Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session C42: Applications of Noisy Intermediate Scale Quantum Computers IFocus

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Sponsoring Units: DQI Chair: Timothy Hsieh, Perimeter Institute for Theoretical Physics Room: BCEC 210A 
Monday, March 4, 2019 2:30PM  3:06PM 
C42.00001: Errormitigated quantum computation with noisy superconducting qubits Invited Speaker: Abhinav Kandala In the absence of fault tolerant hardware architectures, the development of hybrid algorithms has led to interest in approximate computing with noisy intermediate scale quantum computers. However, recent demonstrations [1] have been severely limited by decoherence, highlighting the need for error mitigation techniques to extract accurate computations from noisy quantum hardware. In this context, I shall introduce a zeronoise extrapolation technique [2,3] that enables access to noisefree estimates of expectation values, after the application of a short depth circuit, without requiring any additional quantum resources. I shall discuss challenges for its implementation with superconducting qubits [4], and highlight its broad applicability with experimental demonstrations of quantum simulation [4] and machine learning [5]. [1] A. Kandala, A. Mezzacapo et al, Nature 549, 242 (2017) [2] K. Temme et al, PRL 119, 180509 (2017) [3] Li et al, PRX 7, 021050 (2017) [4] A. Kandala et al, arXiv:1805.04492 [5] V. Havelick et al, arXiv:1804.11326 
Monday, March 4, 2019 3:06PM  3:18PM 
C42.00002: A quantum computing algorithm for the investigation of the molecular excited states Pauline Ollitrault, Panagiotis Barkoutsos, Stefan Woerner, Ivano Tavernelli Quantum computing is emerging as a new paradigm for the solution of quantum chemistry problems. Recently, the Variational Quantum Eigensolver (VQE) algorithm has been proposed and successfully applied to the simulation of the ground state properties of simple molecular systems in a real quantum device. The calculation of molecular excited state properties constitute an additional challenge for both classical and quantum electronic structure algorithms. In fact, in addition to the calculation of a wellconverged ground state wavefunction, one needs to devise schemes for the evaluation of the higher energy states, which  in general  are not accessible through the simple optimization procedure. In this work, a perturbative approach is applied to the ground state wavefunction to derive a pseudoeigenvalue problem, which size is characterized by a favorable scaling in the number of electrons. The different matrix elements are measured on the quantum hardware using the ground state wavefunction parametrized according to the UCC and the hardware efficient Ansätze described in [Barkoutsos et al., Phys. Rev. A 98, 022322]. The method is applied to the calculation of the excited states of simple molecules, including H2, LiH and H2O. 
Monday, March 4, 2019 3:18PM  3:30PM 
C42.00003: Error Mitigation in the Presence of Spatially Correlated Noise Vickram Premakumar, Ekmel Ercan, Joydip Ghosh, Mark G Friesen, Mark Alan Eriksson, Susan Coppersmith, Robert James Joynt The most common error models for quantum computers assume the 
Monday, March 4, 2019 3:30PM  3:42PM 
C42.00004: Benchmarking error mitigation techniques on noisy quantum processors Corentin Bisot, Emmanuel Lilette, Thomas Ayral Several error mitigation techniques have been proposed in the recent years to compensate for the errors of noisy, intermediatescale quantum (NISQ) devices before fullfledged, yet costly error correction methods can be implemented. Here, we present an implementation of a combination of these techniques, and a detailed benchmark based both on realistic noisy simulations and actual computations on current quantum architectures such as, for instance, superconducting transmon processors. We examine the link between the accuracy of the knowledge of the quantum hardware (via tomography), the accuracy on the final observable, and the overhead of mitigation. We also discuss the relevance of these methods for reaching high enough accuracies in applications such as quantum chemistry. 
