Benchmarking the Operation of Quantum Heuristics and Ising Machines: Scoring Parameter Setting Strategies on Real World Optimization Applications
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David E Bernal Neira↗
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A cosmic ray isotope spectrometer
The paper describes a new instrument to be flown on ISEE-C which is optimized to measure the isotopic composition of solar and galactic cosmic rays in the 5-250 MeV/nucleon range. A mass resolution of not greater than 0.3 AMU should be achieved for all elements with charge Z not exceeding 30.
Althouse, W. E.↗
Instrument for Solvent Extraction and Analysis (ISEE) of Organics from Regolith Simulant Using Supercritical Fluid Extraction and Chromatography
ISEE is an instrument with the potential to perform extractions from regolith found on the surface of asteroids and planets, followed by characterization and quantitation of the extracts using supercritical fluid extraction (SFE) and chromatography (SFC). SFE is a developed technique proven to extract a wide range of organic compounds. SFC is similar to High Performance Liquid Chromatography (HPLC) but has the advantage of performing chiral separations without needing to derivatize the chiral compounds. CO2 will be the solvent for both stages as it is readily available in the Mars atmosphere. ISEE will capture CO2 from the environment, and use it for SFE and SFC. If successful, this would allow ISEE to perform analysis of organic compounds without using consumables. This paper will present results on a preliminary, proof-of-principle effort to use SFE and SFC to extract and analyze lunar regolith simulant spiked with organic compounds representing a range of organics that ISEE would expect to characterize. An optimization of variables for the extraction of the organics from the spiked regolith was successfully developed, using 138 bar pressure and 40 C temperature. The extraction flow rate was optimized at 2% SLPM with 30% methanol modifier. The extractions were successful with a value of 77.3+/- 0.9% of organics extracted. However, the recovery of organics after the extraction was very low with only 48.5+/-14.2%. Moreover, three columns were selected to analyze multiple samples at a time; two of them are Viridis HSS C18 SB and Torus DIOL, and the third column, specific for chiral separations, has not yet been selected yet.
Franco, Carolina↗
Hardware-Efficient Quantum Optimization Layered Algorithms and Experiments
Quantum optimization algorithms, such as QAOA, that implement parametrized stochastic optimization solvers attempt to identify low-energy solutions of Ising systems by exploiting available quantum effects in noisy-intermediate scale machines. Engineering a well-performing parametrized quantum optimization circuit is indeed an exercise in balancing the trade-off between expressivity and implementation complexity. We show that, for MaxCut QAOA circuits defined on native hardware topology (Rigetti’s Aspen Quantum Processors), error-mitigation techniques recover simulated features of the noiseless theory. Moreover, we explore a design space for QAOA-like ansatze that perform well in theory as well as in hardware for fully-connected problems. We also discuss how efficient coherence and entanglement detection methods that could be coupled with quantum optimization experiments require only linear overhead in benchmarking time.
quantum computing↗
Accelerating Continuous Variable Coherent Ising Machines Via Momentum
The Coherent Ising Machine (CIM) is a non-conventional architecture that takes inspiration from physical annealing processes to solve Ising problems heuristically. Its dynamics are naturally continuous and described by a set of ordinary differential equations that have been proven to be useful for the optimization of continuous variables nonconvex quadratic optimization problems. The dynamics of such Continuous Variable CIMs (CV-CIM) encourage optimization via optical pulses whose amplitudes are determined by the negative gradient of the objective; however, standard gradient descent is known to be trapped by local minima and hampered by poor problem conditioning. In this work, we propose to modify the CV-CIM dynamics using more sophisticated pulse injections based on tried-and-true optimization techniques such as momentum and Adam. Through numerical experiments, we show that the momentum and Adam updates can significantly speed up the CV-CIM’s convergence and improve sample diversity over the original CV-CIM dynamics. We also find that the Adam-CV-CIM’s performance is more stable as a function of feedback strength, especially on poorly conditioned instances, resulting in an algorithm that is more robust, reliable, and easily tunable. More broadly, we identify the CIM dynamical framework as a fertile opportunity for exploring the intersection of classical optimization and modern analog computing.
