David Peral García successfully defended his PhD thesis on Quantum Natural Language Processing, representing likely the first PhD thesis in Spain dedicated to this emerging field. A proud moment as my first doctoral thesis as an advisor.
Released a new paper on a novel approach to train AI models that can write better quantum code using Qiskit. The approach uses quantum verification at the core, smart training pipeline with DPO and GRPO, and real quantum feedback to ensure generated code works in practice.
Returned to my alma mater, the University of Salamanca, to share my PhD journey with current doctoral students. Discussed the importance of developing critical thinking, resilience, and problem-solving skills beyond research during PhD years.
Published research paper on AI methods for approximate compiling of unitaries, accepted at QCE24. The work uses deep learning and autoencoder-like models to enhance quantum circuit transpiling, demonstrating improvements over exhaustive search and random initialization on 2 and 3-qubit unitaries.
Published research paper introducing Qiskit HumanEval dataset for evaluating Large Language Models capability to generate quantum computing code. The dataset comprises more than 100 quantum computing tasks with prompts, solutions, test cases, and difficulty ratings, establishing benchmarks for generative AI tools in quantum code development.
Published research paper on training specialized LLMs for quantum computing code generation using Qiskit. Addresses unique challenges in quantum programming, including scarcity of quantum code examples and rapid field evolution. Our model outperforms existing state-of-the-art quantum computing models.
Published research demonstrating integration of Reinforcement Learning into quantum transpiling workflows for Qiskit transpiler service. Achieves near-optimal circuit synthesis and routing with significant performance improvements over traditional optimization methods, handling Linear Function, Clifford, and Permutation circuits up to 65 qubits.
Published peer-reviewed research comparing classical and quantum approaches to natural language processing in Expert Systems with Applications. Demonstrated that quantum NLP models can obtain the same or better results for simpler text classification tasks, with experiments utilizing up to 7 qubits across multiple classification scenarios.
Published systematic literature review on quantum machine learning and its applications in Computer Science Review journal. Analyzed 94 studies from 2017-2023, identifying two primary algorithm categories and highlighting image classification as a key application area, while noting that quantum hardware improvements are necessary for QML full potential.