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NASA NTRS2017

A Dose of Reality: Radiation Analysis for Realistic Human Spacecraft

INTRODUCTION As with most computational analyses, a tradeoff exists between problem complexity, resource availability and response accuracy when modeling radiation transport from the source to a detector. The largest amount of analyst time for setting up an analysis is often spent ensuring that any simplifications made have minimal impact on the results. The vehicle shield geometry of interest is typically simplified from the original CAD design in order to reduce computation time, but this simplification requires the analyst to "re-draw" the geometry with a limited set of volumes in order to accommodate a specific radiation transport software package. The resulting low-fidelity geometry model cannot be shared with or compared to other radiation transport software packages, and the process can be error prone with increased model complexity. The work presented here demonstrates the use of the DAGMC (Direct Accelerated Geometry for Monte Carlo) Toolkit from the University of Wisconsin, to model the impacts of several space radiation sources on a CAD drawing of the US Lab module. METHODS The DAGMC toolkit workflow begins with the export of an existing CAD geometry from the native CAD to the ACIS format. The ACIS format file is then cleaned using SpaceClaim to remove small holes and component overlaps. Metadata is then assigned to the cleaned geometry file using CUBIT/Trelis from csimsoft (Registered Trademark). The DAGMC plugin script removes duplicate shared surfaces, facets the geometry to a specified tolerance, and ensures that the faceted geometry is water tight. This step also writes the material and scoring information to a standard input file format that the analyst can alter as desired prior to running the radiation transport program. The scoring results can be transformed, via python script, into a 3D format that is viewable in a standard graphics program. RESULTS The CAD model of the US Lab module of the International Space Station, inclusive of all the racks and components, was simplified to remove holes and volume overlaps. Problematic features within the drawing were also removed or repaired to prevent runtime issues. The cleaned drawing was then run through the DAGMC workflow to prepare for analysis. Pilot tests modeling transport of 1GeV proton and 800MeV/A oxygen sources show that reasonable results are converged upon in an acceptable amount of overall computation time from drawing preparation to data analysis. The FLUKA radiation transport code will next be used to model both a GCR and a trapped radiation source. These results will then be compared with measurements that have been made by the radiation instrumentation deployed inside the US Lab module. DISCUSSION Early analyses have indicated that the DAGMC workflow is a promising toolkit for running vehicle geometries of interest to NASA through multiple radiation transport codes. In addition, recent work has shown that a realistic human phantom, provided via a subcontract with the University of Florida, can be placed inside any vehicle geometry for a combinatorial analysis. This added functionality gives the user the ability to score various parameters at the organ level, and the results can then be used as input for cancer risk models.

Barzilla, J. E.↗
NASA NTRS2017

Optimal Control Surface Layout for an Aeroservoelastic Wingbox

This paper demonstrates a technique for locating the optimal control surface layout of an aeroservoelastic Common Research Model wingbox, in the context of maneuver load alleviation and active utter suppression. The combinatorial actuator layout design is solved using ideas borrowed from topology optimization, where the effectiveness of a given control surface is tied to a layout design variable, which varies from zero (the actuator is removed) to one (the actuator is retained). These layout design variables are optimized concurrently with a large number of structural wingbox sizing variables and control surface actuation variables, in order to minimize the sum of structural weight and actuator weight. Results are presented that demonstrate interdependencies between structural sizing patterns and optimal control surface layouts, for both static and dynamic aeroelastic physics.

Stanford, Bret K.↗
NASA NTRS2017

The Quantum Approximation Optimization Algorithm for MaxCut: A Fermionic View

Farhi et al. recently proposed a class of quantum algorithms, the Quantum Approximate Optimization Algorithm (QAOA), for approximately solving combinatorial optimization problems. A level-p QAOA circuit consists of steps in which a classical Hamiltonian, derived from the cost function, is applied followed by a mixing Hamiltonian. The 2p times for which these two Hamiltonians are applied are the parameters of the algorithm. As p increases, however, the parameter search space grows quickly. The success of the QAOA approach will depend, in part, on finding effective parameter-setting strategies. Here, we analytically and numerically study parameter setting for QAOA applied to MAXCUT. For level-1 QAOA, we derive an analytical expression for a general graph. In principle, expressions for higher p could be derived, but the number of terms quickly becomes prohibitive. For a special case of MAXCUT, the Ring of Disagrees, or the 1D antiferromagnetic ring, we provide an analysis for arbitrarily high level. Using a Fermionic representation, the evolution of the system under QAOA translates into quantum optimal control of an ensemble of independent spins. This treatment enables us to obtain analytical expressions for the performance of QAOA for any p. It also greatly simplifies numerical search for the optimal values of the parameters. By exploring symmetries, we identify a lower-dimensional sub-manifold of interest; the search effort can be accordingly reduced. This analysis also explains an observed symmetry in the optimal parameter values. Further, we numerically investigate the parameter landscape and show that it is a simple one in the sense of having no local optima.