Ising Model↗
A Multilevel Approach For SolvingLarge-Scale QUBO Problems With Noisy Hybrid Quantum Approximate Optimization
Quantum approximate optimization is one ofthe promising candidates for useful quantum computation,particularly in the context of finding approximate solutionsto Quadratic Unconstrained Binary Optimization (QUBO)problems. However, the existing quantum processing units(QPUs) are of relatively small size, and canonical mappingsof QUBO via the Ising model require one qubit per vari-able, rendering direct large-scale optimization infeasible.In classical optimization, a general strategy for addressingmany large-scale problems is via multilevel/multigrid meth-ods, where the large target problem is iteratively coarsenedand the global solution is constructed from multiple small-scale optimization runs. In this work, we experimentallytest how existing QPUs perform when used as a sub-solverwithin such a multilevel strategy. To this aim, we com-bine and extend (via additional classical processing steps)the recently proposed Noise-Directed Adaptive Remapping(NDAR) and Quantum Relax&Round (QRR) algorithms.We first demonstrate the effectiveness of our heuristicextensions on Rigetti’s superconducting transmon deviceAnkaa-2. We find approximate solutions to10instances offully connected82-qubit Sherrington-Kirkpatrick graphswith random integer-valued coefficients obtaining normal-ized approximation ratios (ARs) in the range∼0.98−1.0,and the same class with real-valued coefficients (ARs∼0.94−1.0). Then, we implement the extended NDAR andQRR algorithms as subsolvers in the multilevel algorithmfor6large-scale graphs with at most∼27,000variables.In practice, the QPU (with classical post-processing steps)is used to find approximate solutions to dozens of at most82-qubit problems, which are iteratively used to constructthe global solution. We observe that quantum optimizationresults are competitive in terms of the quality of solutionswhen compared to classical heuristics used as subsolverswithin the multilevel approach.Reproducibility: source code and data are available at[TBA upon acceptance]
quantum computing↗
Model reduction using new optimal Routh approximant technique
An optimal Routh approximant of a single-input single-output dynamic system is a reduced-order transfer function of which the denominator is obtained by the Routh approximation method while the numerator is determined by minimizing a time-response integral-squared-error (ISE) criterion. In this paper, a new elegant approach is presented for obtaining the optimal Routh approximants for linear time-invariant continuous-time systems. The approach is based on the Routh canonical expansion, which is a finite-term orthogonal series of rational basis functions, and minimization of the ISE criterion. A procedure for combining the above approach with the bilinear transformation is also presented in order to obtain the optimal bilinear Routh approximants of linear time-invariant discrete-time systems. The proposed technique is simple in formulation and is amenable to practical implementation.
Hwang, Chyi↗
QSPIN: A High Level Java API for Quantum Computing Experimentation
QSPIN is a high level Java language API for experimentation in QC models used in the calculation of Ising spin glass ground states and related quadratic unconstrained binary optimization (QUBO) problems. The Java API is intended to facilitate research in advanced QC algorithms such as hybrid quantum-classical solvers, automatic selection of constraint and optimization parameters, and techniques for the correction and mitigation of model and solution errors. QSPIN includes high level solver objects tailored to the D-Wave quantum annealing architecture that implement hybrid quantum-classical algorithms [Booth et al.] for solving large problems on small quantum devices, elimination of variables via roof duality, and classical computing optimization methods such as GPU accelerated simulated annealing and tabu search for comparison. A test suite of documented NP-complete applications ranging from graph coloring, covering, and partitioning to integer programming and scheduling are provided to demonstrate current capabilities.
Quantu↗
Mission design for a halo orbiter of the earth
The International Sun-Earth Explorer (ISEE) scientific satellite to be stationed in 1978 in the vicinity of the sun-earth interior libration point to continuously monitor the space between the sun and the earth, including the distant geomagnetic tail is described. Orbit selection considerations for the ISEE-C are discussed along with stationkeeping requirements and fuel-optimal trajectories. Due to the alignment of the interior libration point with the sun as viewed from the earth, it will be necessary to place the satellite into a 'halo orbit' around the libration point, in order to eliminate solar interference with down-link telemetry. Parametric data for transfer trajectories between an earth parking orbit (altitude about 185 km) and a libration-point orbit are presented. It is shown that the insertion magnitude required for placing a satellite into an acceptable halo orbit is rather modest.
