We adapted Microsoft's QuantumKatas from Q# to Qiskit and turned them into a 350-task benchmark for evaluating how well LLMs write quantum code. We ran 16 models across 7 prompting …
A new MCP server that enables AI agents to autonomously train reinforcement learning models for quantum circuit synthesis - including permutation, linear function, and Clifford …
The Curry–Howard correspondence—propositions as types, proofs as programs—offers a conceptual framework for understanding what's missing in current LLMs and what becomes possible …
You can now run the Qiskit Code Assistant locally easily. Download optimized models in GGUF format, install Ollama, and configure your VSCode or JupyterLab extension with a single …
We've upgraded the Qiskit Code Assistant! Last month, we introduced mistral-small-3.2-24b-qiskit, replacing granite-3.3-8b-qiskit, delivering better accuracy across key benchmarks …
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 …
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 …
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 …
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 …
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 …