Entanglement-Induced Barren Plateaus
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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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Connecting Ansatz Expressibility to Gradient Magnitudes and Barren Plateaus
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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Consumption Patterns of a Generalist Omnivore: Eastern Box Turtle Diets in the Long Island Pine Barrens
Eastern Box Turtles (Terrapene carolina) are diet generalists and as such are predicted to have diverse diets in which familiar, low-quality foods are eaten consistently at low levels, and high-quality foods are rare but eaten whenever available. Previous work showed that they feed opportunistically on seasonally available plants (shoots, leaves, flowers, and fruit), invertebrates, mushrooms, and occasionally carrion. We used fecal samples to test optimal foraging predictions relevant to diet generalists and also whether the Eastern Box Turtle diets varied seasonally in a northeastern U.S. pine-oak habitat. We found that in-depth prey species consumption patterns of six different individuals were similar to those of the sampled population overall. Leaf and stem material was consumed by 100% of the turtles in all months despite being lower-quality than other prey available. Invertebrates were consumed by at least 80% of turtles in every study period; Coleopterans were found more commonly than other invertebrates. Snails were not eaten by more than 20% of the turtles in any study period, and mushroom consumption varied from 31–75% of samples in different study periods. Monthly diet overlap was measured using both Pianka’s Index of Overlap (PIO) and the Morisita–Horn Index (MH). The PIO method indicated that the prey consumption patterns were broadly similar from June–October, while the M–H method showed that only the July vs. August comparison was highly similar. The turtle diets changed only slightly between seasons, and they conform to predictions of diet generalist models usually applied to mammals.
54 ENVIRONMENTAL SCIENCES↗
Assessment of the Barren Ground Caribou Die‐off During Winter 2015–2016 Using Passive Microwave Observations
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C. Dolant↗
Investigating parameter trainability in the SNAP-displacement protocol of a qudit system
Here, in this study, we explore the universality of Selective Number-dependent Arbitrary Phase (SNAP) and Displacement gates for quantum control in qudit-based systems. However, optimizing the parameters of these gates poses a challenging task. Our main focus is to investigate the sensitivity of training any of the SNAP parameters in the SNAP-Displacement protocol. We analyze conditions that could potentially lead to the Barren Plateau problem in a qudit system and draw comparisons with multi-qubit systems. The parameterized ansatz we consider consists of blocks, where each block is composed of hardware operations, namely SNAP and Displacement gates [Fösel et al 2020 Efficient cavity control with snap gates arXiv:2004.14256]. Applying Variational Quantum algorithm (VQA) with observable and gate cost functions, we utilize techniques similar to those in [McClean et al 2018 Barren plateaus in quantum neural network training landscapes Nat. Commun.9 1–6] and [Cerezo et al 2021 Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nat. Commun.12 1–12] along with the concept of t − design. Through this analysis, we make the following key observations: (a) The trainability of a SNAP-parameter does not exhibit a preference for any particular direction within our cost function landscape, (b) By leveraging the first and second moments properties of Haar measures, we establish new lemmas concerning the expectation of certain polynomial functions, and (c) utilizing these new lemmas, we identify a general condition that indicates an expected trainability advantage in a qudit system when compared to multi-qubit systems.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
AmeriFlux FLUXNET-1F US-Dix Fort Dix
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Dix Fort Dix. This is the FLUXNET version of the carbon flux data for the site US-Dix Fort Dix produced by applying the standard ONEFlux (1F) software. Site Description - The Fort Dix site is located in the upland forests of the New Jersey Pine Barrens, the largest continuous forested landscape on the Northeastern coastal plain. Upland forests occupy 62% of the 1.1 million acre Pine Barrens and can be divided into three dominant stand types, Oak/Pine (19.1%), Pine/Oak (13.1%), and Pitch Pine/Scrub oak (14.3%). The majority of mature upland forests are the product of regeneration following late 19th century logging and charcoaling activities. Gypsy moths first appeared in the Pine Barrens of New Jersey in 1966. Since the time of arrival, the upland forest stands have undergone several episodes of defoliation, the most significant occurred in 1972, 1981, and 1990. In recent years, the overstory oaks and understory oaks and shrubs of the Fort Dix stand, underwent two periods of defoliation by Gypsy moth, in 2006 and 2007. During these two years, maximum leaf area reached only 70% of the 2005 summer maximum.