Farquhar, R. W.↗
International Space Station Radiation Shielding Model Development
The projected radiation levels within the International Space Station (ISS) have been criticized by the Aerospace Safety Advisory Panel in their report to the NASA Administrator. Methods for optimal reconfiguration and augmentation of the ISS shielding are now being developed. The initial steps are to develop reconfigurable and realistic radiation shield models of the ISS modules, develop computational procedures for the highly anisotropic radiation environment, and implement parametric and organizational optimization procedures. The targets of the redesign process are the crew quarters where the astronauts sleep and determining the effects of ISS shadow shielding of an astronaut in a spacesuit. The ISS model as developed will be reconfigurable to follow the ISS. Swapping internal equipment rack assemblies via location mapping tables will be one option for shield optimization. Lightweight shield augmentation materials will be optimally fit to crew quarter areas using parametric optimization procedures to minimize the augmentation shield mass. The optimization process is being integrated into the Intelligence Synthesis Environment s (ISE s) immersive simulation facility at the Langley Research Center and will rely on High Performance Computing and Communication (HPCC) for rapid evaluation of shield parameter gradients.
Qualls, G. D.↗
Hematopoietic Stem Cell Therapy as a Counter-Measure for Human Exploration of Deep Space
Human exploration of deep space depends, in part, on our ability to counter severe/invasive disorders that astronauts experience in space environments. The known symptoms include hematological/cardiac abnormalities,bone and muscle losses, immunodeficiency, neurological disorders, and cancer. Exploiting the extraordinary plasticity of hematopoietic stem cells (HSCs), which differentiate not only to all types of blood cells, but also to various tissues, we have advanced a hypothesis that ome of the space-caused disorders maybe amenable to hematopoietis stem cell therapy(HSCT) so as to maintain promote human exploration of deep space. Using mouse models of human anemia beta-thaiassemia) as well as spaceflight (hindlimb unloading system), we have obtained feasibility results of HSCT for space anemia, muscle loss, and immunodeficiency. For example, in the case of HSCT for muscle loss, the beta-galactosidese marked HSCs were detected in the hindlimbs of unloaded mouse following transplantation by -X-gal wholemaunt staining procedure. Histochemicaland physical analyses indicated structural contribution of HSCs to the muscle. HSCT for immunodeficiency was investigated ising beta-galactosidese gene-tagged Escherichia coli as the infectious agent. Results of the X-gal staining procedure indicated the rapeutic role of the HSCT. To facilitate the HSCT in space, growth of HSCs were optimized in the NASA Rotating Wall Vessel (RWV) culture systems, including Hydrodynamic Focusing Bioreactor (HFB).
