Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session K27: Quantum Machine Learning IIFocus

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Sponsoring Units: DQI Chair: Maria Schuld, University of KwaZuluNatal Room: BCEC 160C 
Wednesday, March 6, 2019 8:00AM  8:12AM 
K27.00001: The Impact of Quantum Noise in Neuromorphic Systems Gerasimos Angelatos, Hakan Tureci The quantum dynamics of drivendissipative systems with a stable classical fixedpoint are well understood, however dynamics far from equilibrium and in regimes without a fixedpoint are less studied. Considerable theoretical challenges arise because such situations often involve strong transient excitation while the role of quantum fluctuations remains significant. This scenario is realized in neuromorphic optical systems which respond to a weak input above threshold with a robust large amplitude pulse. The quantum dynamics of neuromorphic systems can thus not be described by the standard small fluctuation expansion around a classical steadystate, and a full quantum modeling is out of question due to large transients. We present a general theoretical approach based on quantum stochastic differential equations which captures quantum noise about classical trajectories and apply it to two physical realizations of neuromorphic dynamics: an excitable laser and a superconducting circuit. Contrary to previous findings, fundamental quantum noise drives large fluctuations in pulse response times, while the amplitude response remains robust. In addition, quantum noise softens the bifurcation to a selfsustained pulsation regime by exciting the system in the absence of an input. 
Wednesday, March 6, 2019 8:12AM  8:24AM 
K27.00002: Performance of the Quantum Approximate Optimization Algorithm on the Maximum Cut Problem Gavin Crooks, Nicholas C Rubin The Quantum Approximate Optimization Algorithm (QAOA) is a promising approach for programming a nearterm gatebased hybrid quantum computer to find good approximate solutions of hard combinatorial problems. However, little is currently know about the capabilities of QAOA, or of the difficulty of the requisite parameters optimization. We explore these issues with the aid of QuantumFlow, a simulation of a gate based quantum computer that uses TensorFlow to rapidly optimize variational quantum circuits. Our investigations support the prospects that QAOA will be an effective method for solving interesting problems on nearterm quantum computers 
Wednesday, March 6, 2019 8:24AM  8:36AM 
K27.00003: Machine Learning Detection of Bell Nonlocality in Quantum ManyBody Systems DongLing Deng Machine learning, the core of artificial intelligence, is one of today's most rapidly growing interdisciplinary fields. Recently, its tools and techniques have been adopted to tackle intricate quantum manybody problems. In this talk, I will introduce machine learning techniques to the detection of quantum nonlocality in manybody systems, with a focus on the restrictedBoltzmannmachine (RBM) architecture. Using reinforcement learning, I will demonstrate that RBM is capable of finding the maximum quantum violations of multipartite Bell inequalities with given measurement settings. This result builds a novel bridge between computersciencebased machine learning and quantum manybody nonlocality, which will benefit future studies in both areas. 
Wednesday, March 6, 2019 8:36AM  8:48AM 
K27.00004: Benchmarking superconducting qubits with generative model learning Kathleen Hamilton, Eugen Dumitrescu, Holly Stemp, Raphael Pooser Our work is focused on the identification and development of simple machine learning tasks that can act as hardware benchmarks to compare the relative performance of NISQ devices. Using MMD training and stochastic optimization of circuit parameters, we show how a recently introduced class of generative models (the Quantum Circuit Born Machine [1]) can quantify the performance of noisy superconducting qubits. We identify three sources of error that limit the performance of these models on noisy qubits: decoherence, gate fidelities and measurement errors. We construct several shallow depth circuit ansatz and using metrics which are related to fidelity we demonstrate how different errors affect model performance. We also investigate the effect of applying error mitigation to the final trained circuit versus incorporating error mitigation into the circuit training workflow. 
Wednesday, March 6, 2019 8:48AM  9:00AM 
K27.00005: Localmeasurementbased quantum state tomography via neural networks Bei Zeng Quantum state tomography is a daunting challenge of experimental quantum computing even in moderate system size. One way to boost the efficiency of state tomography is via local measurements on reduced density matrices, but the reconstruction of the full state thereafter is hard. Here, we present a machine learning method to recover the full quantum state from its local information, where a fullyconnected neural network is built to fulfill the task with up to seven qubits. In particular, we test the neural network model with a practical dataset, that in a 4qubit nuclear magnetic resonance system our method yields global states via 2local information with high accuracy. Our work paves the way towards scalable state tomography in large quantum systems. 
