nevertheless, KetGPT and the transformer classifier are novel, promising approaches in an attempt to expand quantum algorithm datasets. Subject Machine learningQuantum ComputingQuantum Circuit DesignTransformersQuantum Circuit Synthesis To reference this document use: http://resolver.tudelft.nl/uuid:ffa47fb5-527b-4e8c-be88-f25fb9d27761 Part of collection Student theses Document type master thesis Rights © 2023 Boran Apak Files PDF Master_Thesis_Boran_Apak_1_.pdf 950.73 KB Close viewer /islandora/object/uuid:ffa47fb5-527b-4e8c-be88-f25fb9d27761/datastream/OBJ/view