Bulletin of the American Physical Society
50th Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics APS Meeting
Volume 64, Number 4
Monday–Friday, May 27–31, 2019; Milwaukee, Wisconsin
Session C04: Quantum simulation and algorithms |
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Chair: Matthew Norcia, JILA/University of Colorado Room: Wisconsin Center 102AB |
Tuesday, May 28, 2019 10:30AM - 10:42AM |
C04.00001: QSCOUT: Quantum Scientific Computing Open User Testbed Melissa C. Revelle, Peter Maunz, Andrew Landahl, Matthew G. Blain, Susan M. Clark, Daniel Lobser, Richard Muller, Kenneth M. Rudinger, Christopher G. Yale Trapped ion systems currently offer the highest fidelity single- and two-qubit gates and they admit multiple ways to scale to large processors. Leveraging these advantages, we introduce the Quantum Scientific Computing Open User Testbed (QSCOUT), a trapped-ion testbed laboratory to address the potential of near-term quantum hardware for scientific computing applications. The system being realized at Sandia using our surface ion traps will feature all-to-all connected qubit layouts that can be adapted to different algorithms. QSCOUT provides unrestricted access to the internals of the quantum hardware and enables users to adapt and modify the quantum gates and underlying pulse sequences. We will support and advise users in the functionality of the QSCOUT testbeds, which will allow users to realize the potential of high-fidelity quantum computing [Preview Abstract] |
Tuesday, May 28, 2019 10:42AM - 10:54AM |
C04.00002: Quantum Optimization for Maximum Independent Set Using Rydberg Atom Arrays Hannes Pichler, Sheng Tao Wang, Leo Zhou, Soonwon Choi, Mikhail Lukin We describe and analyze an architecture for quantum optimization to solve maximum independent set (MIS) problems using neutral atom arrays trapped in optical tweezers. Optimizing independent sets is one of the paradigmatic, NP-hard problems in computer science. Our approach is based on coherent manipulation of atom arrays via the excitation into Rydberg atomic states. Specifically, we show that solutions of MIS problems can be efficiently encoded in the ground state of interacting atoms in 2D arrays by utilizing the Rydberg blockade mechanism. By studying the performance of leading classical algorithms, we identify parameter regimes, where computationally hard instances can be tested using near-term experimental systems. Practical implementations of both quantum annealing and variational quantum optimization algorithms beyond the adiabatic principle are discussed. [Preview Abstract] |
Tuesday, May 28, 2019 10:54AM - 11:06AM |
C04.00003: Calculation of molecular vibrational spectra on a quantum annealer Alexander Teplukhin, Brian Kendrick, Dmitri Babikov Conventional computers have been used to study molecular spectra theoretically for a long time. In recent years, quantum computing hardware has become available to theoreticians, and recently the electronic spectrum of a molecule was computed on a quantum simulator. In this paper, a new methodology is presented to calculate the vibrational spectrum of a molecule on a quantum annealer. The method is based on a mapping of the the ground state variational problem onto an Ising or quadratic unconstrained binary optimization (QUBO) problem by expressing the expansion coefficients using spins or qubits. The algorithm is applied to two chemically important molecules O$_2$ (oxygen) and O$_3$ (ozone). The lowest two vibrational states of these molecules are computed using both a hardware quantum annealer and a software based classical annealer. Noise simulation studies demonstrate that the accuracy of the method is largely affected by the hardware noise. The algorithm is general and represents a new revolutionary approach for solving the real symmetric eigenvalue problem on a quantum annealer. [Preview Abstract] |
Tuesday, May 28, 2019 11:06AM - 11:18AM |
C04.00004: Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices Leo Zhou, Sheng-Tao Wang, Soonwon Choi, Hannes Pichler, Mikhail Lukin The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm designed to tackle combinatorial optimization problems. Despite its promise for near-term quantum applications, not much is currently understood about its performance beyond its lowest-depth variant. An essential but missing ingredient for understanding and deploying QAOA is a constructive approach for the outer-loop classical optimization. We give an in-depth study of QAOA on MaxCut problems by developing an efficient parameter-optimization procedure and revealing its ability to exploit non-adiabatic operations. We then benchmark QAOA and compare it with quantum annealing, especially on difficult instances where adiabatic quantum annealing fails due to small spectral gaps. The comparison reveals that QAOA can learn to use non-adiabatic mechanisms to circumvent challenges associated with vanishing spectral gaps. Finally, we provide a realistic resource analysis on the experimental implementation of QAOA. When we account for quantum projection noise, optimization will be important only for system sizes beyond numerical simulations, but accessible on near-term devices. We also propose a feasible implementation of large MaxCut problems with a few hundred vertices in a system of 2D neutral atoms. [Preview Abstract] |
Tuesday, May 28, 2019 11:18AM - 11:30AM |
