AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization

2026-08-13

AI Summary

This work addresses the limitations of existing quantum resource estimation methods, which rely heavily on compilation pipelines or expert knowledge and are tightly coupled to long-term fault-tolerant assumptions, rendering them ill-suited for the diverse demands of current hardware–algorithm co-design. To overcome these challenges, the paper proposes an automated, full-stack resource estimation and optimization framework. It features a user-definable abstraction of the quantum computing stack, a modular component library, hierarchical resource surrogate models built upon algorithmic profiling and neurosymbolic learning, and a multi-objective optimization mechanism embedded within the deployment workflow. Evaluated across scenarios including fault-tolerant algorithms, small-scale error-correcting codes, gate decompositions, and variational circuit training, the framework uncovers nontrivial resource trade-offs overlooked by existing tools, demonstrating its generality and practical utility.

Abstract

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.

Problem

• Quantum Resource Estimation
• Full-stack Optimization
• Quantum Computing Stack
• Fault-tolerant Assumptions
• Resource Trade-offs

Innovation

• Quantum Resource Estimation
• Full-stack Co-design
• Surrogate Modeling
• Neuro-symbolic Learning
• Multi-objective Optimization