Title: AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Quantum Computing

Quantum Property Estimation

Data Engine

Quantum Many-body Problems

Deep Learning
Abstract: Quantum many-body problems are central to various scientific disciplines, yet their ground-state properties are intrinsically challenging to estimate. Recent advances in deep learning (DL) offer potential solutions in this field, complementing prior purely classical and quantum approaches. However, existing DL-based models typically assume access to a large-scale and noiseless labeled dataset collected by infinite sampling. This idealization raises fundamental concerns about their practical utility, especially given the limited availability of quantum hardware in the near term. To unleash the power of these DL-based models, we propose AiDE-Q (\underline{a}utomat\underline{i}c \underline{d}ata \underline{e}ngine for \underline{q}uantum property estimation), an effective framework that addresses this challenge by iteratively generating high-quality synthetic labeled datasets. Specifically, AiDE-Q utilizes a confidence-check method to assess the quality of synthetic labels and continuously improves the employed DL models with the identified high-quality synthetic dataset. To verify the effectiveness of AiDE-Q, we conduct extensive numerical simulations on a diverse set of quantum many-body and molecular systems, with up to 50 qubits. The results show that AiDE-Q enhances prediction performance for various reference learning models, with improvements of up to $14.2\\%$. Moreover, we exhibit that a basic supervised learning model integrated with AiDE-Q outperforms advanced reference models, highlighting the importance of a synthetic dataset. Our work paves the way for more efficient and practical applications of DL for quantum property estimation.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces AiDE-Q, a data framework engine designed for machine learning tasks in so-called quantum property estimation, particularly in the context of quantum many-body systems. It enables the generation of “high-quality” training data by assigning reliable synthetic labels even to noisy data derived from a limited number of quantum measurements. The core technical innovation is a consistency-check mechanism that assesses the confidence of synthetic labels by evaluating their stability across randomly masked subsets of measurement data. The method is compatible with various deep learning paradigms and the authors provide experimental results on Heisenberg XXZ and cluster Ising systems.\n\nSTRENGTHS AND WEAKNESSES:\nStrenghts:\n-The work introduces a novel framework to construct a dataset of accurate labeled data from a dataset containing inaccurate samples. The algorithm ensures that the quality of the training set does not degrade, as each iteration includes a validation step where the performance of the model is evaluated, and newly generated synthetic data are only accepted if they do not reduce performance.\n\n-It addresses a very important problem.\n\n-The consistency-check used to assess whether a synthetic label is sufficiently accurate does not require additional measurements on the training data, meaning it introduces no measurement overhead.\n\n-The authors validate their framework through experiments predicting physical quantities on systems such as the Heisenberg XXZ model and the one-dimensional cluster-Ising model.\n\n-The paper is well organized and the exposition is clear.\n\n\nWeaknesses:\n\n-The main weakness of the paper is that it does not include any theoretical analysis of the proposed algorithm. In particular, the authors do not discuss the computational cost of running AiDE-Q—for example, is a fixed number of iterations expected to be sufficient in more general cases? How should the algorithm scale with system size or data volume? Moreover, the effect of choosing different threshold values in the consistency check on the performance of the algorithm is not explored.\nWhy can we expect the algorithm will perform well at scale?\n\n-It is also not clear how the method is particularly suited for quantum property estimation problems. Specifically, why should the proposed method outperform existing techniques in the classical machine learning literature that also aim to enhance data quality? For example references [1,2,3] are not discussed or compared against in the paper.\n\nQUESTIONS:\n-Can the authors provide an estimate of the computational cost of running AiDE-Q for different problem sizes? Also, how should the key parameters—like the number of iterations— be chosen in order to obtain a target accuracy?\n-Related to this, how should one choose the threshold used in the consistency check? Does it have a strong impact on performance and running time?\n-Why can we expect the algorithm will perform well at scale?\n-Finally, can the authors compare AiDE-Q with existing methods for improving data quality, such as [1], [2], [3] and explain in what ways their method is better or different?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces AiDE-Q, a framework designed to improve the performance of deep learning (DL) models for quantum property estimation (QPE) when faced with practical limitations on data acquisition, such as a finite number of quantum measurements. The core challenge addressed is that real-world datasets for QPE are often \"hybrid,\" containing a small amount of high-quality data (many measurements, accurate labels) and a large amount of low-quality data (few measurements, noisy labels).