Bulletin of the American Physical Society
APS March Meeting 2018
Volume 63, Number 1
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session S28: Quantum Annealing: Algorithms & ApplicationsFocus

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Sponsoring Units: DQI Chair: Davide Venturelli, NASA/Ames Res Ctr Room: LACC 405 
Thursday, March 8, 2018 11:15AM  11:51AM 
S28.00001: Universal quantum computation in thermal equilibrium Invited Speaker: Elizabeth Crosson Adiabatic quantum computation (AQC) is a method for performing universal quantum computation in the ground state of a slowly evolving local Hamiltonian, and in an ideal setting AQC is known to capture all of the computational power of the quantum circuit model. However, despite having an inherent robustness to noise as a result of the adiabatic theorem and the spectral gap of the Hamiltonian, it remains a longstanding theoretical challenge to show that faulttolerant AQC can in principle be performed below some fixed noise threshold. There are many aspects to this challenge, including the difficulty of adapting known ideas from circuit model faulttolerance as well as the need to develop an error model that is appropriately tailored for open system AQC. In this talk I will introduce a scheme for combining FeynmanKitaev history state Hamiltonians with topological quantum error correction, in order to show that universal quantum computation can be encoded not only in the ground state but also in the finite temperature Gibbs state of a local Hamiltonian. Using only local interactions with bounded strength and a polynomial overhead in the number of qubits, the scheme is designed to serve as a proof of principle that universal AQC can be performed at nonzero temperature, and also to further our understanding of the complexity of highly entangled quantum systems in thermal equilibrium. 
Thursday, March 8, 2018 11:51AM  12:03PM 
S28.00002: Quantum Annealing on Glued Trees: Tunneling and Noise Siddharth Muthukrishnan, Tameem Albash, Daniel Lidar We analyze the quantum annealing algorithm for the gluedtrees problem [PRL, 109, 050501]. This is an oracle problem which has a provable exponential speedup over the best possible classical algorithm. We show that the quantum dynamics of the quantum anneal admit a tunneling description. We further analyze the effect of an additive random matrix noise model [PRA 71, 032330] on the annealing dynamics. We answer the question: How should the strength of the noise and the annealtime scale with problem size in order to obtain a sufficiently high probability of success? 
Thursday, March 8, 2018 12:03PM  12:15PM 
S28.00003: Fast quantum annealing protocol for Ising Hamiltonian with strong random field A. Baris Ozguler, Robert Joynt, Maxim Vavilov An individual qubit can be driven fast from one configuration of its Hamiltonian to another without generating transitions by choosing a proper path in the configuration space [M.V. Berry, J. of Physics A 42, 365303 (2009)]. We apply this technique to a system of qubits that is initially described by the noninteracting Hamiltonian of spin 1/2 particles in a uniform magnetic field along xdirection and is transformed to the system described by the Ising Hamiltonian with random field in zdirection. For the limit of strong disorder, the singlequbit correction terms significantly enhance the probability for the whole system to remain in the ground state for the proposed nonstoquastic annealing protocol. We demonstrate that even when transitions occur for stronger interaction between qubits, the most probable quantum state is one of the lower energy states of the final Hamiltonian. 
Thursday, March 8, 2018 12:15PM  12:27PM 
S28.00004: A Scalable and Tunable Approach to Plant SpinGlass Solutions Dilina Perera, Firas Hamze, Helmut Katzgraber We present an efficient, scalable approach that allows one to construct spinglass instances with planted solutions and tunable hardness. Here, we demonstrate our method for square lattices. Frustrated cycles are constructed via cornersharing spins on lattice building blocks such that they share a common local ground state, which guarantees that the ground state energy of the entire problem is known a priori. Using population annealing Monte Carlo, we compare the computational complexity of planted problems across a multiplicity of instance classes that results from the numerous ways in which the frustrated cycles can be chosen. Our method offers significant advantages over previous approaches for planting solutions in that it is easy to implement, the problems have tunable hardness, and it requires no computational overhead. 
