摘要
Robotics&Machine Learning Daily News的一位新闻记者兼新闻编辑每日新闻-像扩散模型s这样的生成模型是机器学习(ML)中最重要的最新发展之一，它与稳定扩散和dall.e模型一起革命性地改变了图像基因比率领域。这些模型能够根据某些文本描述产生高质量的图像。“我们为量子计算机编程的新模型也是这样做的，但它不是生成图像，而是根据要执行的量子操作的文本描述生成量子电路”，内布拉斯加州因斯布鲁克大学理论物理系的戈尔卡·穆诺兹-吉尔·F罗姆解释说，为了准备某种量子状态或在量子计算机上执行算法，要实现这种运算，需要找到合适的量子门序列，虽然这在经典计算中相当容易，但由于量子世界的特殊性，这在量子计算中是一个很大的问题。最近，许多科学家提出了用各种机器学习方法来构造量子电路的方法，然而，由于机器学习时必须模拟量子电路，因此对这些ML模型的训练往往非常困难。扩散模型避免了这些问题，因为它们是如何训练的。戈尔卡·穆诺兹-吉尔解释说，“这提供了一个巨大的优势”，他与汉斯·J·布里格尔和弗洛里安·弗鲁特一起开发了这种新方法。“此外，”W E表明，去噪扩散模型在生成方面是准确的，而且非常灵活，允许生成具有不同数量的量子比特以及量子门的类型和数量的电路。这些模型还可以被定制以准备考虑量子硬件连通性的电路，即量子比特如何在量子计算机中连接。Gorka Munoz-gil称，“由于一旦模型被训练，制造新电路非常便宜，人们可以利用它来发现感兴趣的量子运算的新见解”。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Generative models like diffusion model s are one of the most important recent developments in Machine Learning (ML), wi th models as Stable Diffusion and Dall.e revolutionizing the field of image gene ration. These models are able to produce high quality images based on some text description. “Our new model for programming quantum computers does the same but, instead of generating images, it generates quantum circuits based on the text d escription of the quantum operation to be performed”, explains Gorka Munoz-Gil f rom the Department of Theoretical Physics of the University of Innsbruck, Austri a. To prepare a certain quantum state or execute an algorithm on a quantum computer , one needs to find the appropriate sequence of quantum gates to perform such op erations. While this is rather easy in classical computing, it is a great challe nge in quantum computing, due to the particularities of the quantum world. Recen tly, many scientists have proposed methods to build quantum circuits with many r elying machine learning methods. However, training of these ML models is often v ery hard due to the necessity of simulating quantum circuits as the machine lear ns. Diffusion models avoid such problems due to the way how they are trained. “T his provides a tremendous advantage”, explains Gorka Munoz-Gil, who developed th e novel method together with Hans J. Briegel and Florian Furrutter. “Moreover, w e show that denoising diffusion models are accurate in their generation and also very flexible, allowing to generate circuits with different numbers of qubits, as well as types and numbers of quantum gates.” The models also can be tailored to prepare circuits that take into consideration the connectivity of the quantum hardware, i.e. how qubits are connected in the quantum computer. “As producing new circuits is very cheap once the model is trained, one can use it to discover new insights about quantum operations of interest”, Gorka Munoz-Gil names anoth er potential of the new method.
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