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This paper introduces AutoQuREO, an automated framework designed to enhance quantum resource estimation (QRE) and optimization across diverse quantum computing stacks. By integrating user-defined abstractions, a modular library for rapid prototyping, surrogate modeling through neuro-symbolic learning, and multi-objective optimization, AutoQuREO facilitates the exploration of complex design spaces in quantum computing. The framework's effectiveness is demonstrated through case studies that reveal previously inaccessible resource trade-offs, positioning it as a vital tool for advancing quantum technology readiness.
AutoQuREO uncovers hidden resource trade-offs in quantum computing that existing tools struggle to reveal, transforming the landscape of quantum resource estimation.
As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks. Quantum resource estimation (QRE) plays a central role in this transition, yet existing approaches remain largely compilation-heavy or domain-knowledge-guided symbolic annotations, and tightly coupled to long-term fault-tolerant assumptions, limiting their topical applicability. In this work, we introduce AutoQuREO, an Automated framework for full-stack Quantum Resource Estimation and Optimization. AutoQuREO is built around four core novelties: (i) a flexible, user-defined abstraction of the quantum computing stack; (ii) a modular library of reusable stack components enabling rapid full-stack prototyping; (iii) surrogate modeling of layer-wise resources via algorithmic profiling and neuro-symbolic learning; and (iv) integrated multi-objective optimization that embeds QRE directly into deployment pipelines. Together, these design choices enable AutoQuREO to serve as a digital twin for quantum computing stacks, supporting the tractable exploration of complex design spaces. We demonstrate the capabilities of AutoQuREO through representative co-design case studies, including early-fault-tolerant quantum algorithms, small error correction codes, gate decomposition and variational training of parametric quantum circuits. These examples illustrate how AutoQuREO enables systematic discovery of unexploited resource trade-offs that are computationally intractable or abstruse using existing QRE tools. AutoQuREO is positioned as a general-purpose platform for advancing quantum technology readiness.
