FET: Medium: A Quantum Computing Based Approach to Undirected Generative Machine Learning Models

健康科学中许多关键的人工智能应用没有足够的实际数据。即使收集了大量的数据，由于每个病人的独特情况（健康状况），数据也存在很大的差距。在这类数据上训练规模庞大的人工智能系统，其结果要么无法用于做出决定性的医疗声明，要么系统的表现并不比传统方法更好。因此，该项目的目标是开发人工智能，特别是快速训练、最小化错误、不需要大量人类专业知识的机器学习算法。该项目的新颖之处在于利用新兴的量子计算（QC）算法，提供快速训练模型的潜力，并能够快速找到更好的解决方案。在这个项目中，QC将应用于机器学习，以证明基于QC的方法在两个具有挑战性的应用中的有效性：(a)脑电图信号的癫痫检测和(b)数字病理图像的自动解释。积极影响这两个高级应用程序将使自动化系统接近领域专家（人类）的性能，并增加该技术在医疗领域的影响，这将影响全球无数人。获得最高水平的QC研究将创造职业发展机会，鼓励高中生追求计算机和信息科学与工程职业。在本项目中，绝热量子退火（QA）将用于解决两个重要的计算挑战：(a)找到全局最小值和(b)从复杂概率分布中抽样。我们将证明，利用qa支持采样的训练可以找到比传统参数优化方法更好的参数，并且它还克服了当前机器学习算法在具有挑战性的应用中的不足，例如脑电图信号的癫痫检测和数字病理图像的自动解释。通过这些发展，本项目还有望证明，具有“难以在概率分布中找到局部谷值”的特性的广泛配置空间（用各种应用相关数据训练的无向概率图形模型）可以很容易地用QA进行采样。这些发现将应用于深度生成模型，以获得更高的分类和模式重建精度。QA达到配置空间中难以采样区域的能力将使许多机器学习应用受益。该项目由新兴技术基金会（FET）和促进竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many crucial artificial intelligence applications in health sciences do not have sufficient practical data. Even when one collects incredible amounts of data, the data has significant gaps because of the unique circumstances (health conditions) of each patient. Training sizeable artificial intelligence systems on such data often results in an output that is either unacceptable for making decisive medical pronouncements or the systems do not perform any better than conventional methodologies. As such, the project's goal is to develop artificial intelligence, particularly machine learning algorithms that train rapidly, minimize errors, and do not require significant human expertise. The project's novelty is utilizing emerging quantum computing (QC) algorithms that offer the potential for rapid training of models and the ability to find better solutions quickly. In this project, QC will be applied to machine learning to demonstrate the efficacy of QC-based methods in two challenging applications: (a) seizure detection on encephalography signals and (b) automatic interpretation of digital pathology images. Positively impacting the two high-level applications will allow automated systems to approach domain expert (human) performance and increase the impact of this technology in the medical field, which will impact countless humans worldwide. Access to the highest levels of QC research will create career development opportunities, encouraging high schoolers to pursue computer and information science and engineering careers. In this project, adiabatic quantum annealing (QA) will be used to solve two significant computational challenges: (a) finding a global minimum and (b) sampling from complex probability distributions. It will be demonstrated that training that utilizes QA-supported sampling can find better parameters than conventional parameter optimization approaches, and it also overcomes the deficiencies of current machine learning algorithms in challenging applications, such as seizure detection on encephalography signals and automatic interpretation of digital pathology images. Through these developments, it is also expected of this project to demonstrate that a wide range of configuration spaces (undirected probabilistic graphical models trained with a variety of application-relevant data) that have the property of "difficult to find local valleys in the probability distribution" to be easily sampled with QA. These findings will be applied to deep generative models for superior classification and pattern reconstruction accuracy. The ability of QA to reach difficult to sample regions of the configuration space will benefit many machine learning applications.This project is jointly funded by Foundations of Emerging Technologies (FET) and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.