Vicente Leyton-Ortega is a research scientist at Oak Ridge National Laboratory and the Software Thrust leader for the Quantum Science Center, where he works on hybrid quantum-classical algorithms, quantum error mitigation, and quantum software development. His research focuses on the integration of quantum computing with high-performance computing systems for scientific applications. His current work centers on developing scalable hybrid algorithms and QHPC software ecosystems. Dr. Leyton-Ortega received his Ph.D. in physics with a focus on out-of-equilibrium quantum dissipative systems.
Presentation Title:
AI for Quantum at the Quantum Science Center: Towards building a Connective Layer Across the Quantum Computing Stack
Presentation Abstract:
Quantum computing is advancing toward utility faster than the surrounding scientific infrastructure can absorb it. The field confronts a fragmented and rapidly expanding literature, the combinatorial intractability of fault-tolerant error correction, the absence of principled evaluation standards for AI systems operating in the quantum domain, and the formidable orchestration burden of coupling nascent quantum processors with mature high-performance computing (HPC). This talk presents the AI for Quantum efforts of the Agentic Software team at the Quantum Science Center (QSC), Oak Ridge National Laboratory, advancing a unifying thesis: that artificial intelligence can serve as the connective and accelerating substrate of the quantum stack, spanning knowledge curation, scientific reasoning, and HPC-integrated execution. We present a complementary portfolio of seven efforts organized along three axes. For knowledge infrastructure, we describe Quantum RAG, a retrieval-augmented generation system grounded in domain-specific corpora, and the Quantum Computing LLMWiki, a curated, machine-readable knowledge resource. For evaluation, we introduce the Quantum Computing Leaderboard, which rigorously benchmarks frontier AI models on quantum code generation, alongside benchmark development for the IEEE Quantum Week (QCE) shared-task problems. For domain reasoning and systems, we present preliminary versions of ChatQEC and FTQEC, language-model-driven assistants for quantum and fault-tolerant error correction, and a quantum computing multi-agent framework that decomposes complex quantum-HPC workflows across specialized agents and currently demonstrates strong code generation performance. We further introduce EPIC, a multi-agent framework for HPC orchestration, data management, and predictive analytics that we are extending toward quantum-HPC (QHPC) co-execution. Throughout, we report design rationale, quantitative findings where available, and the characteristic failure modes that frontier models exhibit on rigorous quantum tasks. We conclude by articulating how these components compose into an integrated assistance and evaluation fabric for the quantum community, and by framing the open problems that must be solved to realize trustworthy, verifiable AI for quantum science.