Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session F27: Programming and Compilation  the Quantum Computing StackFocus

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Sponsoring Units: DQI Chair: Peter Groszkowski, University of Chicago Room: BCEC 160C 
Tuesday, March 5, 2019 11:15AM  11:51AM 
F27.00001: The quantum computing stack: From quantum algorithms to optimized resource estimates Invited Speaker: Thomas Haener It is known that quantum computers offer up to exponential speedups over their classical counterparts for solving certain computational tasks. However, concrete comparisons of resource requirements for specific problem instances are still scarce. In this talk, I will discuss how software for quantum computing can support researchers in carrying out such analyses. Specifically, I will focus on automatic compiler optimizations and how these may offer great benefits even in the presence of handoptimized libraries. 
Tuesday, March 5, 2019 11:51AM  12:03PM 
F27.00002: Automatic Compilation for Portable and Scalable Quantum Software Robert S Smith, Eric C Peterson Noisy intermediatescale quantum (NISQ) processors are becoming larger (20–150 qubits) and more expressive (e.g., supporting a plurality of twoqubit interactions). However, since NISQ processors are errorprone, there is a great disparity between those programs which are desirable to write and those which execute reliably. Together, these aspects make it difficult to write quantum programs which are both portable (i.e., readily executable on different devices) and efficient (i.e., produce more accurate results using fewer resources). We discuss how automatic compilation helps achieve these simultaneous goals more consistently, and compare results of an opensource, extensible automatic compiler quilc to semimanual counterparts. 
Tuesday, March 5, 2019 12:03PM  12:15PM 
F27.00003: Compiler tools for hybrid quantumclassical algorithms Peter Karalekas, Nikolas Tezak, Lauren Capelluto, Eric C Peterson, Robert S Smith, Mark Suska, Adam Mocarski, Stephan Brown, Celena Tanguay, Rodney Sinclair, Nima TaieNobarie, Chloe Song, Stefan Turkowski, Michael Rust, Glenn Jones, Schuyler Fried, Diego Scarabelli, Deanna Abrams, Shane Caldwell, Colm Ryan, Prasahnt Sivarajah, William J Zeng, Blake Johnson, Chad Rigetti We describe the Rigetti compilation toolchain and in particular how it supports optimized implementations of certain hybrid quantumclassical algorithms. Programs written in Quil are transpiled into a restricted subset of Quil instructions that are realizable on the available control hardware and target chip topology. These transpiled programs are further compiled into binary executables for custom FPGA pulse sequencers. The toolchain provides two key features that enable high performance hybrid computing: (1) gate parameters from the original input Quil program are translated to sequencer instructions that load from classical memory shared between the sequencer and classical host computer; (2) compiled programs can contain arbitrary control flow that branches off of singlequbit measurement results. The first feature enables the compilation of Quil into binaries that can be updated at runtime and the second, enables active reset of qubit states. Together these allow for rapid iteration in applications such as the optimization of a variational quantum algorithm, because these binaries can be reexecuted many times for different input parameters without need for recompiling or waiting for qubits to relax. We provide quantitative benchmarks of the improved wallclock performance. 
Tuesday, March 5, 2019 12:15PM  12:27PM 
F27.00004: Quantum Circuit Compilation to NISQ processors Eleanor Rieffel, Davide Venturelli We describe automated reasoning approaches for QCCNISQ, quantum circuit compilation to NISQ (noisy intermediatescale quantum) processor architectures. We tested the approaches for different NISQ processor architectures and QAOA (quantum alternating operator ansatz) circuits. This approach is integrated into our software suite for automated, architecture aware, compilation for emerging gatemodel quantum computers. We give an overview of the key components of this suite: a circuit synthesizer, a QCCNISQ solver, and a visualizer. 
Tuesday, March 5, 2019 12:27PM  1:03PM 
F27.00005: Less than a million CNOTs should be enough to solve a classically intractable instance of a scientific problem with a quantum circuit Invited Speaker: Dmitri Maslov The question I will try to answer in this talk is the following: what is the size of the smallest quantum computation capable of solving an instance of a scientifically interesting problem that is intractable for a classical computer? The problem I consider is Hamiltonian dynamics simulation, in the sense of the ability to sample probability distribution given by a state evolved under the target Hamiltonian for a specified time t and accurate to within a specified error epsilon. The core of the talk concerns the development and application of a range of algorithm design, circuit optimization, and hardware/software codesign techniques, the combination of which leads to the reduction in the quantum resource counts provided by pure algorithmic formulations by several orders of magnitude. Specifically, the best physicallevel quantum circuit features under 650,000 CNOT gates, and the best faulttolerant circuit features under 6.8x10^6 T gates. This illustrates that algorithmic and softwarelevel optimizations will be indispensable for practical quantum computing. 