Ohi, S.↗
Quantum-Assisted Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data
The quantum computing community has been searching for suitable applications to demonstrate the potential of near-term quantum devices. Quantum machine learning is a potential candidate, particularly using models that cannot be efficiently simulated with classical computers [1, 2]. This work focuses on a transition phase of quantum computers where the quantum machine learning model is still simulable classically but projected not to be simulable as the size of the model grows. Ultimately quantum computers may have advantages for high-dimensional real-world problems. Due to the limited number of qubits in current noisy intermediate-scale quantum (NISQ) devices, the direct application of quantum computers in high dimensional data is not feasible. To remedy this problem, an encoder-decoder architecture can be utilized. The encoder model would transform the high-dimensional data into a compact representation, to a level that small quantum computers can be used today (or in the near future), and the decoder would take the quantum processed outputs back to the high-dimensional space. Addressing the two challenges of quantum machine learning, this work investigates a hybrid supervised generative model with a quantum Ising Born machine embedded as the latent distribution. The model contains four main parts (Figure 1.a.): (1) a U-NET architecture responsible for learning segmentation flow, (2) a Prior network responsible for learning an encoded latent distribution of the input data, (3) a Born machine which represents the latent distribution, and (4) a Posterior network in charge of learning the joint encoded latent distribution of inputs and target data. The initial model, proposed by [3], is optimized by (1) maximizing the overlap of the prior and posterior latent distributions, and (2) minimizing the segmentation loss. The proposed model is designed to be investigated in a simulation environment applied to the real-world application of wildfire segmentation. Specifically, the model is designed to solve the patchy wildfire segmentations of Moderate Resolution Imaging Spectroradiometer (MODIS) by taking the MODIS observations and using Visible Infrared Imaging Radiometer Suite’s (VIIRS) consistent wildfire product as the target. The model solves patchy wildfire segmentations and provides insight into the epistemic errors sourced from model variation. The model utilizes the Born machine as a QUBO solver to represent the latent space as a Bernoulli distribution. The proposed configuration allows the variational segmentation model to leverage the true quantum probabilistic nature and derive a more expressive latent configuration, increasing the model performance in describing wildfire segmentations. The quantum probabilistic information of the Born machine is directly incorporated in the Kullback-Leibler divergence loss in the prior and posterior distributions, forcing the Bernoulli latent distribution to maximize the overlap of input and joint input-target distributions. The proposed model is then trained and compared with a baseline only consisting of direct Bernoulli latent distribution with no Born machine representing the latent space. The models are evaluated based on the segmentation metrics, such as precision, recall, intersect of union, with uncertainty boundaries accounting for the stochastic nature of the model. Our findings show that even in low latent-dimensional space (due to the limit in computational power of the classical quantum simulator), we are able to effectively capture the latent representation and hence the model performs better than the baseline. The findings are a projection for scaling the model into higher dimensional latent space with the Born machine surpassing the baseline performance. Figure 1. Sub-figure (a) demonstrates the architecture for the training phase. The model consists of a Prior and Posterior network that encode inputs and joint input-target data into compact representations, respectively. The Born machine represents the latent distribution, and the U-NET branch learns the segmentation patterns of the data. The stochasticity is introduced to the U-NET through its last layer to create meaningful but stochastic segmentations. Sub-figure (b) represents the inference phase where the model takes the stochastic behavior from the prior network and injects that into the U-NET. Each attempt of inference will generate different but similar segmentations from the same distribution of the wildfire event. REFERENCES [1] Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020). The Born supremacy: quantum advantage and training of an Ising Born machine. npj Quantum Information, 6(1), 1-11. [2] Liu, J. G., & Wang, L. (2018). Differentiable learning of quantum circuit born machines. Physical Review A, 98(6), 062324. [3] Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K., ... & Ronneberger, O. (2018). A probabilistic u-net for segmentation of ambiguous images. Advances in neural information processing systems, 31.
quantum machine learning↗
Mars Surveyor Program '01 Mars Environmental Compatibility Assessment wet chemistry lab: a sensor array for chemical analysis of the Martian soil
The Mars Environmental Compatibility Assessment (MECA) instrument was designed, built, and flight qualified for the now canceled MSP (Mars Surveyor Program) '01 Lander. The MECA package consisted of a microscope, electrometer, material patch plates, and a wet chemistry laboratory (WCL). The primary goal of MECA was to analyze the Martian soil (regolith) for possible hazards to future astronauts and to provide a better understanding of Martian regolith geochemistry. The purpose of the WCL was to analyze for a range of soluble ionic chemical species and electrochemical parameters. The heart of the WCL was a sensor array of electrochemically based ion-selective electrodes (ISE). After 20 months storage at -23 degrees C and subsequent extended freeze/thawing cycles, WCL sensors were evaluated to determine both their physical durability and analytical responses. A fractional factorial calibration of the sensors was used to obtain slope, intercept, and all necessary selectivity coefficients simultaneously for selected ISEs. This calibration was used to model five cation and three anion sensors. These data were subsequently used to determine concentrations of several ions in two soil leachate simulants (based on terrestrial seawater and hypothesized Mars brine) and four actual soil samples. The WCL results were compared to simulant and soil samples using ion chromatography and inductively coupled plasma optical emission spectroscopy. The results showed that flight qualification and prolonged low-temperature storage conditions had minimal effects on the sensors. In addition, the analytical optimization method provided quantitative and qualitative data that could be used to accurately identify the chemical composition of the simulants and soils. The WCL has the ability to provide data that can be used to "read" the chemical, geological, and climatic history of Mars, as well as the potential habitability of its regolith.
