Quantum LDPC code discovery requires searching large algebraic design spaces while reliably certifying the parameters and equivalence classes of any candidates found. We present a workflow that uses large language models to mutate Python programs …
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 setups — 39,200 runs — and the results say a lot about where these models are strong and where they still fall short.
We adapt Microsoft's QuantumKatas - a well-established quantum computing curriculum - from Q# to Qiskit, the most widely-adopted quantum computing framework, and package it with an evaluation framework for systematic LLM assessment. The resulting …
Systems and techniques that facilitate intelligent unitary synthesis for quantum computing are provided. For example, a system can access a unitary matrix of a quantum payload circuit that fails to satisfy design constraints of a quantum computer's …
A new MCP server that enables AI agents to autonomously train reinforcement learning models for quantum circuit synthesis - including permutation, linear function, and Clifford circuits.
A computer-implemented system with machine learning capabilities designed to address quantum computing challenges. The system's recommendation component employs a machine learning model to generate, based on an input, a recommendation comprising a …
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.
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 …