Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session X32: Quantum Machine Learning IIILive

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Sponsoring Units: DQI GDS Chair: Guillaume Verdon, Google 
Friday, March 19, 2021 8:00AM  8:12AM Live 
X32.00001: Branching Quantum Convolutional Neural Networks: A Variational Ansatz with MidCircuit Measurements Ian MacCormack, Conor Delaney, Alexey Galda, Prineha Narang We introduce the bQCNN, a variation of the quantum convolutional neural network (QCNN) in which outcomes from midcircuit measurements of subsets of qubits inform subsequent quantum gate operations. This leads to a classical branching structure in which each branch contains its own set of trainable parameterized entangling gates, resulting in significantly more parameters as compared to a standard QCNN circuit of the same depth. We demonstrate classification tasks in which the bQCNN significantly outperforms a comparable QCNN of the same circuit depth. Using results from noisy simulations, we discuss the advantages that midcircuitmeasurement based circuits can offer as variational ansätze in NISQ devices. 
Friday, March 19, 2021 8:12AM  8:24AM Live 
X32.00002: Learning local and nonlocal quatum data via generative model over tensor network architechture Khadijeh Najafi, Ahmadreza Azizi, Miles Stoudenmire, Xun Gao, Mikhail Lukin, Susanne F Yelin, Masoud Mohseni Nonlocality lies at the heart of many striking features of quantum states such as entanglement. The GreenbergerHorneZeilinger (GHZ) states and Cluster states are known as an important category of highly entangled quantum states. They play key roles in various quantumbased technologies and are particularly of interest in benchmarking noisy quantum hardwares. A novel quantum inspired generative model known as Born Machine which leverages on probabilistic nature of quantum physics has shown a great success in learning classical and quantum data over tensor network (TN) architecture. To this end, we investigate the training of the Born Machine for learning both local and nonlocal data encoded in GHZ and Cluster states over various tensor network architectures. Our result indicates that gradientbased training schemes over TN Born Machine fails to learn the nonlocal information of the coherent superposition (or parity) of the GHZ state. Finally, we adapt a gradient free training algorithm similar to Density Matrix Renormalization Group. This opens a new direction of adapting quantum inspired gradient free training schemes in learning highly entangled and other exotic quantum states. 
Friday, March 19, 2021 8:24AM  8:36AM Live 
X32.00003: A few examples of Machine Learning and Artificial Neural Networks applied to Quantum Physics Franco Nori Machine learning provides effective methods for identifying topological features [1]. We show that unsupervised manifold learning can successfully retrieve topological quantum phase transitions [1]. We have also developed [2] machine learninginspired quantum state tomography based on neuralnetwork representations of quantum states. We also consider conditional generative adversarial networks (CGANs) to QST [3]. We demonstrate [4] that artificial neural networks can simulate firstprinciples calculations of extended materials. 
Friday, March 19, 2021 8:36AM  8:48AM Live 
X32.00004: Quantumenhanced data classification with a variational entangled sensor network Yi Xia, Wei Li, Quntao Zhuang, Zheshen Zhang Variational quantum circuits (VQCs) built upon noisy intermediatescale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning. However, the required VQC depth to demonstrate a quantum advantage overclassical schemes is beyond the reach of available NISQ devices. Supervised learning assisted by an entangledsensor network (SLAEN) is a distinct paradigm that harnesses VQCs trained by classical machine learning algorithms to tailor multipartite entanglement shared by the sensors for solving practically useful data processing problems. Here, we report the first experimental demonstration of SLAEN and show an entanglementenabled reduction in the error probability for classification of multidimensional radiofrequency signals. Our work paves a new route for quantumenhanced data processing and its applications in the NISQ era. 
