Published a comprehensive blog post summarizing what Qiskit Code Assistant is and how to start using it. Following the recent launch, users are actively leveraging the tool features, and the team is developing improved models with enhanced capabilities for open source release.
Attended IEEE Quantum Week 2024 highlighting four major IBM Quantum AI initiatives: Qiskit Transpiler Service with AI-powered optimization, first preview of Qiskit Code Assistant, unitary compilation research with David Kremer, and the Qiskit HumanEval benchmark presentation.
Presenting the Qiskit HumanEval benchmark for LLMs at IEEE Quantum Week 2024 in the SYS-BNCH Benchmarking session. Available afterwards at the IBM Quantum booth to discuss AI and quantum computing initiatives.
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.
Excited to announce starting a new position at IBM Quantum as the first ever AI for Quantum Product Owner, leading initiatives at the convergence of artificial intelligence and quantum computing.
Excited about Forbes article highlighting IBM Quantum work at the intersection of AI and quantum computing. The integration of AI and Quantum Computing has the potential to transform industries and advance quantum computing capabilities significantly.
Announcing multiple major releases at the convergence of AI and quantum computing: beta version of Qiskit transpiler service (unveiled at THINK24), research paper on AI-powered transpiler passes, and Qiskit Code Assistant with LLMs and the Qiskit HumanEval benchmark. Both projects recognized through IBM Quantum Challenge.
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.