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When a Quantum Scientist Interviews ChatGPT

Rasit Onur Topaloglu

Published Feb 9, 2023• 1 citation
Publisher

Abstract

The following is a conversation with ChatGPT, a large language machine learning model, on the topic of quantum. In the discussion section, the responses of ChatGPT are analyzed. Although comprehensive, impressive, and full of surprises, ChatGPT is not yet free of errors, typos, or obscure suggestions. A particular recommendation is to include awareness to ChatGPT about the source of its knowledge.

DOI
10.36227/techrxiv.21982619

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The following is a conversation with ChatGPT, a large language machine learning model, on the topic of quantum. In the discussion section, the responses of ChatGPT are analyzed. Although comprehensive, impressive, and full of surprises, ChatGPT is not yet free of errors, typos, or obscure suggestions. A particular recommendation is to include awareness to ChatGPT about the source of its knowledge.
Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.
This is my first attempt to communicate with the ChatGPT on the Einstein-Podolsky-Rosen paradox. ChatGPT reacted promptly with a good overview of this very wide topic. ChatGPT during our half hour conversation concluded that there is still room for the further development of the EPR paradox because this research field is far from the final theory. However, ChatGPT was skeptical to search for the missing element of the physical reality in papers of Old Masters working between 17th and 19th centuries. (One potential candidate for a more general physical theory of the EPR paradox can be found in the overlooked works of Old Masters working in the period of the 17th–19th centuries).
Quantum computing is a growing field that promises many real-world applications such as quantum cryptography or quantum finance. The number of people able to use quantum computing is however still very small. This limitation comes from the difficulty to understand the concepts and to know how to start coding. Therefore, there is a need for tools that can assist non-expert in overcoming this complexity. One possibility would be to use existing conversational agents. Unfortunately ChatGPT and other Large-Language Models produce inaccurate results.
Quantum Software Engineering (QSE) has recently emerged as a potential research and development direction frequently practiced by many tech joints. However, quantum developers face challenges in optimizing quantum computing and QSE concepts. Quantum developers use the Stack Overflow (SO) platform to report and discuss quantum-related challenges. Also, quantum practitioners use specialized quantum tags to label quantum-related posts in developers' forums. However, these quantum tags referred to more technical quantum aspects than the developer posts. Therefore, categorizing quantum practitioners' questions based on quantum concepts can help quantum developers better identify frequently occurring challenges to QSE. For this purpose, we conducted qualitative and quantitative studies to classify quantum developers' questions into various frequently occurring quantum-related challenges. We extracted 2829 developers' questions from various Q&A platforms using queries and filters based on quantum-related tags. Next, the developers' posts on the Q&A forums were critically analyzed to identify frequently discussed quantum-related challenges and develop a novel grounded theory. The frequent quantum developer challenges identified by analyzing practitioners' discussions in the Q&A forums include Tooling, Theoretical, Learning, Conceptual, Errors, and API Usage. Moreover, using content analysis and grounded theory, the developers' discussions were annotated with commonly reported quantum challenges to develop a ground truth and a novel dataset. A Large Language model (ChatGPT) was used to validate the human annotation and overcome disagreements. Finally, various fine-tuned Deep and Machine learning (D&ML) classifiers automatically classify developer discussions into commonly reported quantum challenges. Additionally, to improve the classification results, we utilized textual data augmentation approaches, such as random deletion, swapping, and insertion with the D&ML classifiers. We obtained average accuracies of 89%, 86%, 84%, 84%, and 80% with FNN, CNN, LSTM, GRU, and RNN classifiers, respectively. This helps quantum researchers and vendors propose solutions and tools to frequently occurring issues for quantum developers.