Shipped granite-8b-qiskit-rc-0.10, the latest revision of LLMs empowering Qiskit Code Assistant. Trained on significantly expanded Qiskit synthetic dataset, marking the end of an era as the final model using current training approach before pivoting to newer Granite base models.
Celebrating qiskit-ibm-transpiler recognition as the 4th most used quantum computing development tool globally in the 2024 Unitary Fund survey. A remarkable achievement for a project with just 1 year of public existence, combining Qiskit heuristic algorithms with novel AI transpiler passes.
Excited to participate in the Quantum Developer Conference 2024 at IBM Thomas J. Watson Research Center, networking with quantum computing professionals and showcasing new developments at the intersection of AI and quantum computing - featuring the Qiskit Code Assistant and AI-powered transpiler passes.
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.
This paper explores artificial intelligence (AI) methods for the approximate compiling of unitaries, focusing on the use of fixed two-qubit gates and arbitrary single-qubit rotations typical in superconducting hardware. Our approach involves three …
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.