C04.00005: Training of Quantum Circuits on a Hybrid Quantum Computer Daiwei Zhu, Norbert Linke, Marcello Benedetti, Kevin Landsman, Nhung Nguyen, Cinthia Alderete, Alejandro Perdomo-Ortiz, Nathan Korda, Alistair Garfood, Charles Brecque, Laird Egan, Oscar Perdomo, Christopher Monroe Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by near-term quantum computers. Here we implement a datadriven quantum circuit training algorithm on the canonical Bars-and-Stripes data set using a quantum-classical hybrid machine. The training proceeds by running parameterized circuits on a trapped ion quantum computer, and feeding the results to a classical optimizer. We apply two separate strategies, Particle Swarm and Bayesian optimization to this task. We show that the convergence of the quantum circuit to the target distribution depends critically on both the quantum hardware and classical optimization strategy. Our study represents the first successful training of a high-dimensional universal quantum circuit, and highlights the promise and challenges associated with hybrid learning schemes. [Preview Abstract] |
Tuesday, May 28, 2019 11:30AM - 11:42AM |
C04.00006: Quantum Convolutional Neural Networks Iris Cong, Soonwon Choi, Mikhail Lukin We introduce and analyze a novel quantum machine learning model motivated by convolutional neural networks (CNN). Our quantum convolutional neural network (QCNN) makes use of only O(log(N)) variational parameters for input sizes of N qubits, allowing for its efficient training and implementation on realistic, near-term quantum devices. We show that QCNN circuits combine the multi-scale entanglement renormalization ansatz and quantum error correction to mimic renormalization-group flow, making them capable of recognizing different quantum phases and associated phase transitions. As an example, we illustrate the power of QCNNs in recognizing a 1D symmetry-protected topological phase, and demonstrate that a QCNN trained on a set of exactly solvable points can reproduce the phase diagram over the entire parameter regime. Finally, generalizations and possible applications of QCNN are discussed. [Preview Abstract] |
Tuesday, May 28, 2019 11:42AM - 11:54AM |
C04.00007: Optimization of Objective Function Estimation for Gate-Model Quantum Computers Laszlo Gyongyosi, Sandor Imre Quantum computers exploit the fundamentals of quantum mechanics to solve computational problems more efficiently than traditional computers. Gate-model quantum computers provide a flexible framework to realize quantum computers in experiments. The maximization of the objective function of computational problems is a remarkable application scenario of experimental gate-model quantum computers. The objective function estimation of the quantum computer is a high-cost procedure that requires several rounds of quantum state preparations, quantum computational steps, and quantum state measurements. Here, we define a framework for objective function estimation and maximization in gate-model quantum computers. The method significantly reduces the costs of the objective function estimation and provides an estimate of the new state of the quantum computer. The results are particularly convenient for the performance optimization of experimental gate-model quantum computations. [Preview Abstract] |
Tuesday, May 28, 2019 11:54AM - 12:06PM |
C04.00008: Extracting Dynamical Structure Factor from Cold Atom Simulator Yao Wang, Joannis Koepsell, Eugene Demler We propose an approach to extract the dynamical structure factors using the measurements accessible in a cold atom simulator. Through a non-equilibrium simulation using the Hubbard model with the presence of dynamical charge impurity, we mimic an optical tweezer on an optical lattice. With a quasi-orthogonal projection, we demonstrate the feasibility of extracting the dynamical charge structure factors out of the instantaneous charge distributions measurable by a quantum gap microscope. We also observe the low-energy bimagnon excitations raising from the signal with strong confining potentials, which enables the detection of dynamical spin excitations within the same measurement. With proper pumping sources, this approach can be extended to simulate other more complicated solid-state spectroscopies. [Preview Abstract] |
Tuesday, May 28, 2019 12:06PM - 12:18PM |
C04.00009: Symmetric subspace randomized benchmarking Charles Baldwin, John Gaebler, Bryce Byork, Daniel Stack Randomized benchmarking is the standard tool for accurately characterizing error rates of quantum hardware. However, multi-qubit benchmarking requires individual addressing of each qubit, which is a difficult in certain trapped-ion testbeds. We present a new two-qubit randomized benchmarking procedure that operates only in the symmetric subspace of a pair of qubits. By performing benchmarking only in the symmetric subspace, we drastically reduce the number of gates required, and simplify the experimental implementation. We demonstrate the protocol in a trapped-ion experiment using arbitrary global single-qubit rotations and the Molmer-Sorenson interaction. Most expected errors in a Molmer-Sorenson gate keep population in the symmetric subspace but even errors that mix symmetric and anti-symmetric subspaces can be diagnosed. These errors appear as leakage and their rate can by characterized by combining our protocol with recently proposed leakage benchmarking. [Preview Abstract] |
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