Monday, March 4, 2019 3:42PM  3:54PM 
C42.00005: Efficient scheduling of noise characterization protocols in quantum computing architectures Riddhi Swaroop Gupta, Michael Jordan Biercuk

Monday, March 4, 2019 3:54PM  4:06PM 
C42.00006: Superfast encodings for fermionic quantum simulation Kanav Setia, Sergey Bravyi, Antonio Mezzacapo, James Whitfield Here we revisit the Superfast Encoding introduced by Kitaev and one of the authors. This encoding maps a target fermionic Hamiltonian with twobody interactions on a graph of degree dto a qubit simulator Hamiltonian composed of Pauli operators of weight O(d). A system of m fermi modes gets mapped to n=O(md) qubits. We propose Generalized Superfast Encodings (GSE) which require the same number of qubits as the original one but have more favorable properties. First, we describe a GSE such that the corresponding quantum code corrects any singlequbit error provided that the interaction graph has degree d≥6. In contrast, we prove that the original Superfast Encoding lacks the error correction property for d≤6. Secondly, we describe a GSE that reduces the Pauli weight of the simulator Hamiltonian from O(d)to O(logd). The robustness against errors and a simplified structure of the simulator Hamiltonian offered by GSEs can make simulation of fermionic systems within the reach of nearterm quantum devices. As an example, we apply the new encoding to the fermionic Hubbard model on a 2D lattice. 
Monday, March 4, 2019 4:06PM  4:18PM 
C42.00007: Experimental Implementation of Quantum Circuit Born Machines in NearTerm Quantum Devices Alejandro Perdomo, Vicente LeytonOrtega, Oscar Perdomo Finding valuable machine learning that could benefit from noisy intermediatescale quantum computers is one of the leading research efforts towards the milestone of practical quantum advantage. In this talk, we will focus in the one of most challenging tasks for the machine learning community: the case of generative modeling in unsupervised machine learning. In Ref. [1], a datadriven quantum circuit learning (DDQCL) approach was proposed as a hybrid quantumclassical algorithm capable of training shallow quantum circuits to prepare desirable quantum states. This resulting quantum state is referred as a Quantum Circuit Born Machine (QCBM) [1,2] and it exploits the probabilistic nature of the Born amplitudes from the computational basis states to capture correlations in the classical training data set. This QCBM model can be used to solve unsupervised generative modeling tasks such as image generation and reconstruction. We will discuss results of experimental implementations of QCBMs via DDQCL, as well as ideas for DDQCL variants that could be useful in, for example, quantum state preparation and noise mitigation. 
Monday, March 4, 2019 4:18PM  4:30PM 
C42.00008: Simulations of Real Time Scattering in the 1D Quantum Ising Model Erik Gustafson, Yannick Meurice, Judah F UnmuthYockey We will discuss the results of quantum simulating a real time scattering event in the 1D Quantum Ising spin models using a quantum simulator. We discuss how we can measure the phase shifts of scattering processes. We compare the results from exact diagonalization with those using a quantum simulator. We examine the errors introduced in our simulation by some of the noise that would be present on a real quantum computer. We discuss the efficacy of simulating this model on a quantum computer. 
Monday, March 4, 2019 4:30PM  4:42PM 
C42.00009: Quantum Local Search for Graph Community Detection Ruslan Shaydulin, Hayato UshijimaMwesigwa, Ilya Safro, Susan Mniszewski, Yuri Alexeev We present Quantum Local Search (QLS) approach and demonstrate its efficacy by applying it to the problem of community detection in realworld networks. QLS is a hybrid algorithm that combines a classical machine with a small quantum device. QLS starts with an initial solution and searches its neighborhood, iteratively trying to find a better candidate solution. One of the main challenges of the quantum computing in NISQ era is the small number of available qubits. QLS addresses this challenge by using the quantum device only for the neighborhood search, which can be restricted to be small enough to fit on nearterm quantum device. We implement QLS for modularity maximization graph clustering using QAOA on IBM Q Experience as a quantum local solver. We demonstrate the potential for quantum acceleration by showing that existing stateoftheart optimization solvers cannot find a good solution to the local problems quickly and provide an estimate of how larger quantum devices can improve the performance of QLS. We apply QLS to the problem of clustering microbiome cooccurence networks and present the preliminary results. 