Friday, March 19, 2021 8:48AM  9:00AM Live 
X32.00005: Classical variational simulation of the Quantum Approximate Optimization Algorithm Matija Medvidović, Giuseppe Carleo A key open question in quantum computing is whether quantum algorithms can offer significant advantage over classical algorithms for tasks of practical interest. Probing classical computing limits in simulating quantum systems is one important route to address this question. We introduce a method to classically simulate quantum circuits made of several layers of parameterized gates, a key component of variational algorithms suitable for nearterm quantum computers. Our approach is based on a neuralnetwork parameterization of the manyqubit wave function, focusing on states relevant for the Quantum Approximate Optimization Algorithm (QAOA). We reach 54 qubits and QAOA depth of 4 without requiring largescale computational resources. When possible, we compare obtained states with outputs of exact simulators and find good approximations for cost function values and state vectors. For larger qubit counts, our approach provides accurate QAOA simulations at previously unexplored regions of its parameter space, and to benchmark the next generation of experiments in the Noisy IntermediateScale Quantum era. (arXiv:2009.01760) 
Friday, March 19, 2021 9:00AM  9:12AM Live 
X32.00006: Using Reinforcement Learning for Quantum Control in Magnetic Resonance Will Kaufman, Benjamin Alford, Pai Peng, Xiaoyang Huang, Paola Cappellaro, Chandrasekhar Ramanathan Robust control of a quantum system is fundamental to studying those systems or performing quantum simulation or computation. Reinforcement learning (RL) offers promising alternatives to existing methods for quantum control. We compare RL algorithms to gradient ascent pulse engineering (GRAPE) for both statetostate transfer operations as well as the design of desired unitary operations on single and twoqubit systems. GRAPE algorithms perform well when the system Hamiltonian is wellknown, and when any uncertainties can be well parametrized a priori. On the other hand, RL algorithms, by treating the system’s dynamics as a black box and only receiving partial observations and reward signals from the system, have the potential to provide robust control of larger systems with more complex sources of error. The application of RL to Hamiltonian engineering of manyspin systems for quantum simulation and sensing is also considered. 
Friday, March 19, 2021 9:12AM  9:24AM Live 
X32.00007: Machine LearningDerived Entanglement Witnesses Eric Zhu, Larry T. H. Wu, Li Qian Recent studies of the classification of entangled states have utilized aspects of machine learning such as neural networks. However, the number of features (or observables) taken as input into such systems required to provide correct inference often grow to the number required for full state tomography. 
Friday, March 19, 2021 9:24AM  9:36AM Live 
X32.00008: Unsupervised machine learning quantum dynamics Matthew Choi, Daniel FlamShepherd, Thi Ha Kyaw, Alan AspuruGuzik As we enter the age of Artificial Intelligence (AI) and Noisy IntermediateScale Quantum Computing (NISQ), there has been an increased interest in applying machine learning models to quantum physics. Most applications of AI in quantum mechanics use supervised learning models such as Recurrent Neural Networks (RNN) to predict various quantum properties. However, these models are essentially performing curve fitting and have not learned the underlying dynamics found in a quantum system. In this talk, we describe the application of generative models using neural ODEs to quantum dynamics, which we show, can learn the underlying quantum dynamics and can extrapolate well beyond the training regime when performing reconstructions. Furthermore, random samples from the model satisfy the Heisenberg uncertainty principle. We apply our model to closed and open quantum system dynamics, showing that the model can distinguish between the dynamics of pure and mixed states. We demonstrate, for each hamiltonian it has trained on, the model learns an interpretable representation of the Hilbert space. 
Friday, March 19, 2021 9:36AM  9:48AM Live 
X32.00009: Quantum adiabatic machine learning with zooming Alexander Zlokapa, Alex Mott, Joshua Job, JeanRoch Vlimant, Daniel Lidar, Maria Spiropulu Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to stateoftheart machine learning methods with a specific application to Higgs boson classification. We propose QAMLZ, a novel algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAMLZ matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the ROC curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a new class of problems that can be solved by quantum annealers and suggests the approach in performance of nearterm quantum machine learning towards classical benchmarks. 
Friday, March 19, 2021 9:48AM  10:00AM Live 
X32.00010: Convolutional Neural Networks and Symmetries of Quantum 1D Spin Chains Shah Saad Alam, Yilong Ju, Jonathan Minoff, Fabio Anselmi, Ankit Patel, Han Pu

Friday, March 19, 2021 10:00AM  10:12AM Not Participating 
X32.00011: Deep Quantum Control: Endtoend quantum control using deep learning algorithms Omid Khosravani The expected speedup of quantum algorithms on nearterm quantum processors as well as the resource requirements for achieving quantum faulttolerance relies on the fidelity of gate operations on available qubits. Traditionally, quantum control and mitigation protocols have been obtained via optimizing control trajectories by assuming simple a priori error models that impact the qubits, or the amplitude, frequency, and phase of the driving control fields. Although these models have been instrumental in achieving individual 1,2qubit gates with average fidelities of 9999.9%, however various sources of noise get mixed up and accumulated in running quantum circuits with larger numbers of qubits. Here we propose an "endtoend" framework, which instead starts from direct experimental observations to obtain optimal quantum control trajectories that are sufficiently resilient to all sources of errors. We achieve this by combining existing quantum control and characterization techniques with the representation and learning power of modern deep learning algorithms. We demonstrate our framework to achieve high fidelity 1,2qubit gates that are resilient to various sources of noise. 