Clark, Kenneth↗
Subtleties in the trainability of quantum machine learning models
A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called quantum machine learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. However, its success usually hinges on efficiently training the parameters in quantum neural networks, and the field of QML is still lacking theoretical scaling results for their trainability. Some trainability results have been proven for a closely related field called variational quantum algorithms (VQAs). While both fields involve training a parametrized quantum circuit, there are crucial differences that make the results for one setting not readily applicable to the other. In this work, we bridge the two frameworks and show that gradient scaling results for VQAs can also be applied to study the gradient scaling of QML models. Our results indicate that features deemed detrimental for VQA trainability can also lead to issues such as barren plateaus in QML. Consequently, our work has implications for several QML proposals in the literature. In addition, we provide theoretical and numerical evidence that QML models exhibit further trainability issues not present in VQAs, arising from the use of a training dataset. We refer to these as dataset-induced barren plateaus. These results are most relevant when dealing with classical data, as here the choice of embedding scheme (i.e., the map between classical data and quantum states) can greatly affect the gradient scaling.
97 MATHEMATICS AND COMPUTING↗
Analyzing the Quantum Approximate Optimization Algorithm: Ansätze, Symmetries, and Lie Algebras
The quantum approximate optimization algorithm (QAOA) has been proposed as a method to obtain approximate solutions for combinatorial optimization tasks. In this work, we study the underlying algebraic properties of three QAOA ansätze for the maximum-cut problem on connected graphs, while focusing on the generated Lie algebras as well as their invariant subspaces. Specifically, we analyze the standard QAOA ansatz as well as the orbit and multiangle ansätze. We are able to fully characterize the Lie algebras of the multiangle ansatz across arbitrary connected graphs, finding that they only fall into one of just six families. Aside from the cycle and path graphs, the Lie dimensions for every graph are exponentially large in the system size, meaning that multiangle ansätze are extremely prone to exhibiting barren plateaus. Then, a similar quasi-graph-independent Lie-algebraic characterization beyond the multiangle ansatz is impeded as the circuit exhibits additional “hidden” symmetries besides those naturally arising from a certain parity-superselection operator and all automorphisms of the considered graph. Disregarding the “hidden” symmetries, we can upper bound the dimensions of the orbit and the standard Lie algebras, and the dimensions of the associated invariant subspaces are determined via explicit character formulas. To finish, we conjecture that (for most graphs) the standard Lie algebras have only components that are either exponential or that grow, at most, polynomially with the system size. This would imply that the QAOA is either prone to barren plateaus or classically simulable. More generally, our work provides a symmetry framework and tools to analyze any desired variational quantum algorithm.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Theoretical guarantees for permutation-equivariant quantum neural networks
Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential. For instance, models based on quantum neural networks (QNNs) can suffer from excessive local minima and barren plateaus in their training landscapes. Recently, the nascent field of geometric quantum machine learning (GQML) has emerged as a potential solution to some of those issues. The key insight of GQML is that one should design architectures, such as equivariant QNNs, encoding the symmetries of the problem at hand. Here, we focus on problems with permutation symmetry (i.e., symmetry group $S_n$), and show how to build $S_n$-equivariant QNNs We provide an analytical study of their performance, proving that they do not suffer from barren plateaus, quickly reach overparametrization, and generalize well from small amounts of data. To verify our results, we perform numerical simulations for a graph state classification task. Our work provides theoretical guarantees for equivariant QNNs, thus indicating the power and potential of GQML.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Contribution of Tibetan Plateau ecosystems to local and remote precipitation through moisture recycling
The ecosystems of the Tibetan Plateau (TP) provide multiple important ecosystem services that benefit both local populations and those beyond, such as through climate regulation services on precipitation for East Asia and China. However, the precipitation regulation service of the TP ecosystems for supplying moisture and maintaining precipitation is yet to be evaluated. In this study, we used the moisture recycling framework and a moisture tracking model to quantify the precipitation regulation services of TP ecosystems for their contribution to precipitation. We found TP ecosystems contributed substantially to local and downwind precipitation, with a declined contribution from 221 mm/year for the TP and neighboring areas, to <10 mm/year for eastern China and other surrounding countries. Among ecosystem types, grassland contributed most to precipitation, followed by barren and snow lands, forests, and shrublands. In terms of seasonality, precipitation contribution from TP ecosystems was greater in summer months than in non-summer months for western China, while the opposite was true for eastern China – though the magnitude was much smaller. Over the past two decades, the significant evapotranspiration increases in TP translated to a widespread increase in precipitation contribution for TP and downwind beneficiary regions from 2000 to 2020. Our study provides a quantitative way to understand the precipitation regulation services of TP ecosystems through moisture recycling, substantiating their key role to safeguard precipitation and the water cycle for downwind regions– effectively acting as an ecological security shelter that could be perceived by the public.