Monday, March 4, 2019 4:42PM  4:54PM 
C42.00010: Marginals optimization procedure: algorithmically extending the capability of nearterm quantum computers Peter Johnson, Max Radin, Amara Katabarwa, Jhonathan Romero, Yudong Cao We are entering an era in which quantum computers can perform contrived tasks that classical computers cannot. However, these devices are far from being able to break RSA encryption or to simulate complex chemical reactions. A big question driving the field is: can we find a practical use for these nearterm intermediate scale quantum (NISQ) devices? A promising approach is to explore “variational quantum algorithms”, which treat a quantum circuit much like an artificial neural network to solve optimization problems approximately. These optimization problems include estimating the ground state energy of a small molecule and understanding the structure of social networks. 
Monday, March 4, 2019 4:54PM  5:06PM 
C42.00011: Reliable Analog Quantum Simulation and Quantum Complexity Karthik Chinni, Pablo Poggi, Ivan Deutsch An analog quantum simulator does not employ digital gates with quantum error correction. Yet, one hopes such devices can achieve a “quantum advantage,” i.e., enable the simulation of some property that cannot be simulated efficiently on a classical computer. Typically, one considers “universal” properties in condensed matter, as these are the quantities that are robust in the presence of perturbations [1]. What is the relationship between robustness and complexity? Are the robust properties efficiently simulatable on a classical computer, and the complex properties hypersensitive to perturbation? To address these questions, we seek to quantify the reliability of an analog quantum simulator while simulating complex systems and thereby identify these universal quantities. We study a “programmable” analog quantum simulator in the 16dimensional Hilbert space based on optimal control of atomic spins in cesium [2], and study the basic paradigms such as the excited state quantum phase transitions [3] in the LipkinMeshkovGlick (LMG) model [3]. 
Monday, March 4, 2019 5:06PM  5:18PM 
C42.00012: Experimental Methods for Improving Heuristic Quantum Algorithms on NISQ Devices Bradley Mitchell, Ravi Naik, Unpil Baek, Dar Dahlen, John Mark Kreikebaum, Kevin P O'Brien, Vinay Ramasesh, Machiel Blok, Wim Lavrijsen, Costin Iancu, Irfan Siddiqi Heuristic quantum algorithms, such as QAOA (Quantum Approximate Optimization Algorithm) and VQE (Variational Quantum Eigensolver), have the potential for performing useful, classically intractable calculations on NISQ (Noisy Intermediate Scale Quantum) devices, with applications ranging from general optimization to quantum chemistry. Outstanding challenges in implementing these algorithms include error mitigation and minimizing costly calls to quantum hardware. We report experimental developments to identify and address these challenges on a superconducting quantum processor. To this end, we employ techniques including using an expanded Hilbert space of the transmon as a computational space and performing Pauli twirling operations. 
Monday, March 4, 2019 5:18PM  5:30PM 
C42.00013: Stateoftheart Classical Tools to Benchmark NISQ Devices Salvatore Mandra, Benjamin Villalonga, Sergio Boixo, Helmut Katzgraber, Eleanor Rieffel In the race to show quantum advantage, early quantum devices must be compared to the stateoftheart classical technology currently available. At the Quantum Artificial Intelligence Lab (QuAIL) at NASA Ames, we are continuously developing new classical algorithms to benchmark/validate quantum hardware and to raise the bar to claim quantum advantage. In my talk, I will present some of our latest stateoftheart classical tools to optimize classical cost functions (in collaboration with Texas A&M University), including numerical results on hard benchmark problem sets. Moreover, I will present our optimized classical simulator for large quantum circuits (in collaboration with the Google AI team), including numerical simulations of the Google Bristlecone quantum processing unit. 
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