Thrilled to have been a keynote speaker at Metafuturo 2025, sharing insights on the convergence of Quantum Computing and AI. From LLMs and agentic AI applications for quantum computing to AI-optimized quantum circuits at IBM Quantum.
Attending IEEE Quantum Week in Albuquerque, New Mexico, sharing IBM Quantum's work at the intersection of AI and Quantum Computing. Presenting on QPU time prediction with ML, AI methods for quantum circuit optimization, and demoing AI-powered quantum development environments.
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.
Celebrating the impact of AI transpiler passes on IBM's quantum hardware achievements. Our team's work on AI-powered transpilation helped enable quantum volume milestones of 1024 and 2048 on IBM's r3 beta QPU (ibm_pittsburgh).
Announcing the latest open-source LLM releases from the Qiskit Code Assistant team, featuring Qiskit 2.0 compatibility, enhanced text understanding, and new models including Granite 3.3, Granite 3.2, and Qwen2.5-Coder series.
Attended IBM Tech 2025 in Singapore, an exclusive invitation-only gathering of IBM top technical talent from around the world. Deep discussions on AI, quantum computing, and emerging technologies with brilliant colleagues across different technical disciplines.
Celebrating the arxiv publication of the Pauli Network Circuit Synthesis with Reinforcement Learning paper. The AI-powered transpiler pass has been available in the Qiskit Transpiler Service since November 2024, as presented at Quantum Developer Conference 2024.
Released a new version of Qiskit HumanEval compatible with Qiskit 1.4, featuring significant improvements to the benchmark including more robust and rigorous code execution tests for more accurate evaluations of LLM-generated quantum code.
Exciting update: Qiskit Code Assistant service now exposes compatible endpoints with OpenAI Completions API. This enables seamless usage via existing libraries like OpenAI and LiteLLM, making it easy to infuse Qiskit knowledge into your LLM pipelines.
Released granite-8b-qiskit-rc-0.10, the latest revision of the LLMs that empower Qiskit Code Assistant. Trained on significantly expanded Qiskit synthetic dataset, this marks the final model using the current training approach as we pivot to newer Granite base models and cutting-edge techniques.