quantum algorithm↗
NASA NTRS2018

XY vs X Mixer in Quantum Alternating Operator Ansatz for Optimization Problems with Constraints

Quantum Approximate Optimization Algorithm, further generalized as Quantum Alternating Operator Ansatz (QAOA), is a family of algorithms for combinatorial optimization problems. It is a leading candidate to run on emerging universal quantum computers to gain insight into quantum heuristics. In constrained optimization, penalties are often introduced so that the ground state of the cost Hamiltonian encodes the solution (a standard practice in quantum annealing). An alternative is to choose a mixing Hamiltonian such that the constraint corresponds to a constant of motion and the quantum evolution stays in the feasible subspace. Better performance of the algorithm is speculated due to a much smaller search space. We consider problems with a constant Hamming weight as the constraint. We also compare different methods of generating the generalized W-state, which serves as a natural initial state for the Hamming-weight constraint. Using graph-coloring as an example, we compare the performance of using XY model as a mixer that preserves the Hamming weight with the performance of adding a penalty term in the cost Hamiltonian.

quantum computing↗
NASA NTRS2018

Semi-Autonomous Rodent Habitat for Deep Space Exploration

NASA has flown animals to space as part of trailblazing missions and to understand the biological responses to spaceflight. Mice traveled in the Lunar Module with the Apollo 17 astronauts and now mice are frequent research subjects in LEO on the ISS. The ISS rodent missions have focused on unravelling biological mechanisms, better understanding risks to astronaut health, and testing candidate countermeasures. A critical barrier for longer-duration animal missions is the need for humans-in-the-loop to perform animal husbandry and perform routine tasks during a mission. Using autonomous or telerobotic systems to alleviate some of these tasks would enable longer-duration missions to be performed at the Deep Space Gateway. Rodent missions performed using the Gateway as a platform could address a number of critical risks identified by the Human Research Program (HRP), as well as Space Biology Program questions identified by NRC Decadal Survey on Biological and Physical Sciences in Space, (2011). HRP risk areas of potentially greatest relevance that the Gateway rodent missions can address include those related to visual impairment (VIIP) and radiation risks to central nervous system, cardiovascular disease, as well as countermeasure testing. Space Biology focus areas addressed by the Gateway rodent missions include mechanisms and combinatorial effects of microgravity and radiation. The objectives of the work proposed here are to 1) develop capability for semi-autonomous rodent research in cis-lunar orbit, 2) conduct key experiments for testing countermeasures against low gravity and space radiation. The hardware and operations system developed will enable experiments at least one month in duration, which potentially could be extended to one year in duration. To gain novel insights into the health risks to crew of deep space travel (i.e., exposure to space radiation), results obtained from Gateway flight rodents can be compared to ground control groups and separate groups of mice exposed to simulated Galactic Cosmic Radiation (at the NASA Space Radiation Lab). Results can then be compared to identical experiments conducted on the ISS. Together results from Gateway, ground-based, and ISS rodent experiments will provide novel insight into the effects of space radiation.

deep space gateway↗
NASA NTRS2017

Knowledge Base for Distributed Spacecraft Mission Design Using the Trade-Space Analysis Tool for Constellations (TAT-C)