Li, Yan↗
Measurement-induced entanglement phase transitions in variational quantum circuits
Variational quantum algorithms (VQAs), which classically optimize a parametrized quantum circuit to solve a computational task, promise to advance our understanding of quantum many-body systems and improve machine learning algorithms using near-term quantum computers. Prominent challenges associated with this family of quantum-classical hybrid algorithms are the control of quantum entanglement and quantum gradients linked to their classical optimization. Known as the barren plateau phenomenon, these quantum gradients may rapidly vanish in the presence of volume-law entanglement growth, which poses a serious obstacle to the practical utility of VQAs. Inspired by recent studies of measurement-induced entanglement transition in random circuits, we investigate the entanglement transition in variational quantum circuits endowed with intermediate projective measurements. Considering the Hamiltonian Variational Ansatz (HVA) for the XXZ model and the Hardware Efficient Ansatz (HEA), we observe a measurement-induced entanglement transition from volume-law to area-law with increasing measurement rate. Moreover, we provide evidence that the transition belongs to the same universality class of random unitary circuits. Importantly, the transition coincides with a “landscape transition” from severe to mild/no barren plateaus in the classical optimization. Our work may provide an avenue for improving the trainability of quantum circuits by incorporating intermediate measurement protocols in currently available quantum hardware.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Section 3 Reporting: Preserve America
Brookhaven National Laboratory (BNL) is a multidisciplinary laboratory with seven Nobel Prize-winning discoveries, 37 R&D 100 Awards, and countless advances in science and technology. For over 75 years, the Laboratory has played a leading role in the Department of Energy (DOE) Science and Technology mission and continues to contribute to the DOE’s missions in energy resources, environmental quality, and national security. The Laboratory is operated and managed by Brookhaven Science Associates (BSA), which was founded by the Research Foundation for the State University of New York on behalf of Stony Brook University, and Battelle, a non-profit applied science and technology organization. BNL is committed to longstanding partnerships with researchers, academic institutions, industry, students, teachers, and the surrounding community. BNL is located near the center of Suffolk County on Long Island, New York within Brookhaven Township, about 60 miles east of New York City. Most of BNL’s principal facilities are located near the center of the 5,265-acre (8.23 square mile) site (Figure 1). The developed area encompasses approximately 1,820 acres, consisting of: ▪ 500 acres originally developed by the Army (as part of WW II Camp Upton) and still used for offices and other operational buildings. ▪ 200 acres occupied by large, specialized research facilities. ▪ 520 acres occupied by outlying facilities, such as the Sewage Treatment Plant, research agricultural fields, housing facilities, and fire breaks. ▪ 400 acres of roads, parking lots, and connecting areas. ▪ 200 acres developed for the Long Island Solar Farm. The balance of the site, approximately 3,400 acres, is largely wooded and represents a native pine barrens ecosystem. In November 2000, DOE set aside 530 acres of undeveloped land at BNL as the Upton Ecological and Research Reserve. The Upton Reserve preserves this portion of the pine barrens ecosystem and provides an area for ecological research and education activities.
99 GENERAL AND MISCELLANEOUS↗
LANDSAT Remote Sensing: Observations of an Appalachian mountaintop surface coal mining and reclamation operation
The potential benefits of using LANDSAT remote sensing data by state agencies as an aide in monitoring surface coal mining operations are reviewed. A mountaintop surface mine in eastern Kentucky was surveyed over a 5 year period using satellite multispectral scanner data that were classified by computer analyses. The analyses were guided by aerial photography and by ground surveys of the surface mines procured in 1976. The application of the LANDSAT data indicates that: (1) computer classification of the various landcover categories provides information for monitoring the progress of surface mining and reclamation operations; (2) successive yearly changes in barren and revegetated areas can be qualitatively assessed for surface mines of 100 acres or more of disrupted area; (3) barren areas consisting of limestone and shale mixtures may be recognized, and revegetated areas in various stages of growth may be identified against the hilly forest background.
