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.
Extremely proud of the massive Qiskit SDK v1.0 release including two projects from my team: the Qiskit Transpiler with AI transpiling passes and the Qiskit Code Assistant. This full-stack software for quantum computing brings together cutting-edge AI and quantum technologies.
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.
IBM Quantum team recruiting AI Engineer interns based in Spain to work on introducing new AI-based capabilities in the software stack, including AI circuit transpilers compatible with Qiskit and LLM-powered code assistants. Interns will support AI model training, deployment, software service development, and MLOps work.
Honored by major announcements from IBM Quantum Summit 2023, including IBM Heron chip with 3-5x performance improvement, 1,121-processor Condor system, operational Quantum System Two, Qiskit 1.0 release, Quantum Serverless beta, and AI integration for automated code development and enhanced transpiler tools.