Opportunities for multi-point measurements, greater revisit frequency, failure robustness, and improved cost effectiveness motivate consideration of Distributed Spacecraft Missions (DSMs) for future Earth science missions. However, careful analysis is required to assess the distributed sensing capabilities of a constellation compared to more mature monolithic spacecraft while also considering other important dimensions such as cost and risk. The large combinatorial DSM design space limits existing mission analysis tools and exploration methods which emphasize monolithic design variables. The Trade-space Analysis Tool for Constellations (TAT-C) under development at Goddard seeks to enumerate and evaluate alternative mission architectures to minimize cost and maximize scientific return for pre-defined goals during pre-phase A analysis.Similar to other model-centric engineering efforts, efficient data management is a significant challenge for DSM mission analysis. In TAT-C, a Knowledge Base (KB) is envisioned as a cumulative central repository of information and meta-information about DSMs. Initial KB concepts store related data for reuse within or across mission analyses; however, over time, the KB is envisioned to be an important layer to coordinate actions of both human analysts and automated design agents to search a large design space for desirable mission alternatives. Preliminary KB research builds on a modern web technology stack to provide the following functionality: 1) storage of trade-space search requests which set requirements and constraints for DSM concepts, 2) storage of analysis results which quantify performance metrics for evaluated DSM concepts, 3) a RESTful application programming interface (API) for scripted access to data from TAT-C modules, 4) web-based graphical user interface (GUI) for manual access to underlying data, and 5) access control and management restrictions relevant to data protection and security. These efforts have culminated in a prototype KB used by the research team during TAT-C development to assess opportunities for future work.

Pattern Recognition↗
NASA NTRS2018

QAOA Tutorial Outline

In this tutorial we discuss the quantum alternating operator ansatz (QAOA), which is a variational algorithm that can be used for approximate optimization of combinatorial problems with soft and hard constraints.We go through the design of the quantum circuit and its actual implementation in real hardware, discussing compilation issues such as gate synthesis and scheduling of all the required gates and qubit-swapping overhead.

Venturelli, Davide↗
NASA NTRS2015

Simulation of Liquid Rocket Engine Failure Propagation Using Self-Evolving Scenarios

Traditional probabilistic risk assessment approaches often require failure scenarios to be explicitly defined through event sequences that are then quantified as part of the integrated analysis. This approach becomes difficult when failure propagation paths change as a function of the system operation. Additionally, if the propagation paths represent interactions among even a modest number of components, the scenario count becomes combinatorially intractable. This paper presents an alternate approach for quantifying the probability of failure propagation in such a case. Rather than explicitly defining scenario sequences, simple physical models are created for each of the components. In this way, only the physical states and rules of component interactions must be defined, rather than event sequences for each individual scenario. Initiating failures are introduced into the system, either randomly or as defined by relative likelihood, and the failures cascade through the system via the interaction rules. This process is repeated using Monte Carlo methods and, as a result, the most probable scenarios “self-evolve” in terms of both sequence path and frequency. This approach was applied to failures occurring in the engine compartment of a space launch vehicle with four liquid rocket engines and four high-pressure helium tanks. Each engine was modeled with key components, such as turbomachinery, combustion chamber, propellant lines, and additional support systems. Three test cases were conducted with different high-energy engine failures. End results of interest included an additional engine-out failure and tank burst, which represent the loss-of-mission (LOM) and loss-of-crew (LOC) failure environments, respectively. Observations show that almost every scenario outcome is unique and that many scenarios involve complex chain reactions that are difficult to predict. This validates the usefulness of the modeling approach in assessing the overall risks to the crew during a launch vehicle abort.

Rocket Engine↗
NASA NTRS2017

Quantum Approximate Optimization with Hard and Soft Constraints

Challenging computational problems arising in the practical world are frequently tackled by heuristic algorithms. Small universal quantum computers will emerge in the next year or two, enabling a substantial broadening of the types of quantum heuristics that can be investigated beyond quantum annealing. The immediate question is What experiments should we prioritize that will give us insight into quantum heuristics? One leading candidate is the quantum approximate optimization algorithm (QAOA) metaheuristic. Here, we provide a framework for designing QAOA circuits for a variety of combinatorial optimization problems with both hard constraints that must be met and soft constraints whose violation we wish to minimize. We work through a number of examples, and discuss design principles and implementation considerations.

Hadfield, Stuart↗
NASA NTRSDate not supplied

A Rapid Target-Search Technique for KBO Exploration Trajectories

A rapid, grid-based, target-search algorithm is presented to find candidate se-quences of small-body encounters for mission design. The algorithm is especially relevant for cases with large combinatorial spaces. In this paper, the al-gorithm is used to identify candidate flyby sequences of multiple Kuiper-Belt Ob-jects (KBOs). Before reaching the first KBO in the sequence, the trajectories in this paper first use gravity assists at one or more of the giant planets to pump-uptheir orbital energy—reducing launch C3. The target-search algorithm consists offour sequential steps: (1) parameter definition, (2) fine-tuned Lambert-based gridsearch of ballistic trajectories visiting one KBO, (3) rapid, ∆V-based proximitysearch for additional KBOs using the state transition matrices (STMs), and (4) tra-jectory optimization of the most promising KBO sequences using the EvolutionaryMission Trajectory Generator (EMTG). The paper also defines an empirical-basedprocess to characterize the maximum step size for the target arrival dates in theLambert grid search. Lastly, a candidate mission to two KBOs is presented. Theresults indicate that the ∆V computed from the STM propagations is not repre-sentative of the final ∆V computed in EMTG; however, it does serve as a useful‘reachability’ metric to identify nearby KBOs.

