量子电路的设计目前由所讨论的量子算法的具体目标驱动。因此，这种方法依赖于量子算法设计者的大量人工努力，以便为任务设计合适的电路。然而，如果不以指数级增加电路设计工作量并引入不希望的归纳偏置，这种方法在未来无法扩展到更复杂的量子算法。受这一观察的启发，我们提出通过借鉴神经架构搜索（Neural Architecture Search，NAS）来自动化电路设计过程。在这项工作中，我们提出了两种 Quantum-NAS 算法，旨在针对特定量子任务找到高效电路。我们选择量子数据压缩作为驱动量子任务，并通过找到高效的自编码器设计来展示我们算法的性能，这些设计在三个不同任务——量子数据去噪、经典数据压缩和纯量子数据压缩——上优于基线。我们的结果表明，量子 NAS 算法可以显著减轻人工工作量，同时为任意给定任务提供高性能的量子电路。
英文摘要：
The design of quantum circuits is currently driven by the specific objectives of the quantum algorithm in question. This approach thus relies on a significant manual effort by the quantum algorithm designer to design an appropriate circuit for the task. However this approach cannot scale to more complex quantum algorithms in the future without exponentially increasing the circuit design effort and introducing unwanted inductive biases. Motivated by this observation, we propose to automate the process of cicuit design by drawing inspiration from Neural Architecture Search (NAS). In this work, we propose two Quantum-NAS algorithms that aim to find efficient circuits given a particular quantum task. We choose quantum data compression as our driver quantum task and demonstrate the performance of our algorithms by finding efficient autoencoder designs that outperform baselines on three different tasks - quantum data denoising, classical data compression and pure quantum data compression. Our results indicate that quantum NAS algorithms can significantly alleviate the manual effort while delivering performant quantum circuits for any given task.
In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest. In the past, NAS was hardly accessible to researchers without access to large-scale compute systems, due to very long compute times for the recurrent search and evaluation of new candidate architectures. The NAS-Bench-101 dataset facilitates a paradigm change towards classical methods such as supervised learning to evaluate neural architectures. In this paper, we propose a graph encoder built upon Graph Neural Networks (GNN). We demonstrate the eff…
Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder,…
In this paper, we present a novel neuroevolutionary method to identify the architecture and hyperparameters of convolutional autoencoders. Remarkably, we used a hypervolume indicator in the context of neural architecture search for autoencoders, for the first time to our current knowledge. Results show that images were compressed by a factor of more than 10, while still retaining enough information to achieve image classification for the majority of the tasks. Thus, this new approach can be used to speed up the AutoML pipeline for image compression.
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks
School of Systems Science, Beijing Normal University（北京师范大学系统科学学院）；School of System Design and Intelligent Manufacturing, Southern University of Science and Technology（南方科技大学系统设计与智能制造学院）；Department of Electrical and Computer Engineering, University of Illinois Chicago（伊利诺伊大学芝加哥分校电气与计算机工程系）
Recent approaches in the theoretical analysis of model-based deep learning architectures have studied the convergence of gradient descent in shallow ReLU networks that arise from generative models whose hidden layers are sparse. Motivated by the success of architectures that impose structured forms of sparsity, we introduce and study a group-sparse autoencoder that accounts for a variety of generative models, and utilizes a group-sparse ReLU activation function to force the non-zero units at a given layer to occur in blocks. For clustering models, inputs that result in the same group of active…
In this paper, we propose a winner-take-all method for learning hierarchical sparse representations in an unsupervised fashion. We first introduce fully-connected winner-take-all autoencoders which use mini-batch statistics to directly enforce a lifetime sparsity in the activations of the hidden units. We then propose the convolutional winner-take-all autoencoder which combines the benefits of convolutional architectures and autoencoders for learning shift-invariant sparse representations. We describe a way to train convolutional autoencoders layer by layer, where in addition to lifetime spars…
Text autoencoders are often used for unsupervised conditional text generation by applying mappings in the latent space to change attributes to the desired values. Recently, Mai et al. (2020) proposed Emb2Emb, a method to learn these mappings in the embedding space of an autoencoder. However, their method is restricted to autoencoders with a single-vector embedding, which limits how much information can be retained. We address this issue by extending their method to Bag-of-Vectors Autoencoders (BoV-AEs), which encode the text into a variable-size bag of vectors that grows with the size of the t…
NASA Ames Research Center（NASA艾姆斯研究中心）；KBR Inc（KBR公司）；Bristol University（布里斯托大学）；BAER Institute（BAER研究所）
Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the Quantum-assisted Variational Autoencoder has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity …
Searching for Quantum Effects in the Brain: A Bell-Type Test for Nonclassical Latent Representations in Autoencoders
School of Science, Zhejiang University of Science and Technology（浙江大学科学技术学院）；Department of Physics and Institute of Theoretical and Computational Physics, University of Crete（希腊克里特大学物理系和理论与计算物理研究所）；College of Life and Environmental Sciences, Hangzhou Normal University（杭州师范大学生命与环境科学学院）；School of Automation and Electrical Engineering, Zhejiang University of Science and Technology（浙江大学科学技术学院自动化与电气工程学院）；John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保罗森工程与应用科学学院）
Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach
Department of Physics, Sinop University（锡诺普大学物理系）；Institute for Quantum Materials and Technologies, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院量子材料与技术研究所）
We focus on using an architecture similar to the $β$-Variational Autoencoder ($β$-VAE) to discriminate if a quantum state is entangled or separable based on measurements. We split the data into two sets, the set of local and correlated measurements. Using the latent space, which is a low dimensional representation of the data, we show that restricting ourselves to the set of local data it is not possible to distinguish between entangled and separable states. Meanwhile, when considering both correlated and local measurements, an accuracy of over 80% is attained in the structure of the latent sp…
The lack of evidence for new interactions and particles at the Large Hadron Collider has motivated the high-energy physics community to explore model-agnostic data-analysis approaches to search for new physics. Autoencoders are unsupervised machine learning models based on artificial neural networks, capable of learning background distributions. We study quantum autoencoders based on variational quantum circuits for the problem of anomaly detection at the LHC. For a QCD $t\bar{t}$ background and resonant heavy Higgs signals, we find that a simple quantum autoencoder outperforms classical autoe…