Friday, March 19, 2021 10:12AM  10:24AM Live 
X32.00012: Unsupervised Learning of Physical Systems with Twodimensional Tensor Network Structures Ahmadreza Azizi, Khadijeh Najafi, Masoud Mohseni Learning underlying patterns with unsupervised generative models is a challenging task. Inspired by the probabilistic nature of quantum physics, Born Machines as generative models have shown great success in learning the joint probability distribution of a given dataset. Leveraging on the expressibility and training power of Projected Entangled Pair State (PEPS) networks, we study the capability of our Born Machine with PEPS structure in learning the underlying patterns in the classical Ising model and two dimensional Rydberg atom. Considering that PEPS models may not be easily extended to larger systems with higher bond dimensions, we also investigate the effect of more efficient tensor network contractions such as MPS snakelike and MPSMPO as well as random contractions on the performance of Born Machine. 
Friday, March 19, 2021 10:24AM  10:36AM Live 
X32.00013: PatternRecognition Training of a Quantum Neuron on a Quantum Computer London Cavaletto, Luca Candelori, Alex Matos Abiague The use of advanced quantum neuron models for pattern recognition applications require fault tolerant quantum coomputing. Such models are challenging to implement on currently available quantum processors due to noise introduced by nonlocal operations. We propose an alternative quantum perceptron (QP) model that uses a reduced number of multiqubit gates and is less susceptible to quantum errors than other existing models. We demonstrate the performance of the proposed model through an implemention of the QP on a few qubits using an actual quantum computer. The proposed QP uses an Nary encoding of the binary input data characterizing the patterns. We develop various hybrid (quantumclassical) training procedures for simulating the learning process of the QP and test their efficiency. We also provide a comparative analysis of the required quantum error corrections for scalability. 
Friday, March 19, 2021 10:36AM  10:48AM On Demand 
X32.00014: Explainable Natural Language Processing with Matrix Product States Pradeep Bhadola, Jirawat Tangpanitanon, Chanatip Mangkang, Yuichiro Minato, Dimitris Angelakis, thiparat chotibut Deep Learning (DL) has empowered computers with superior performance in modern Natural Language Processing (NLP) tasks, such as sentiment analysis and machine translation. Even for texts with longrange correlations such as sequences of characters in Wikipedia, DL can effectively express the powerlaw decay in the mutual information between two distant characters [1]. Despite empirical successes, its intrinsic nonlinearity complicates the analysis of algorithmic behaviours. Which network architectures and how many parameters are essential to reproduce longrange correlations are important yet theoretically challenging questions to tackle. Here, we attempt to provide systematic answers through the mapping between DL and its matrix product state (MPS) counterpart [2]. By recasting DL as MPS, we show that the number of parameters required to achieve high performance in sentiment analysis, and to reproduce powerlaw decay in the mutual information in Wikipedia texts, can be efficiently extracted from the entanglement entropy in the dual MPS. Our work utilises tools in manybody quantum physics to resolve explainability issues of NLP, and more generally of sequence modelling. 
Friday, March 19, 2021 10:48AM  11:00AM On Demand 
X32.00015: Machinelearning tools for rapid control, calibration and characterization of QPUs and other quantum devices Nicolas Wittler, Federico Roy, Kevin Pack, Max Werninghaus, Anurag Saha Roy, Daniel Egger, Stefan Filipp, Frank K Wilhelm, Shai Machnes The principal limiting factor in scaleup of quantum computers is not the number of qubits, but the entangling gate infidelity. Current QPU bringup relies on a large number of tailored routines to extract individual model parameters (characterization), and the huge effort required inevitably this leads to incomplete characterization, partial insight into the sources of error, and threfore slow progress in improving gate fidelities. 
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