Miguel Benayas Penas↗
NASA NTRS2017

Initial Results of Heuristic Guided Orbit Selection for a Low Frequency Radio Interferometric Spacecraft Constellation

A constellation of radio telescope spacecraft can leverage interferometry to accurately image distant objects throughout the universe, but mission design must balance among many interrelated constraints. In particular, the number of craft and the selection of time-varying orbital parameters play a pivotal role in determining what interferometric baselines are feasible with respect to different targets, and thus drives the breadth and quality of data available to the constellation. The large combinatorial orbit configuration space and competing concerns present a challenging problem that is not well addressed by traditional mission design processes. This paper describes application of automated optimization methods to help direct mission design effort to the most promising dynamic constellation geometries: those that achieve broad interferometric coverage but remain cost-effective and resilient to failures. Several automatic heuristic-driven optimization algorithms representing complementary search strategies were created to explore among concrete constellation configuration plans. Evaluation of each candidate constellation plan was accelerated by efficiently combining precomputed caches of orbital and interferometric data. Results indicate that leveraging automated optimization for constellation mission design is both practical and illuminating: generated solutions provided both evidence for existing design intuitions as well as fresh insights into novel configurations.

Hernandez, Sonia↗
NASA NTRS2020

Scheduling the Mapping of a planet under geometrical constraints

Scheduling the coverage of a planet by scientific instruments on board spacecrafts under observational constraints is central in a significant number of current and future missions for the exploration of the solar system. In this paper, we describe the components and algorithms of a software used to study early-phase mission design or to schedule daily operations of currently in-flight spacecraft. The scheduling problem at hand is usually a large combinatorial problem. A discretization process is described and several ordering algorithms are designed and compared. Experiments show that high-quality schedules can be produced by approaches combining reasoning about rolling, coverage and priorities.

Wells, Christopher↗
NASA NTRS2022

Separating Propulsive Mass and Energy for Space Applications

Initially and traditionally, space access and in-space propulsion utilized combustion and expulsion of chemical “fuels” carried on board, with performance governed by the rocket equation. These chemicals produced energy, and after combustion, constituted the propulsive mass/momentum. The best such chemicals in terms of Isp that are deemed safe engineering/mission wise are hydrogen and oxygen, producing some 450 seconds of Isp. There are more reactive chemicals, such as fluorine, which have higher Isp, but also have serious safety issues. Propulsion in atmospheres can ingest atmospheric constituents (aka “airbreathing propulsion”) which provide additional propulsive mass when heated and a component of the combustion/energy generation process. This utilization of non-stored/carried propulsive mass provides partial separation of propulsive mass and energy and produces higher Isp. The other approach to separating propulsive mass and energy is to, either on board or added from offboard, supply additional energy, such as from nuclear or solar sources. This is the approach for fission nuclear thermal and electric propulsion, which can provide an Isp in excess of 800 seconds of Isp. Other processes or in addition to thermal expansion, such as electro-magnetics, can be employed to increase exit velocity and Isp. Separating propulsive mass and energy for space faring is commonly referred to as a means to circumvent the rocket equation and is capable of producing major benefits for the development of commercial deep space and space faring in general including affordable fast transits to mitigate the human health impacts of galactic cosmic radiation (GCR) and microG. The key to higher than chemical Isp, beyond H2-O2, and beyond the radiated energy and systems limitations of solar for space faring, is a light weight, high energy density source, either on board or via utilization of energy beaming to the vehicle. Traditional energy sources include chemical, heat, electrics, mechanical, photons, and nuclear. Propulsive mass can be carried on board, sourced beyond the surface of Earth via in-situ resource utilization (ISRU), and includes harvesting from atmospheres. High Isp via electromagnetic related propulsive mass acceleration requires sufficient ionization and conductivity. The major metrics for space propulsion are costs, safety, Isp, weight, and thrust level, the latter dependent upon mission requirements. High thrust for human missions is needed to reduce time exposed to radiation and microG and high thrust is required for space access. Costs of space access are reducing via reusability, printing manufacture, and robotization of manufacturing and operation. Chemical rockets provide high thrust from the expansion of the heated mass constituents at high mass flow. The other high thrust propulsion approach is magnetohydrodynamics (MHD), which, in addition to high thrust, has a high Isp of over 2,000 seconds of Isp. The VASIMIR engine offers some 5,000 seconds of Isp at high thrust [ref. 3]. Electric propulsion cycles are capable of Isp much higher than that but at low thrust levels using available energy sources. With cost as a major metric, reusable rockets and improved manufacturing and operations are rapidly greatly lowering the costs of space access, which could provide/supply in space fuel depots and affordable chemical propulsion for the desired human fast transits (e.g., some 200-day round trips to Mars). The other option for fast transits is VASIMIR, given a nuclear on-board energy source that has the requisite many megawatts of power and an alpha, kgs of weight/kW of energy produced, on the order of one (i.e., a light weight, high energy density energy source). The purpose of this report is to examine the options beyond traditional rocket engines, where propulsive mass and energy are combined, specifically the separation of propulsive mass and energy. This report considers the spectrum of advanced energetics, sources of propulsive mass, conductivity enhancement approaches, energy beaming possibilities, and candidate propulsion cycles. Suggestions are made for various combinatorial, system level, beyond traditional combustion rocket, space propulsion approaches for human deep space missions given the changing conditions of increased knowledge of deep space resources for ISRU, revolutionary energetics, and technology advancements writ large.

