Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session E27: Quantum Machine Learning IFocus

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Sponsoring Units: DQI DQI Chair: Daniel Kyungdeock Park, KAIST Room: BCEC 160C 
Tuesday, March 5, 2019 8:00AM  8:12AM 
E27.00001: Improved Training of Quantum Boltzmann Machines Eric Anschuetz, Yudong Cao Quantum Boltzmann machines (QBMs) are a natural quantum generalization of restricted Boltzmann machines (RBMs) that, at least under numerical simulation, perform better than their classical counterparts in learning generic data distributions. However, training QBMs using gradientbased methods requires sampling observables in quantum thermal distributions, a problem that generically is NPhard. In this work, we find that the locality of the gradient observables that must be sampled gives rise to an efficient sampling method based on the Eigenstate Thermalization Hypothesis (ETH), and thus an efficient method for training QBMs on quantum devices. Furthermore, we demonstrate a hybrid gradientbased/black box optimization procedure that outperforms strictly gradientbased training methods. 
Tuesday, March 5, 2019 8:12AM  8:24AM 
E27.00002: Measurementbased adaptation protocol with quantum reinforcement learning Lucas Lamata, Francisco AlbarrÃ¡nArriagada, Juan Carlos Retamal, Enrique Solano Machine learning employs dynamical algorithms that mimic the human capacity to learn, where the reinforcement learning ones are among the most similar to humans in this respect. On the other hand, adaptability is an essential aspect to perform any task efficiently in a changing environment, and it is fundamental for many purposes, such as natural selection. Here, we propose an algorithm based on successive measurements to adapt one quantum state to a reference unknown state, in the sense of achieving maximum overlap. The protocol naturally provides many identical copies of the reference state, such that in each measurement iteration more information about it is obtained. In our protocol, we consider a system composed of three parts, the "environment" system, which provides the reference state copies; the register, which is an auxiliary subsystem that interacts with the environment to acquire information from it; and the agent, which corresponds to the quantum state that is adapted by digital feedback with input corresponding to the outcome of the measurements on the register. F. AlbarrÃ¡nArriagada, J. C. Retamal, E. Solano, and L. Lamata, Phys. Rev. A 98, 042315 (2018). 
Tuesday, March 5, 2019 8:24AM  8:36AM 
E27.00003: Improving training of Boltzmann machines with error corrected quantum annealing Richard Li, Daniel A Lidar Boltzmann machines, a class of machine learning models, are the basis of several deep learning methods that have been successfully applied to both supervised and unsupervised machine learning tasks. Quantum annealing may help lead to future advances in the development of these learning algorithms, but its usefulness is determined in part by the effective temperature. We have applied nested quantum annealing correction (NQAC) to do unsupervised learning with a small bars and stripes (BAS) dataset, and to a coarsegrained MNIST dataset, which consists of blackandwhite images of handwritten integers, to do supervised learning. For both datasets, we demonstrate an effective temperature reduction with NQAC that leads to an increase in learning performance. We also find better performance overall with longer annealing times and offer some interpretation of the results based on comparison to simulated quantum annealing (SQA) simulations. 
Tuesday, March 5, 2019 8:36AM  9:12AM 
E27.00004: Opportunities and Challenges in QuantumAssisted Machine Learning Invited Speaker: Alejandro Perdomo With quantum computing technologies nearing the era of commercialization and quantum advantage, machine learning (ML) has been proposed as one of the promising ``killer" applications. Despite significant effort, there has been a disconnect between most quantum ML proposals, the needs of ML practitioners, and the capabilities of nearterm quantum devices towards a conclusive demonstration of a meaningful quantum advantage in the near future. In this talk, we provide concrete examples of intractable ML tasks that could be enhanced with nearterm devices. We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semisupervised learning, instead of the popular and more tractable supervised learning tasks. We focus on hybrid quantumclassical approaches and illustrate some of the key challenges we foresee for nearterm implementations. 
Tuesday, March 5, 2019 9:12AM  9:24AM 
E27.00005: Quantum generative adversarial learning in a superconducting quantum circuit Hu Ling, shuhao wu, Weizhou Cai, Yuwei Ma, Xianghao Mu, Yuan Xu, Haiyan Wang, Yipu Song, DongLing Deng, ChangLing Zou, Luyan Sun Generative adversarial learning is one of the most exciting recent breakthroughs in machine learninga subfield of artificial intelligence that is currently driving a revolution in many aspects of modern society. It has shown splendid performance in a variety of challenging tasks such as image and video generations. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to possess the potential of exhibiting an exponential advantage over its classical counterpart. Here, we report the first proofofprinciple experimental demonstration of quantum generative adversarial learning in a superconducting quantum circuit. We demonstrate that, after several rounds of adversarial learning, a quantum state generator can be trained to replicate the statistics of the quantum data output from a digital qubit channel simulator, with a high fidelity (98.8% on average) that the discriminator cannot distinguish between the true and the generated data. Our results pave the way for experimentally exploring the intriguing longsoughtafter quantum advantages in machine learning tasks with noisy intermediatescale quantum devices. 
