AI Methods for Quantum Circuit Optimization
Full-day tutorial at IEEE Quantum Week 2025 (QCE25) on using and training AI-powered transpiler passes in Qiskit for quantum circuit optimization, co-presented with David Kremer …
Full-day tutorial at IEEE Quantum Week 2025 (QCE25) on using and training AI-powered transpiler passes in Qiskit for quantum circuit optimization, co-presented with David Kremer …
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 …
This paper explores the application of machine learning (ML) techniques in predicting the QPU processing time of quantum jobs. By leveraging ML algorithms, this study introduces …
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 …
Family of open-source LLMs (8B–24B) for quantum code generation, plus the Qiskit HumanEval* benchmarks — 30K+ downloads on Hugging Face
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 …
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 …
We introduce a Reinforcement Learning (RL)-based method for re-synthesis of quantum circuits containing arbitrary Pauli rotations alongside Clifford operations. By collapsing each …