Thursday, March 8, 2018 12:27PM  12:39PM 
S28.00005: Demonstration of a scaling advantage for a quantum annealer over simulated annealing Tameem Albash, Daniel Lidar A complete determination of the optimal timetosolution (TTS) using the DWave quantum annealing processors has not been possible to date, preventing definitive conclusions about the presence of a scaling advantage. The main technical obstacle has been the inability to verify an optimal annealing time within the available range. Here we overcome this obstacle and present a class of problem instances for which we observe an optimal annealing time using a DWave 2000Q processor. This allows us to perform an optimal TTS benchmarking analysis and perform a comparison to several classical algorithms, including simulated annealing, spinvector Monte Carlo, and discretetime simulated quantum annealing. We establish the first example of a scaling advantage for an experimental quantum annealer over simulated annealing, with 95% confidence, over the range of problem sizes that we can test. However, we do not find evidence for a quantum speedup: simulated quantum annealing exhibits the best scaling by a significant margin. 
Thursday, March 8, 2018 12:39PM  12:51PM 
S28.00006: Performance of Quantum Annealers on Hard Scheduling Problems Bibek Pokharel, Davide Venturelli, Eleanor Rieffel We analyze the performance of three generations of quantum annealers (housed at NASA Ames), DWave Two, 2X and 2000Q, on hard scheduling problems. We quantify the improvements of each generation in both absolute timetosolution and scaling of the timetosolution with respect to problem sizes. We examined the contributions to this improvement, including the effect of hardware improvements and of the shorter anneal times available on the more recent annealers. We also examined how stronger or weaker ferromagnetic couplings enforcing constraints within vertex models (physical qubits representing the same logical qubit after embedding) affect performance. Our results offer insights about how future quantum annealers can be designed and programmed to be more effective at solving pragmatic optimization problems like the scheduling problems. 
Thursday, March 8, 2018 12:51PM  1:03PM 
S28.00007: A deceptive step towards quantum speedup detection Salvatore Mandra, Helmut Katzgraber There have been multiple attempts to design synthetic benchmark problems with the goal of detecting quantum speedup in current quantum annealing machines. To date, classical heuristics have consistently outperformed quantumannealing based approaches. Here we introduce a class of problems based on frustrated cluster loops — deceptive cluster loops — for which all currently known stateoftheart classical heuristics are outperformed by the DW2000Q quantum annealing machine. While there is a sizable constant speedup over all known classical heuristics, a noticeable improvement in the scaling remains elusive. These results represent the first steps towards a detection of potential quantum speedup, albeit without a scaling improvement and for synthetic benchmark problems. 
Thursday, March 8, 2018 1:03PM  1:15PM 
S28.00008: Solving a Higgs detection optimization problem with quantum annealing for machine learning Joshua Job, Alex Mott, JeanRoch Vlimant, Daniel Lidar, Maria Spiropulu The discovery of Higgsboson decays in a background of standardmodel processes was assisted by machine learning methods. The classifiers used to separate signal from background are trained using high quality but imperfect simulations of the physical processes involved, resulting in systematic errors. Here we use quantum and simulated annealing to solve this binary classification problem, mapped to the problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier which is resilient against overtraining and certain systematic errors in the training set. We show that these annealingbased classifier systems perform comparably to the stateoftheart machine learning methods that are currently used in particle physics while still being simple functions of directly interpretable experimental parameters with clear physical meaning. This technique may find application in other areas of HEP, such as realtime decision making in eventselection problems. 
Thursday, March 8, 2018 1:15PM  1:27PM 
S28.00009: Improving Machine Learning via Nested Quantum Annealing Correction Richard Li, Walter Vinci, Daniel Lidar Unsupervised machine learning, where algorithms seek to extract patterns from data without predefined labels, is a growing area of interest with application to many different fields. However, the training of such algorithms is difficult, due to the intractability of traditional sampling techniques. Quantum annealing, which has the potential to sample more efficiently and from a wider range of distributions than classical methods, may help lead to future advances in the development of unsurpervised learning algorithms. However, the effective temperature at which experimental quantum annealers sample scales in general with the size of the problems. As such, error correction schemes are needed to counter this trend if there is to be some benefit in using quantm annealing. To this end, we have applied nested quantum annealing correction (NQAC), a scalable and generalizable error correction scheme, to a reduced MNIST dataset, which consists of blackandwhite images of handwritten integers, and tested whether NQAC's ability to provide an effective temperature reduction leads to an increase in learning performance with nesting level. 
