# AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization

arXiv: https://arxiv.org/abs/2608.12936 · HTML: https://mlsanity.com/paper/2608.12936/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 142 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Builds on (most cited first, 28 of 28)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025)
- [Autoformalization with Large Language Models](https://mlsanity.com/paper/2205.12615/) (2022)
- [Symbolic Regression with a Learned Concept Library](https://mlsanity.com/paper/2409.09359/) (2024)
- [Challenges and Opportunities in Quantum Optimization](https://mlsanity.com/paper/2312.02279/) (2023)
- [KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search](https://mlsanity.com/paper/2406.17630/) (2024)
- [Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning](https://mlsanity.com/paper/2405.13196/) (2024)
- [Compiler Optimization for Quantum Computing Using Reinforcement Learning](https://mlsanity.com/paper/2212.04508/) (2022)
- [Assessing requirements to scale to practical quantum advantage](https://mlsanity.com/paper/2211.07629/) (2022)
- [MerLean: An Agentic Framework for Autoformalization in Quantum Computation](https://mlsanity.com/paper/2602.16554/) (2026)
- [Disentangling Hype from Practicality: On Realistically Achieving Quantum Advantage](https://mlsanity.com/paper/2307.00523/) (2023)
- [Efficient decomposition of unitary matrices in quantum circuit compilers](https://mlsanity.com/paper/2101.02993/) (2021)
- [Improving Quantum Circuit Synthesis with Machine Learning](https://mlsanity.com/paper/2306.05622/) (2023)
- [How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits](https://mlsanity.com/paper/2411.10406/) (2024)
- [SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond](https://mlsanity.com/paper/2502.03367/) (2025)
- [ScaffCC: Scalable Compilation and Analysis of Quantum Programs](https://mlsanity.com/paper/1507.01902/) (2015)
- [Resource-Efficient Quantum Computing by Breaking Abstractions](https://mlsanity.com/paper/2011.00028/) (2020)
- [A MLIR Dialect for Quantum Assembly Languages](https://mlsanity.com/paper/2101.11365/) (2021)
- [Expressing and Analyzing Quantum Algorithms with Qualtran](https://mlsanity.com/paper/2409.04643/) (2024)
- [TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery](https://mlsanity.com/paper/2410.01752/) (2024)
- [Enabling Dataflow Optimization for Quantum Programs](https://mlsanity.com/paper/2101.11030/) (2021)
- [Optimizing Quantum Circuits, Fast and Slow](https://mlsanity.com/paper/2411.04104/) (2024)
- [Flexible Type-Based Resource Estimation in Quantum Circuit Description Languages](https://mlsanity.com/paper/2408.03121/) (2024)
- [Integrating Quantum Software Tools with(in) MLIR](https://mlsanity.com/paper/2601.02062/) (2026)
- [EQISA: Energy-efficient Quantum Instruction Set Architecture using Sparse Dictionary Learning](https://mlsanity.com/paper/2603.20646/) (2026)
- [Model selection in hybrid quantum neural networks with applications to quantum transformer architectures](https://mlsanity.com/paper/2603.21749/) (2026)
- [Resource Estimation for Fault-Tolerant Quantum Programs](https://mlsanity.com/paper/2608.04573/) (2026)

## Abstract (arXiv)

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.