Source record↗
Surface Albedo Darkening from wildfires in Northern Sub-Saharan Africa
Wildfires are recognized as a key physical disturbance of terrestrial ecosystems and a major source of atmospheric trace gases and aerosols. They are known to produce changes in landscape patterns and lead to changes in surface albedo that can persist for long periods. Here, we estimate the darkening of surface albedo due to wildfires in different land cover ecosystems in the Northern Sub-Saharan Africa using data from the Moderate Resolution Imaging Spectroradiometer (MODIS). We determined a decrease in albedo after fires over most land cover types (e.g. woody savannas: (-0.00352 0.00003) and savannas: (- 0.003910.00003), which together accounted for >86% of the total MODIS fire count between 2003 and 2011). Grasslands had a higher value (-0.00454 0.00003) than the savannas, but accounted for only about 5% of the total fire count. A few other land cover types (e.g. Deciduous broad leaf: (0.00062 0.00015), and barren: 0.00027 0.00019), showed an increase in albedo after fires, but accounted for less than 1% of the total fires. Albedo change due to wildfires is more important during the fire season (October-February). The albedo recovery progresses rapidly during the first year after fires, where savannas show the greatest recovery (>77%) within one year, while deciduous broadleaf, permanent wetlands and barren lands show the least one-year recovery (56%). The persistence of surface albedo darkening in most land cover types is limited to about six to seven years, after which at least 98% of the burnt pixels recover to their pre-fire albedo.
Wildfires↗
On the practical usefulness of the Hardware Efficient Ansatz
Variational Quantum Algorithms (VQAs) and Quantum Machine Learning (QML) models train a parametrized quantum circuit to solve a given learning task. The success of these algorithms greatly hinges on appropriately choosing an ansatz for the quantum circuit. Perhaps one of the most famous ansatzes is the one-dimensional layered Hardware Efficient Ansatz (HEA), which seeks to minimize the effect of hardware noise by using native gates and connectives. The use of this HEA has generated a certain ambivalence arising from the fact that while it suffers from barren plateaus at long depths, it can also avoid them at shallow ones. In this work, we attempt to determine whether one should, or should not, use a HEA. We rigorously identify scenarios where shallow HEAs should likely be avoided (e.g., VQA or QML tasks with data satisfying a volume law of entanglement). More importantly, we identify a Goldilocks scenario where shallow HEAs could achieve a quantum speedup: QML tasks with data satisfying an area law of entanglement. We provide examples for such scenario (such as Gaussian diagonal ensemble random Hamiltonian discrimination), and we show that in these cases a shallow HEA is always trainable and that there exists an anti-concentration of loss function values. Our work highlights the crucial role that input states play in the trainability of a parametrized quantum circuit, a phenomenon that is verified in our numerics.
97 MATHEMATICS AND COMPUTING↗
Evaluation of common trap types for capturing wild pigs
Abstract Wild pigs ( Sus scrofa ) are one of the most ecologically harmful invasive vertebrates globally. Due to their negative impacts, wild pig populations are managed via trapping, yet studies investigating various trap designs have been limited in duration, location, and number of trappers. Using data from professional trappers across the southeastern U.S., we evaluated the performance of 3 wild pig trap types (corral, drop, passive net), and the effect of forage season (barren, green‐up, masting) on trapping success. Specifically, we compared trap effectiveness (estimated proportion of targeted wild pigs caught per capture event), trap night effort (number of days to first catch), and the amount of bait and time used per individual take over the lifetime of each trap location across the 3 trap types and forage seasons. Across >850 capture events by 31 trappers, all 3 trap types evaluated were effective at capturing entire social groups of wild pigs. Few differences were observed among trap types, although drop traps in masting season captured a greater estimated proportion of wild pigs per capture event compared to other trap type‐season combinations. Our data also revealed that passive net traps required slightly more bait/wild pig captured than drop traps but there was no difference in the time/wild pig captured among traps. Our findings demonstrate that when operated by wildlife professionals, multiple designs of wild pig traps can be effective at removing targeted groups of wild pigs. Further, our results offer insights regarding the performance of wild pig trap types across seasons that can be used to inform wild pig trapping programs.
Taylor, Charles R. [Warnell School of Forestry and↗
Efficient Trainability of Linear Optical Modules in Quantum Optical Neural Networks
The existence of “barren plateau landscapes” for generic discrete-variable quantum neural networks, which obstructs efficient gradient-based optimization of cost functions defined by global measurements, would be surprising in the case of generic linear optical modules in quantum optical neural networks due to the tunability of the intensity of continuous variable states and the relevant unitary group having exponentially smaller dimension. We demonstrate that coherent light in m modes can be generically compiled efficiently if the total intensity scales sublinearly with m, and extend this result to cost functions based on homodyne, heterodyne, or photon detection measurement statistics, and to noisy cost functions in the presence of attenuation. Here, we further demonstrate efficient trainability of m mode linear optical quantum circuits for variational mean field energy estimation of positive quadratic Hamiltonians for input states that do not have energy exponentially vanishing with m.
47 OTHER INSTRUMENTATION↗
Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover
Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = −0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.
54 ENVIRONMENTAL SCIENCES↗