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

许多关键的人工智能在健康科学中的应用没有足够的实用数据。即使收集了令人难以置信的大量数据，由于每个患者的独特情况(健康状况)，这些数据也存在着显著的差距。对规模可观的人工智能系统进行此类数据的训练，往往会导致输出结果要么无法接受，无法做出决定性的医学声明，要么系统的表现并不比传统方法好。因此，该项目的目标是开发人工智能，特别是机器学习算法，这种算法可以快速训练，最大限度地减少错误，而且不需要大量的人类专业知识。该项目的新颖性在于利用了新兴的量子计算(QC)算法，这些算法提供了快速训练模型和快速找到更好解决方案的能力。在这个项目中，QC将被应用到机器学习中，以展示基于QC的方法在两个具有挑战性的应用中的有效性：(A)脑电信号上的癫痫检测和(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，并增加这项技术在医疗领域的影响，这将影响全球无数人类。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、信息科学和工程职业。在这个项目中，绝热量子退火法(QA)将被用来解决两个重要的计算挑战：(A)寻找全局最小值和(B)从复概率分布中采样。实验结果表明，与传统的参数优化方法相比，使用QA支持采样的训练方法可以找到更好的参数，并且克服了当前机器学习算法在脑电信号的癫痫检测和数字病理图像的自动解释等挑战性应用中的不足。通过这些发展，预计该项目还将证明，广泛的配置空间(用各种与应用相关的数据训练的无向概率图形模型)具有“在概率分布中很难找到局部山谷”的属性，可以很容易地用质量保证进行抽样。这些发现将被应用于深度生成模型，以获得更好的分类和模式重建精度。QA能够到达配置空间中难以采样的区域，这将使许多机器学习应用程序受益。该项目由新兴技术基金会(FET)和既定的激励竞争研究计划(EPSCoR)联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.