Researchers have developed AutoQuREO, a novel framework designed to automate the estimation and optimization of quantum computing resources. This system addresses a key challenge in the field by providing a full-stack approach that moves beyond compilation-heavy or domain-knowledge-guided methods. AutoQuREO utilizes a flexible abstraction of quantum computing stacks, a modular component library, surrogate modeling with neuro-symbolic learning, and integrated multi-objective optimization to act as a digital twin for quantum computing stacks. The framework has been demonstrated through case studies involving fault-tolerant quantum algorithms, error correction codes, gate decomposition, and parametric quantum circuits, showcasing its ability to uncover resource trade-offs. AI
IMPACT This framework could accelerate the development and deployment of practical quantum computing applications by streamlining resource management.
RANK_REASON The cluster describes a new framework presented in an arXiv paper for quantum resource estimation and optimization.
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- arXiv
- AutoQuREO
- quantum computing
- Quantum resource estimation
- Error correction codes for secure chaos-based communication system
- fault-tolerant quantum algorithms
- gate decomposition
- parametric quantum circuits
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