Wednesday, March 6, 2019 9:00AM  9:12AM 
K27.00006: Variational Quantum Neural Programming PierreLuc DallaireDemers Variational algorithms used for quantum simulations are naturally resistant to some errors and are therefore well suited for NISQ devices. Their application in quantum machine learning has yielded methods for data classification, compression and generation such as the quantum autoencoder. Using inspiration from neural programming in classical machine learning, we show how a quantum program can be learned through gradient descent. Quantum programs are usually defined operationally over a variable number of qubits while variational quantum algorithms are typically meant to operate on fixedsize quantum registers. We define a class of differentiable ansatz that can operate on an arbitrary number of qubits and be used to reproduce algorithms such as the quantum Fourier transform and phase estimation using only a set of examples on few qubits. We generalize this class of ansatz to explore the space of shallow algorithms. 
Wednesday, March 6, 2019 9:12AM  9:48AM 
K27.00007: Quantum machine learning: Challenges and Opportunities Invited Speaker: Leonard Wossnig In this talk I will pose the general framework of learning and then introduce the different topics which jointly define the area of quantum machine learning. Since machine learning is a intrinsically data driven approach, dependencies and assumptions play a major role. I will therefore introduce different input and output assumptions and discuss corresponding data access models before giving a high level explanation of the different techniques which have been proposed. I will finally discuss current and future challenges and opportunities of the field. 
Wednesday, March 6, 2019 9:48AM  10:00AM 
K27.00008: A Universal Training Algorithm for Quantum Deep Learning Guillaume Verdon, Jason Pye, Michael Broughton Quantum variational algorithms have seen a recent surge of interest, yet their connection to classical deep neural networks has so far remained elusive. In this talk, we will establish how to port over classical neural networks as quantum parametric circuits, and we will further introduce a quantumnative backpropagation principle which can be leveraged to train any quantum parametric network. We will present two main quantum optimizers leveraging this quantum backpropagation principle: Quantum Dynamical Descent (QDD), which uses quantumcoherent dynamics to optimize network parameters, and Momentum Measurement Gradient Descent (MoMGrad), which is a quantumclassical analogue of QDD. We will briefly cover multiple applications of QDD/MoMGrad to various problems of quantum information learning, and how to use these optimizers to train classical neural networks in a quantum fashion. Furthermore, we will show how to efficiently train hybrid networks comprised of classical neural networks and quantum parametric circuits, running on classical and quantum processing units, respectively. 
Wednesday, March 6, 2019 10:00AM  10:12AM 
K27.00009: Quantumclassical reinforcement parity learning from noisy classical data Daniel Kyungdeock Park, Jonghun Park, Suhwang Jeong, Jeongseok Ha, JuneKoo(KEVIN) RHEE Development of quantum algorithms for data analysis and machine learning has gained much attention recently. For practical applications of such algorithms in the big data era, the quantum advantage must be retained in noisy settings. One intriguing example in which the quantum algorithm outperforms the classical counterpart in the presence of noise is the problem of learning a parity function defined by a hidden bit string, known as learning parity with noise (LPN). However, a learner is most likely to receive noisy classical data, rather than noisy quantum data as considered in the original quantum LPN algorithm. Then, whether the quantum technique is still preferred remains an interesting open problem. Here, we present a quantumclassical reinforcement learning algorithm to solve the LPN problem efficiently for classical data. The algorithm uses classical training data to prepare an input quantum state suitable for the original quantum LPN algorithm. Based on the outcome of the quantum algorithm, a reward and an action are classically determined to update the input quantum state for the next learning cycle. Our method uses an exponentially smaller number of training samples than the direct application of the original quantum LPN algorithm to classical data. 
Wednesday, March 6, 2019 10:12AM  10:24AM 
K27.00010: Estimating quantum circuit probabilities and Hamiltonian properties using amplitude estimation Hammam Qassim Classical simulation of quantum systems is an old and wellmotivated problem. With the advent of quantum technologies this problem takes on new importance, as it becomes essential to have a toolkit for testing and debugging quantum circuits and devices. 
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