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 command.
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 when AI systems learn to construct and verify proofs natively.
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 and more precise responses for quantum programming tasks.
Technical presentation at the BasQ Qiskit Fall Fest 2025, a two-day quantum computing festival. This talk covers how AI can be applied to Qiskit development and quantum computing workflows, using the Qiskit Code Assistant and the AI-powered …
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.
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.
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.
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.
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.