Tuesday, March 5, 2019 1:03PM  1:15PM 
F27.00006: Overview and Comparison of Gate Level Quantum Software Platforms Ryan LaRose Quantum computers are available to use over the cloud, but the recent explosion of quantum software platforms can be overwhelming for those deciding on which to use. In this paper, we provide a current picture of the rapidly evolving quantum computing landscape by comparing four software platformsForest (pyQuil), QISKit, ProjectQ, and the Quantum Developer Kitthat enable researchers to use real and simulated quantum devices. Our analysis covers requirements and installation, language syntax through example programs, library support, and quantum simulator capabilities for each platform. For platforms that have quantum computer support, we compare hardware, quantum assembly languages, and quantum compilers. We conclude by covering features of each and briefly mentioning other quantum computing software packages. 
Tuesday, March 5, 2019 1:15PM  1:27PM 
F27.00007: A case study for quantum software development: Linear systems solver Jan Gukelberger, Martin Roetteler, Matthias Troyer As the power of quantum computing hardware is growing, software implementations and tools for accurate resource estimation that goes beyond simple asymptotic scaling become crucial for judging the feasibility of an application. Solving linear systems of equations is one of the prime applications for which quantum algorithms with an exponential advantage over the best known classical algorithms have been developed. In this talk, we report on the implementation of stateoftheart quantum algorithms solving partial differential equations within the Microsoft Quantum Development Kit, which provides robust simulation, debugging, and resource estimation facilities. A key point in this work has been the handling of arithmetic functions: Gate synthesis facilitates accurate resource counts and is a requisite for deployment to quantum hardware, whereas emulation by the classical simulator allows for software testing on larger systems. To this end, we have equipped the Microsoft Quantum Development Kit with a generic and efficient emulation capability for quantum oracles defined by classical functions. 
Tuesday, March 5, 2019 1:27PM  1:39PM 
F27.00008: Twostep approach to scheduling quantum circuits Gian Giacomo Guerreschi, Jongsoo Park As the effort to scale up existing quantum hardware proceeds, it becomes necessary to schedule quantum gates in a way that minimizes the number of operations. There are three constraints that have to be satisfied: the logical dependency of the quantum gates in the algorithm, the fact that any qubit may be involved in at most one gate at a time, and the restriction that twoqubit gates are implementable only between connected qubits. The last aspect implies that the compilation depends not only on the algorithm, but also on hardware properties like connectivity. Here we present a twostep approach in which logical gates are initially scheduled neglecting connectivity considerations and routing operations are added at a later step in ways that minimize their overhead. We rephrase the subtasks of gate scheduling in terms of graph problems like edgecoloring and maximum subgraph isomorphism. While this approach is general, we specialize to a onedimensional array of qubits to propose a routing scheme that is minimal in the number of exchange operations. As a practical application, we schedule the quantum approximate optimization algorithm in a linear geometry and quantify the reduction in the number of gates and circuit depth that results from different scheduling strategies. 
Tuesday, March 5, 2019 1:39PM  1:51PM 
F27.00009: Realtime randomized compilation of quantum algorithms Guilhem Ribeill, Matthew Ware, Brian Donovan, Luke Govia Recent experiments have indicated the effectiveness of Pauli Frame Randomization (PFR) as a noise shaping technique. Not only does PFR tailor general noise into Paulistochastic noise, it also decouples qubits from their noise environment increasing system Markovianity. Presently, the randomization process requires extensive precompilation of pulse sequences. Here, we demonstrate an in hardware implementation of PFR on the BBN Arbitrary Pulse Sequencer II that generates randomized sequences in realtime. We first implement randomized benchmarking without precomputing pulse sequences before using this capability to randomize a Gate Set Tomography experiment on a superconducting quantum processor. 
Tuesday, March 5, 2019 1:51PM  2:03PM 
F27.00010: SKQuantOpt: Optimizers for Noisy IntermediateScale Quantum Devices Wim Lavrijsen, Ana Tudor, Jeffrey Larson, Kevin J. Sung, Lucy Linder, Juliane Mueller, Jarrod R. McClean, Ryan Babbush, Miroslav Urbanek, Costin Iancu, Wibe A De Jong Classical optimizers play an important role in quantum computing, e.g., in the hybrid Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization algorithms. They are used in hyperparameter tuning, calibration, machine learning, etc. Unfortunately, most of the easily accessible optimizers do not handle noise well, leaving them below threshold for use with Noisy IntermediateScale Quantum (NISQ) devices. We present skquantopt, part of scikitquant.org, a set of optimizers tuned for the needs of NISQ. We have taken the stateoftheart optimizers and tested them on a range of VQE applications and on hyperparameter tuning for optimization on DWave. Mesh methods, including hybrids that add local models, yield the best results. We present these results as well as guidance on use. Collected in skquantopt, we provide the best optimizers in Python, the most used language in quantum computing, through the standard channels. The interfaces are made consistent with default parameters attuned to quantum computing problems, allowing for easy application and fast evaluation. 
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