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 predictive models that are designed to enhance operational efficiency in …
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.
This paper investigates artificial intelligence (AI) methodologies for the synthesis and transpilation of permutation circuits across generic topologies. Our approach uses Reinforcement Learning (RL) techniques to achieve near-optimal synthesis of …
An approach for assisting in the generation of quantum source code. The approach may include receiving a quantum source code input with a specification of constraints of a quantum unit on which the quantum source code input is to be run, wherein the …
We introduce a Reinforcement Learning (RL)-based method for re-synthesis of quantum circuits containing arbitrary Pauli rotations alongside Clifford operations. By collapsing each sub-block to a compact representation and then synthesizing it …
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 …
Excited to announce starting a new position at IBM Quantum as the first ever AI for Quantum Product Owner, leading initiatives at the convergence of artificial intelligence and quantum computing.
Excited about Forbes article highlighting IBM Quantum work at the intersection of AI and quantum computing. The integration of AI and Quantum Computing has the potential to transform industries and advance quantum computing capabilities significantly.
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to port code while adapting the code for a configuration of a target system. A system can comprise a memory that …
This paper demonstrates the integration of Reinforcement Learning (RL) into quantum transpiling workflows, significantly enhancing the synthesis and routing of quantum circuits. By employing RL, we achieve near-optimal synthesis of Linear Function, …