We report a quantum simulation of the deuteron binding energy on quantum processors accessed via cloud servers. We use a Hamiltonian from pionless effective field theory at leading order. We design a ...low-depth version of the unitary coupled-cluster ansatz, use the variational quantum eigensolver algorithm, and compute the binding energy to within a few percent. Our work is the first step towards scalable nuclear structure computations on a quantum processor via the cloud, and it sheds light on how to map scientific computing applications onto nascent quantum devices.
A content-addressable memory (CAM) stores key-value associations such that the key is recalled by providing its associated value. While CAM recall is traditionally performed using recurrent neural ...network models, we show how to solve this problem using adiabatic quantum optimization. Our approach maps the recurrent neural network to a commercially available quantum processing unit by taking advantage of the common underlying Ising spin model. We then assess the accuracy of the quantum processor to store key-value associations by quantifying recall performance against an ensemble of problem sets. We observe that different learning rules from the neural network community influence recall accuracy but performance appears to be limited by potential noise in the processor. The strong connection established between quantum processors and neural network problems supports the growing intersection of these two ideas.
We present qcor—a language extension to C++ and compiler implementation that enables heterogeneous quantum-classical programming, compilation, and execution in a single-source context. Our work ...provides a first-of-its-kind C++ compiler enabling high-level quantum kernel (function) expression in a quantum-language agnostic manner, as well as a hardware-agnostic, retargetable compiler workflow targeting a number of physical and virtual quantum computing backends. qcor leverages novel Clang plugin interfaces and builds upon the XACC system-level quantum programming framework to provide a state-of-the-art integration mechanism for quantum-classical compilation that leverages the best from the community at-large. qcor translates quantum kernels ultimately to the XACC intermediate representation, and provides user-extensible hooks for quantum compilation routines like circuit optimization, analysis, and placement. This work details the overall architecture and compiler workflow for qcor, and provides a number of illuminating programming examples demonstrating its utility for near-term variational tasks, quantum algorithm expression, and feed-forward error correction schemes.
Public participation in groundwater projects is increasing, however, the efficacy of the data collected in such studies, is not well‐documented in the literature. In this study, the authors describe ...a citizen science project focused on measuring and recording groundwater levels in an aquifer and evaluate whether the groundwater data collected by the participants are trustworthy. A total of 31 participants were initially recruited to measure and record groundwater levels from 29 monitoring wells on a barrier island. Following recruitment, the authors provided training to the citizen scientists by introducing groundwater concepts, and showing the participants how to measure, record and report groundwater level data (over an 81‐day period) with an electronic water level meter. The water level data recorded by the citizen scientists (i.e., 35 time series datasets with over 450 unique measurements) were then compared to high frequency data recorded by automated water level loggers that were already deployed in the groundwater monitoring wells to assess the trustworthiness of the data. Trustworthiness was evaluated using measures of reliability (i.e., consistency in measuring the same thing), validity (i.e., degree to which results are truthful), and other standard graphical and statistical techniques. The results suggest that with proper training, guidance, and motivation, citizen scientists can collect trustworthy groundwater level data that could be useful for monitoring the sustainability of aquifers and managing of groundwater levels. It is noted however, that such positive outcomes require significant investments of time and effort on the part of the project managers.
Article impact statement: Groundwater level data collected by citizen scientists are trustworthy, but project managers of citizen science projects are cautioned that acquisition of robust data requires significant investments of time and effort.
Citizen science is the participation of non-scientists in the collection of scientific data and other aspects of the scientific process. In this manuscript, we explore what it means to participate in ...citizen science from two perspectives-that of a researcher designing and facilitating a citizen science project, and that of a citizen scientist volunteering the time and energy required for participation. We examine the methods and goals of the projects, describing the challenges faced by researchers and science volunteers alike as they participate in research processes aimed to increase community involvement in science and, by extension, environmental management issues. We describe how the constraints of citizen science models and methods underscore the importance of incorporating alternative anthropological and ethnographic approaches in coastal research, and offer eco-ethnography as a way for scientists to extend their citizen science projects to better reflect the needs and concerns of local communities impacted by climate change and sea-level rise.
Quantum field theory (QFT) simulations are a potentially important application for noisy intermediate scale quantum (NISQ) computers. The ability of a quantum computer to emulate a QFT, therefore, ...constitutes a natural application-centric benchmark. Foundational quantum algorithms to simulate QFT processes rely on fault-tolerant computational resources, but to be useful on NISQ machines, error-resilient algorithms are required. Here we outline and implement a hybrid algorithm to calculate the lowest energy levels of the paradigmatic 1+1--dimensional interacting scalar QFT. We calculate energy splittings and compare results with experimental values obtained on currently available quantum hardware. We show that the accuracy of mass-renormalization calculations represents a useful metric with which near-term hardware may be benchmarked. We also discuss the prospects of scaling the algorithm to full simulation of interacting QFTs on future hardware.
Quantum computing is emerging as a remarkable technology that offers the possibility of achieving major scientific breakthroughs in many areas. Here, by leveraging the unique features of quantum ...mechanics, quantum computers may be instrumental in advancing many areas, including science, energy, defense, medicine, and finance. This includes solving complex problems whose solution lies well beyond the capacity of contemporary and even future supercomputers that are based on conventional computing technologies. As a foundation for future generations of computing and information processing, quantum computing represents an exciting area for developing new ideas in computer science and computational engineering.
QCOR Mintz, Tiffany M.; McCaskey, Alexander J.; Dumitrescu, Eugene F. ...
ACM journal on emerging technologies in computing systems,
04/2020, Letnik:
16, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Quantum computing (QC) is an emerging computational paradigm that leverages the laws of quantum mechanics to perform elementary logic operations. Existing programming models for QC were designed with ...fault-tolerant hardware in mind, envisioning stand-alone applications. However, the susceptibility of near-term quantum computers to noise limits their stand-alone utility. To better leverage limited computational strengths of noisy quantum devices, hybrid algorithms have been suggested whereby quantum computers are used in tandem with their classical counterparts in a heterogeneous fashion. This
modus operandi
calls out for a programming model and a high-level programming language that natively and seamlessly supports heterogeneous quantum-classical hardware architectures in a single-source-code paradigm. Motivated by the lack of such a model, we introduce a language extension specification, called
QCOR
, which enables single-source quantum-classical programming. Programs written using the QCOR library–based language extensions can be compiled to produce functional hybrid binary executables. After defining QCOR’s programming model, memory model, and execution model, we discuss how QCOR enables variational, iterative, and feed-forward QC. QCOR approaches quantum-classical computation in a hardware-agnostic heterogeneous fashion and strives to build on best practices of high-performance computing. The high level of abstraction in the language extension is intended to accelerate the adoption of QC by researchers familiar with classical high-performance computing.
Adiabatic quantum computing is a promising route to the computational power afforded by quantum information processing. The recent availability of adiabatic hardware has raised challenging questions ...about how to evaluate adiabatic quantum optimization programs. Processor behavior depends on multiple steps to synthesize an adiabatic quantum program, which are each highly tunable. We present an integrated programming and development environment for adiabatic quantum optimization called JADE that provides control over all the steps taken during program synthesis. JADE captures the workflow needed to rigorously specify the adiabatic quantum optimization algorithm while allowing a variety of problem types, programming techniques, and processor configurations. We have also integrated JADE with a quantum simulation engine that enables program profiling using numerical calculation. The computational engine supports plug-ins for simulation methodologies tailored to various metrics and computing resources. We present the design, integration, and deployment of JADE and discuss its potential use for benchmarking adiabatic quantum optimization programs by the quantum computer science community.
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