Tuesday, March 5, 2019 9:24AM  9:36AM 
E27.00006: Noncommutative Boltzmann Machines Mark Novotny Building on the 2018 paper on quantum Boltzmann Machines (qBM) by Amin et al [1], the concept of noncommutative Boltzmann Machines (ncBM) is introduced. ncBM contain qBM as a subset, but can be viewed, for example, as machine learning with superoperators. In particular, we study ncBM with the Liouvillian superoperator, and show the negative phase of machine learning becomes easy to calculate in a particular limit. Both Bernoulli data sets [1] and quantum dragon datasets [2] are utilized for both generative and discriminative learning. Possibilities of using nearterm adiabatic quantum annealing machines for ncBM will be discussed. 
Tuesday, March 5, 2019 9:36AM  9:48AM 
E27.00007: Hybrid quantumclassical schemes for generative adversarial learning: HQGANs Jhonathan Romero, Alan AspuruGuzik Quantum computing and machine learning are two fastgrowing research areas. Recently, these two areas have been merged into the field of quantum machine learning (QML), seeking to find ways in which quantum computers can offer advantages at solving machine learning problems over classical computers. Here, we propose to use quantum computers to learn models that mimic observed data distributions, a type of task known as generative learning, by substituting neural networks with variational quantum circuits in the generative adversarial networks (GANs) framework. GANs are statistical models that learn to sample from an observed data distribution by looking at individual samples. They consist of two neural networks, known as the discriminator and the generator, competing against each other in a minimax game. We propose a hybridquantum classical scheme that trains two variational quantum circuits, playing the role of discriminator and generator, to perform the same task on classical data. The proposed hybrid quantum GANs (HQGANs) might benefit machine learning by improving the ability to model more complex data distributions and could offer a new niche of applications for nearterm quantum computers. 
Tuesday, March 5, 2019 9:48AM  10:00AM 
E27.00008: Variational circuits for machine learning with nearterm devices Maria Schuld Variational circuits are parameterdependent quantum algorithms that can be optimized for a certain task. One approach in quantum machine learning is to interpret these circuits as machine learning models that can be trained to generalise from data. Such models are often refered to as variational quantum classifiers. This talk will focus on various issues around this approach, for example how to think about the power of variational quantum classifiers, how we can train them and what they might be good for. 
Tuesday, March 5, 2019 10:00AM  10:12AM 
E27.00009: Quantum Manifold Learning Algorithms for Dimensionality Reduction Xi He, Li Sun, Xiaokai Hou, Xiaoting Wang Manifold learning is a kind of method which discusses the machine learning problems under the manifold hypothesis. It assumes that the sampled highdimensional data actually comes from the embedding of some lowdimensional manifold structure. Manifold learning has wide range of applications in dimensionality reduction and data visualization. In the field of manifold learning, two most representative and commonly used algorithms are isometric mapping and locally linear embedding. Using techniques of quantum computing, we research out two quantum algorithms in correspondence to them. Compared with corresponding classical algorithms, the two quantum algorithms proposed in this paper can be implemented on a quantum computer with quantum speedup. Quantum isometric mapping provides at least quadratic acceleration and quantum locally linear embedding takes logarithmic resources. In addition, we attempt to find out a common process to deal with quantization of manifold learning algorithms. 
Tuesday, March 5, 2019 10:12AM  10:24AM 
E27.00010: Differentiable Quantum Circuits and Generative Modeling JinGuo Liu, Lei Wang We present a fresh approach to quantum machine learning by using quantum circuits as probabilistic generative models. The proposed QCBM overcomes the challenging problem in training implicit density models with discrete outputs in deep learning. The key component of our gradientbased learning algorithm is to measure the gradient of the two sample test loss function on a quantum computer unbiasedly and efficiently. With the inspiration of matrix product state, we are able to train a Born machine to generate intermediatescale images with number of qubits much less than pixel numbers. 
Tuesday, March 5, 2019 10:24AM  10:36AM 
E27.00011: Machinelearned QCVV for distinguishing singlequbit noise Travis Scholten, YiKai Liu, Kevin Young, Robin BlumeKohout We investigate the use of machine learning (ML) algorithms for developing new QCVV protocols. ML algorithms learn approximations to functions that relate experimental data to some property of interest. As an example, we show ML algorithms can successfully learn separating surfaces for distinguishing coherent and stochastic noise affecting a single qubit. The performance of various ML algorithms depends strongly on the geometry of experimental data (in this case, data from gate set tomography experiments). We show performance can be boosted by hyperparameter tuning and feature engineering. 
Tuesday, March 5, 2019 10:36AM  10:48AM 
E27.00012: Quantum optical neural networks for next generation quantum information processing Gregory R Steinbrecher, Jonathan Olson, Dirk R. Englund, Jacques Carolan Physically motivated quantum algorithms for specific nearterm quantum hardware will likely be the next frontier in quantum information science. Here, we show how many of the features of neural networks for machine learning can naturally be mapped into the quantum optical domain by introducing the quantum optical neural network (QONN). Through numerical simulation and analysis we train the QONN to perform a range of quantum information processing tasks, including protocols for quantum optical state compression, reinforcement learning, and blackbox quantum simulation. Our results indicate QONNs are a powerful design tool for quantum optical systems and a promising architecture for next generation quantum processors. 
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