Thursday, March 8, 2018 1:27PM  1:39PM 
S28.00010: Quantum Autoencoders via Quantum Adders with Genetic Algorithms Lucas Lamata, Unai AlvarezRodriguez, José MartínGuerrero, Mikel Sanz, Enrique Solano The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between approximate quantum adders and quantum autoencoders. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms. Ref: arXiv:1709.07409 
Thursday, March 8, 2018 1:39PM  1:51PM 
S28.00011: Neuroinspired Quantum Learning Rule Inspired by Boltzmann Machine Yoshihiro Osakabe, Hisanao Akima, Masao Sakuraba, Mitsunaga Kinjo, Shigeo Sato To introduce learning function into quantum computing, we have investigated the method to operate a quantum neural network (QNN) in the similar manner for a classical neural network. Thanks to the analogy between neuronneuron and qubitqubit interactions [S. Sato et al., 2003, and M. Kinjo et al., 2005], we have established a new model of quantum associative memory (QuAM) [Y. Osakabe et al., 2017]. It means that we have proposed a method for a QNN to store multiple target patterns with a Hamiltonian, but an iterative learning scheme was not developed at that time. Thus, this paper proposes a quantum learning method for a QNN inspired by Hebbian and antiHebbian learning utilized in Boltzmann machine (BM); the quantum versions of Hebb and antiHebb rules of BM are developed by tuning coupling strengths among qubits repeatedly according to probability distribution of a QNN. Numerical results indicate that the proposed quantum learning rules work well and it is confirmed that the combination of quantum Hebb and antiHebb rules certainly improves the learning performance of a QNN. 
Thursday, March 8, 2018 1:51PM  2:03PM 
S28.00012: Novel Computing Approaches with Superconducting Circuits Jose Pacheco, Ajit Hira, Amanda Trujillo Recently, considerable scientific interest has been generated by experimental approaches that employ superconducting circuits to the implementation of quantum information protocols. First, we review the different possibilities, with emphasis on Digital Quantum Simulations. We also discuss the premise that the entire physical universe is describable by information. Mainly, we present the results of our attempts to build a few highly controllable quantum systems to carry out some targeted quantum systems, including the Ising Spin Model, the Heisenberg Spin Model, and the FermiHubbard Model. We also consider the use of parametric flux or voltage modulation. 
Thursday, March 8, 2018 2:03PM  2:15PM 
S28.00013: Progress on coherent Ising machines constructed from optical parametric oscillators Peter McMahon, Alireza Marandi, Ryan Hamerly, Edwin Ng, Tatsuhiro Onodera, Yoshitaka Haribara, Carsten Langrock, Davide Venturelli, Eleanor Rieffel, Shuhei Tamate, Takahiro Inagaki, Hiroki Takesue, Shoko Utsunomiya, Kazuyuki Aihara, Robert Byer, Martin Fejer, Hideo Mabuchi, Yoshihisa Yamamoto We present a scalable optical processor with electronic feedback, based on networks of optical parametric oscillators. The design of our machine is inspired by adiabatic quantum computers, although it is not an AQC itself. Our prototype machine is able to find exact solutions of, or sample good approximate solutions to, a variety of hard instances of Ising problems with up to 100 spins and 10,000 spinspin connections. We will show results from problems with continuous J_ij couplings and externalfield h_i terms, as well as from a comparison with the DWave 2000Q quantum annealer on unweighted MAXCUT problems. Reference: P.L. McMahon*, A. Marandi*, et al. Science 354, No. 6312, pp. 614617 (2016). 
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