Supervised fine-tuning on gate-by-gate quantum simulation traces allows LLMs to achieve near-perfect accuracy in predicting quantum measurement outcomes, with added GRPO improving generalization to larger qubit counts.
Unleashing the potential of llms for quantum computing: A study in quantum architecture design
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This research agenda argues that cloud-native architectures, microservices, autoscaling, and emerging trends like serverless inference and federated learning are required to make large language models efficient and scalable.
A synthesis of expert insights from the ADAC Quantum Computing Working Group and member survey on the complementary roles of quantum and classical high-performance computing in future hybrid infrastructures.
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Fine-Tuning Large Language Models for Quantum Reasoning
Supervised fine-tuning on gate-by-gate quantum simulation traces allows LLMs to achieve near-perfect accuracy in predicting quantum measurement outcomes, with added GRPO improving generalization to larger qubit counts.
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Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda
This research agenda argues that cloud-native architectures, microservices, autoscaling, and emerging trends like serverless inference and federated learning are required to make large language models efficient and scalable.