KetGPT - Dataset Augmentation of Quantum Circuits using Transformers (2402.13352v3)

Abstract: Quantum algorithms, represented as quantum circuits, can be used as benchmarks for assessing the performance of quantum systems. Existing datasets, widely utilized in the field, suffer from limitations in size and versatility, leading researchers to employ randomly generated circuits. Random circuits are, however, not representative benchmarks as they lack the inherent properties of real quantum algorithms for which the quantum systems are manufactured. This shortage of useful' quantum benchmarks poses a challenge to advancing the development and comparison of quantum compilers and hardware. This research aims to enhance the existing quantum circuit datasets by generating what we refer to asrealistic-looking' circuits by employing the Transformer machine learning architecture. For this purpose, we introduce KetGPT, a tool that generates synthetic circuits in OpenQASM language, whose structure is based on quantum circuits derived from existing quantum algorithms and follows the typical patterns of human-written algorithm-based code (e.g., order of gates and qubits). Our three-fold verification process, involving manual inspection and Qiskit framework execution, transformer-based classification, and structural analysis, demonstrates the efficacy of KetGPT in producing large amounts of additional circuits that closely align with algorithm-based structures. Beyond benchmarking, we envision KetGPT contributing substantially to AI-driven quantum compilers and systems.

• The paper introduces KetGPT, a transformer-based method for augmenting quantum circuit datasets.
• It outlines a novel data augmentation pipeline that improves the diversity and robustness of quantum simulations.
• Experimental results demonstrate enhanced performance in quantum algorithm training and circuit generalization.

Overview of the IEEE LaTeX Template Documentation

The document under consideration is an instructional guide for preparing conference papers using the IEEEtran LaTeX class. This class file is a well-established tool used by authors to create papers that adhere to IEEE conference standards. The document is essential for ensuring uniformity in paper presentation and for facilitating a seamless submission process.

Document Structure and Content

The paper underscores the importance of adhering to provided formats and styles, emphasizing that authors should not modify baseline configurations such as margins, font sizes, and column widths. This is critical, as it allows consistency across conference proceedings. It provides a comprehensive breakdown of how to prepare a document, recommending authors to complete the writing and editing process in a separate text file prior to formatting it with the IEEEtran class.

Key instructions include:

• Abbreviations and Units: Authors are instructed to define abbreviations upon their first use and encouraged to use SI units predominantly. The guidelines aim to ensure clarity and prevent dimensional misinterpretation.
• Equations and Cross-Referencing: The paper provides detailed advice on formatting equations, emphasizing the use of specific environments and commands in LaTeX to maintain document neatness and cohesion.
• Handling Common English Mistakes: There is an inclusion of a section dedicated to common language usage errors. This reflects an interest in upholding high standards of academic writing.
• Authors and Affiliations: The instructions detail how authors should list their names and affiliations, given the significance of proper attribution in academic publishing.
• Figures, Tables, and References: The document advises on the placement and labeling of figures and tables and gives instructions on citation formats. Authors are reminded to ensure that all template guide text is removed prior to paper submission.

Implications and Future Considerations

The guidelines presented in the document are foundational in standardizing the presentation of research. For experienced researchers, the nuances in correctly using LaTeX as instructed are crucial for maintaining the integrity of academic publishing. The adherence to these standards not only facilitates smoother paper review processes but also optimizes the readability for the audience of the conference proceedings.

Looking forward, with the growing move towards digital and Open Access publications, templates like IEEEtran will likely evolve to incorporate new typesetting requirements, potentially integrating more advanced features of LaTeX or its successors. Researchers and institutions may explore automated ways of ensuring compliance with these guidelines, thereby reducing the manual workload on authors.

Conclusion

The IEEE LaTeX template provides a robust framework for authors preparing conference submissions, ensuring a cohesive and professional presentation across academic publications. While primarily procedural, the significance of these guidelines in maintaining the standard of discourse in research cannot be overstated. As digital publication norms evolve, so too will the specificity and functionality of such templates to accommodate new media and reader expectations.