\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\nThe paper addresses a critical and practical bottleneck in the application of DL to quantum many-body physics: the high cost and inherent noise of data acquisition from quantum systems. The problem of learning from hybrid-quality datasets is relevant for near-term quantum devices. The proposed AiDE-Q framework offers a practical solution to leverage large but noisy datasets effectively.\n\nWhile the concept of a \"data engine\" or self-training with pseudo-labels exists in the broader machine learning literature, its application to QPE is novel. The specific implementation, particularly the \"consistency-check\" method to gauge the quality of synthetic labels based on measurement data variance, is an original and clever contribution tailored to the problem domain.\n\nThe paper is well-written, clearly structured, and easy to follow. The problem setup is well-motivated (Figure 1), and the AiDE-Q framework is explained lucidly with the help of a detailed diagram (Figure 2). The experimental settings and results are presented clearly, making the contributions accessible.\n\n**Weaknesses**\n\nThe paper is primarily empirical. there is no theoretical analysis to formalize this or provide guarantees on convergence or performance improvement. For instance, under what conditions does the iterative process guarantee not to accumulate errors from incorrectly labeled data?\n\nThe framework introduces several new hyperparameters, such as the variance threshold for data selection (set to the top 10%), the number of subsets for the consistency check, and the size of these subsets. The paper uses fixed values for these and provides limited analysis of their sensitivity. A more detailed study on how these choices impact performance would strengthen the paper's claims of robustness.\n\nThe method of raising the variance threshold when validation performance drops is mentioned but not detailed. This seems like a crucial heuristic to prevent model degradation. A more principled or detailed explanation of this adaptive mechanism would be beneficial. Is it a fixed increment, or does it depend on the magnitude of the performance drop?\n\nQUESTIONS:\nSection 3.2 mentions that if the validation performance decreases, the variance threshold is raised to be more selective. Could you elaborate on the mechanism for adjusting this threshold?\n\nLIMITATIONS:\nAddressed", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents AiDE-Q, a method for enhancing the datasets used to train deep learning (DL) models to estimate the ground state properties of quantum systems. Exactly calculating the ground state properties of a quantum system is exponentially expensive on classical hardware due to theoretical constraints, and practically challenging on quantum hardware due to hardware noise and the limited availability of quantum hardware. As a result, DL models for ground state property estimation are typically trained on hybrid datasets consisting of a mix of high-quality data with highly accurate labels and low-quality data with very approximate labels. The high ratio of low to high quality data naturally leads to poor DL model performance.\n\nTo address this issue, the paper proposes the integration of high-quality synthetic data into hybrid datasets. AiDE-Q iteratively improves DL models by generating synthetic labels through a trained model and employing a consistency-check method to assess the quality of these labels. Experimental results demonstrate that AiDE-Q achieves up to a 14.2% improvement in model performance when predicting entanglement entropy and correlation in the Heisenberg XXZ model and cluster Ising model with up to 50 qubits.\n\nSTRENGTHS AND WEAKNESSES:\nThis paper is overall a strong paper. Using quantum data to help train machine learning models is a popular idea. However, the effectiveness of these models will likely be limited even with the advent of fault-tolerant quantum computers. That's because access to quantum hardware will likely remain limited for the foreseeable future as building and operating quantum computers will continue to be very expensive. Having methods for improving the training sets of DL models for estimating the ground state properties will be critical.\n\nThe authors provide strong (given the length constraints of NeurIPS) evidence that AiDE-Q does improve model training. The authors ran comparative studies using a number of state-of-the-art DL approaches to ground state property estimation, showing improvement in most cases with AiDE-Q. \n\nAiDE-Q is conceptually simple, which helps streamline the paper's presentation. The authors do a good job at contextualizing their work and surveying the subfield of DL-based approaches to ground state property estimation.\n\n**Weaknesses:** It would be nice to see simulations done on noisy quantum computers. The noise models can be reflective of early fault-tolerant devices (<10^-3), Pauli stochastic errors, some very good qubits, etc. Otherwise it is hard to evaluate AiDE-Q's short-to-medium-term relevance to the community.\n\nI didn't really spot many weaknesses outside of a typo or two. \"Basic of quantum computing\" should be \"Basics of quantum computing.\"\n\nQUESTIONS:\nWhat happens when you add in noise?\n\nLIMITATIONS:\nNo analysis is done in the presence of noise.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}