Dennis M. Bushnell↗
NASA NTRSDate not supplied

Introducing Tropical Geometric Approaches to Delay Tolerant Networking Optimization

Delay Tolerant Networking (DTN) is the standard approach to the networking of space systems with the goal of supporting the Solar System Internet (SSI). Current space networks have a small scale and often depend on rigorously scheduled (pre-determined) contact opportunities; this manual approach inhibits scalability. The goal of this paper is to recast these scheduling problems in order to apply the optimization machinery of tropical geometry. Contact opportunities in space are dependent on such factors as orbital mechanics and asset availability, which induce time-varying connectivity; indeed, end-to-end connectivity might never occur. Routing optimization within this structure is classically difficult and typically utilizes Dijkstra's algorithm as applied to contact graphs. Alternatively, we follow the successes of tropical geometry in train schedule optimization, job assignments, and even traditional networking, by extending this approach to this more general (i.e. disconnected) problem space. These successes imply tropical geometry provides a useful framework in the context of DTNs, starting with applications to queuing theory and long-haul links. Recently, tropical geometry has been applied to parametric path optimization on graphs with variable edge weights. In this work, we extend these advances to account for the problem of routing in a space network, and find that tropical geometry is well-suited to the challenges offered by this new setting, including contact schedules featuring probabilities. Our approach leverages the combinatorial nature of the problem to give feasible shortest path trees in the presence of variable channel conditions and latency, evolving topologies, and uncertainty inherent in space routing. We discuss our tropical approach to DTN for two Python implementations, a Verilog Tropical ALU implementation, tropical frameworks for other parametric graph problems, and solution stability. Lastly, a program for future work is included to illuminate the path ahead.

Delay Tolerant Networking↗
NASA NTRSDate not supplied

Runway Configuration Management with Offline Reinforcement Learning

Runway configuration management (RCM) is a challenging task, and it affects the efficiency of the National Airspace System (NAS) and airport surface operations significantly. Each airport, depending on the geometry, capacity, local climate patterns, etc. has multiple configurations for the runway usage for arriving and departing flights. Many factors such as the incoming/outgoing traffic load, wind direction and speed, convective weather, cloud ceiling and other environmental factors might affect the choice of a runway configuration at any point in time. However, other factors such as safety measures and regulations, noise abatement, capacity of each configuration, and preference of the air traffic controllers (ATCs) can also play a significant role in selecting the configuration. A sub-optimal selection of the runway configuration, or delay in making configuration changes might result in significant increase in taxi times for aircraft on the surface of the airport, fuel and energy use of the aircraft, and maintenance costs. It can also lead to safety concerns, such as an aircraft performing one or more go-arounds before being able to land. All these factors make RCM an extremely important and challenging decision-making process for the ATCs. The current state of practice sets the runway configuration by the ATCs based on relevant information available at the time including weather, traffic, noise abatement, safety bounds, etc. This makes the decision-making process subjective based on the accuracy of the available information and the bias in human decision making. Unfortunately, this approach yields poor results (e.g., significant delays) if the predicted outcomes are uncertain and their relative impact is not well understood. This is especially evident when the uncertainty increases the size of possible predicted outcomes (combinatorial explosion in possible scenarios) that cannot be handled by human reasoning. On the other hand, an automated approach based on machine intelligence can make use of historical data and search through all (or significant amount of) possible scenarios under uncertainty and make well-informed decisions.

