arXiv subjects

Juhani Merilehto

Publications and source records attributed to Juhani Merilehto.

4 recordsLinked to original sources

arXiv2026

Generative AI for Quantum Circuits and Quantum Code: A Technical Review and Taxonomy

We review thirteen generative systems and five supporting datasets for quantum circuit and quantum code generation, identified through a structured scoping review of Hugging Face, arXiv, and provenance tracing (January-February 2026). We organize the field along two axes: artifact type (Qiskit code, OpenQASM programs, circuit graphs); crossed with training regime (supervised fine-tuning, verifier-in-the-loop RL, diffusion/graph generation, agentic optimization); and systematically apply a three-layer evaluation framework covering syntactic validity, semantic correctness, and hardware executability. The central finding is that while all reviewed systems address syntax and most address semantics to some degree, none reports end-to-end evaluation on quantum hardware (Layer 3b), leaving a significant gap between generated circuits and practical deployment. Scope note: quantum code refers throughout to quantum program artifacts (QASM, Qiskit); we do not cover generation of quantum error-correcting codes (QEC).

cs.CE↗

arXiv2026

Malware Detection based on API Calls: A Reproducibility Study

This study independently reproduces the malware detection methodology presented by Felli cious et al. [7], which employs order-invariant API call frequency analysis using Random Forest classification. We utilized the original public dataset (250,533 training samples, 83,511 test samples) and replicated four model variants: Unigram, Bigram, Trigram, and Combined n gram approaches. Our reproduction successfully validated all key findings, achieving F1-scores that exceeded the original results by 0.99% to 2.57% across all models at the optimal API call length of 2,500. The Unigram model achieved F1=0.8717 (original: 0.8631), confirming its ef fectiveness as a lightweight malware detector. Across three independent experimental runs with different random seeds, we observed remarkably consistent results with standard deviations be low 0.5%, demonstrating high reproducibility. This study validates the robustness and scientific rigor of the original methodology while confirming the practical viability of frequency-based API call analysis for malware detection.

cs.CR↗

arXiv2025

A 200-Line Python Micro-Benchmark Suite for NISQ Circuit Compilers

We present microbench.py, a compact (approx. 200 lines) Python script that automates the collection of key compiler metrics, i.e., gate depth, two-qubit-gate count, wall-clock compilation time, and memory footprint, across multiple open-source quantum circuit transpilers. The suite ships with six didactic circuits (3 to 8 qubits) implementing fundamental quantum algorithms and supports Qiskit, tket, Cirq, and the Qiskit-Braket provider; in this paper we showcase results for Qiskit 0.46 and Braket 1.16. The entire run completes in under three minutes on a laptop, emits a single CSV plus publisheable plot, and reproduces the figure here with one command. We release the code under the MIT licence to serve as a quick-start regression harness for NISQ compiler research.

cs.ET↗

arXiv2024

From PDFs to Structured Data: Utilizing LLM Analysis in Sports Database Management

This study investigates the effectiveness of Large Language Models (LLMs) in processing semi-structured data from PDF documents into structured formats, specifically examining their application in updating the Finnish Sports Clubs Database. Through action research methodology, we developed and evaluated an AI-assisted approach utilizing OpenAI's GPT-4 and Anthropic's Claude 3 Opus models to process data from 72 sports federation membership reports. The system achieved a 90% success rate in automated processing, successfully handling 65 of 72 files without errors and converting over 7,900 rows of data. While the initial development time was comparable to traditional manual processing (three months), the implemented system shows potential for reducing future processing time by approximately 90%. Key challenges included handling multilingual content, processing multi-page datasets, and managing extraneous information. The findings suggest that while LLMs demonstrate significant potential for automating semi-structured data processing tasks, optimal results are achieved through a hybrid approach combining AI automation with selective human oversight. This research contributes to the growing body of literature on practical LLM applications in organizational data management and provides insights into the transformation of traditional data processing workflows.

cs.CE↗