Non-NASA Center↗
Alternative gravity-assist sequences for the ISEE-3 escape trajectory
The International Sun-Earth Explorer-3 (ISEE-3) was moved from its mission halo orbit upstream of the earth's magnetosphere on June 10, 1982. Multiple lunar swingby maneuvers were then used to shape the trajectory for extensive exploration of the distant geomagnetic tail and finally to place the spacecraft on a course that will intercept the comet Giacobini-Zinner on September 11, 1985. With this new mission objective, the spacecraft was renamed the International Cometary Explorer (ICE). A double-lunar-swingby control technique involving five passes by the moon was eventually implemented to accomplish this, but in the process of finding this solution several feasible, though less optimal, alternative trajectory sequences implementing the same procedure were discovered. This paper provides a descriptive comparison of these very different orbital profiles and serves to illustrate the utility and great flexibility of this orbital control procedure. Targeting methodology for finding constrained solutions in this space is also presented.
Muhonen, D.↗
The ISEE 1 and 2 Medium Energy Particles Experiment
The Medium Energy Particles Experiment (MEPE) on board ISEE 1 and 2 consists of the WIM instrument on ISEE 1 and the KED instrument on ISEE 2. Both instruments employ solid-state detectors and magnetic analysis to measure the angular, energy, and intensity distributions of protons (ions) above 24 keV and electrons above 20 keV. The WIM instrument also includes a composition measurement employing Delta E-by-E and time-of-flight techniques. Three-parameter analysis is performed above 250 keV/nucleon, and single parameter analysis is performed above 125 keV/nucleon for helium through oxygen. Three-dimensional angular distributions are obtained through the use of a scan platform in the WIM instrument and multiple detector heads in the KED instrument. A variety of operational modes are used to optimize data collection from both instruments. Resolutions up to 128 channels in energy, 192 samples over the unit sphere in angle, and 0.095 sec in time are available.
Williams, D. J.↗
A Small Spacecraft Swarm Deployment and Stationkeeping Strategy for Sun-Earth L1 Halo Orbits
Spacecraft orbits about the Sun-Earth librarian point L1 have been of interest since the 1950s. An L1 halo orbit was first achieved with the International Sun-Earth Explorer-3 (ISEE-3) mission, and similar orbits around Sun-Earth L1 were achieved in the Solar and Heliospheric Observatory (SOHO), Advanced Composition Explorer (ACE), Genesis, and Deep Space Climate Observatory (DSCOVR) missions. With recent advancements in CubeSat technology, we envision that it will soon be feasible to deploy CubeSats at L1. As opposed to these prior missions where one large satellite orbited alone, a swarm of CubeSats at L1 would enable novel science data return, providing a topology for intersatellite measurements of heliophysics phenomena both spatially and temporally, at varying spatial scales.The purpose of this iPoster is to present a flight dynamics strategy for a swarm of numerous CubeSats orbiting Sun-Earth L1. The presented method is a coupled, two-part solution. First, we present a deployment strategy for theCubeSats that is optimized to produce prescribed, time-varying intersatellite baselines for the purposes of collectingmagnetometer data as well as radiometric measurements from cross-links. Second, we employ a loose controlstrategy that was successfully applied to SOHO and ACE for minimized stationkeeping propellant expenditure. Weemphasize that the presented solution is practical within the current state-of-the-art and heritage CubeSat technology,citing capabilities of CubeSat designs that will launch on the upcoming Exploration Mission 1 (EM-1) to lunar orbitsand beyond. Within this iPoster, we present animations of the simulated deployment strategy and resulting spacecrafttrajectories. Mission design parameters such as total v required for long-term station keeping andminimum/maximum/mean spacecraft separation distances are also presented.
Optimal control↗