Milad Memarzadeh↗
NASA NTRSDate not supplied

Medical countermeasures for extraterrestrial environments: Current status and future prospects with focus on acute injuries

In all space exploratory activities involving humans there are associated risks to health and well-being. Exposure to ionizing radiation represents one of the more serious health concerns. Although medical issues can arise shortly following the rare acute exposures, the more likely radiation-associated injuries will manifest in a delayed fashion following sub-acute exposures or protracted exposures to relatively low radiation doses. The radiological conditions that present these health risks during extraterrestrial space travel are presented in this article, along with current physical and medical countermeasures for such exposure contingencies. The aim of this article is to discuss radiation medical countermeasures that may be considered for future space exploration and travel. Biomedical advances have occurred toward the control and minimization of acute, sub-acute, and fractionated radiation exposure injuries, whether they stem from intentional application of radio-therapeutic procedures or unintentional, accidental terrestrial-based exposures. Those advances, based largely on novel bioengineering, genetic, and combinatorial pharmaceutical strategies, are briefly reviewed here, along with the promising prospects of additional, new and improved medical countering systems/agents that will be forthcoming with additional research and development efforts.

Acute/chronic radiation injuries↗
NASA NTRSDate not supplied

Sex differences and deep space stressors: effects of 5-ion gcrsim, simulated microgravityand social isolation on immune function, brain, and behavior in mice

This project is testing the hypothesis that Ionizing Radiation (IR), microgravity and social isolation combine synergistically to trigger an oxidative stress response that alters immune homeostasis, brain structure/function, and neurobehavioral/cognitive performance. Specific Aims for this project are to: (1) Determine dose-response curves for acute ‘Five-Ion GCR Simulation’ exposure for immune, brain and performance responses in crew age-matched adult male and female mice; (2) Determine effects of acute ‘Five-Ion GCR Simulation’ exposure singly and in combination with simulated microgravity and social isolation, on immune, brain and performance responses in crew age-matched male and female mice mimicking deep space missions; and (3) Determine efficacy of the dietary antioxidant, Nicotinamide Mononucleotide (NMN), a key intermediate in nicotinamide adenine dinucleotide (NAD+) biosynthesis. Here we report findings from our studies of mature (24-week-old) male and female mice exposed to simulated 5-Ion GCRsim (0, 5, 15, or 50cGy) at the NASA Space Radiation Laboratory (NSRL) followed by combinatorial exposures to 15cGy, simulated microgravity via head-down tilt (hindlimb unloading) and social isolation. Immune, brain and behavioral (sensorimotor, risk-taking & cognitive) measures were acquired at ‘Acute’ (IR+24hrs, IR+72hrs), ‘Intermediate’ (IR+14 days) and ‘Delayed’ (IR+28 to IR+124 days) to inform biological responses anticipated during a transit to Mars. This project addresses NASA’s efforts to characterize risks and identify appropriate countermeasures in both women and men in anticipation of future deep space missions. Ensuring crew health and performance during extended transits necessitates that sensorimotor and cognitive abilities remain strong to avoid potentially catastrophic health and safety outcomes. Supported by the NASA Human Research Program (HRP) Human Factors Behavioral Performance Element Grant 18 18FLAG 2 0028.

behavioral sciences↗
NASA NTRSDate not supplied

Optimization Algorithms as Quantum Performance Benchmarks

Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) have the potential to demonstrate significant run-time performance benefits over current state-of-the-art solutions. Using existing methods for characterizing classical optimization algorithms, we analyze solution quality obtained by solving Max-Cut problems using a quantum annealing device and gate-model quantum simulators and devices. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through effective visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. The framework is an enhancement to the existing open-source QED-C Application-Oriented Benchmark suite and can connect to the open-source analysis libraries. The suite can be executed on various quantum simulators and quantum hardware systems.

benchmarking↗