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Quantum technologies exploit entanglement to enhance various tasks beyond their classical limits including computation, communication and measurements. Quantum metrology aims to increase the precision of a measured quantity that is estimated in the presence of statistical errors using entangled quantum states. We present a novel approach for finding (near) optimal states for metrology in the presence of noise, using variational techniques as a tool for efficiently searching the high-dimensional space of quantum states, which would be classically intractable. We comprehensively explore systems consisting of up to 9 qubits and find new highly entangled states that are not symmetric under permutations and non-trivially outperform previously known states up to a constant factor 2. We consider a range of environmental noise models; while passive quantum states cannot achieve a fundamentally superior scaling (as established by prior asymptotic results) we do observe a significant absolute quantum advantage. We finally outline a possible experimental setup for variational quantum metrology which can be implemented in near-term hardware.

We introduce QuEST, the Quantum Exact Simulation Toolkit, and compare it to ProjectQ, qHipster and a recent distributed implementation of Quantum++. QuEST is the first open source, hybrid multithreaded and distributed, GPU accelerated simulator of universal quantum circuits. Embodied as a C library, it is designed so that a user’s code can be deployed seamlessly to any platform from a laptop to a supercomputer. QuEST is capable of simulating generic quantum circuits of general one and two-qubit gates and multi-qubit controlled gates, on pure and mixed states, represented as state-vectors and density matrices, and under the presence of decoherence. Using the ARCUS and ARCHER supercomputers, we benchmark QuEST’s simulation of random circuits of up to 38 qubits, distributed over up to 2048 compute nodes, each with up to 24 cores. We directly compare QuEST’s performance to ProjectQ’s on single machines, and discuss the differences in distribution strategies of QuEST, qHipster and Quantum++. QuEST shows excellent scaling, both strong and weak, on multicore and distributed architectures.

Imaginary time evolution is a powerful tool for studying quantum systems. While it is possible to simulate with a classical computer, the time and memory requirements generally scale exponentially with the system size. Conversely, quantum computers can efficiently simulate quantum systems, but not non-unitary imaginary time evolution. We propose a variational algorithm for simulating imaginary time evolution on a hybrid quantum computer. We use this algorithm to find the ground-state energy of many-particle systems; specifically molecular hydrogen and lithium hydride, finding the ground state with high probability. Our method can also be applied to general optimisation problems and quantum machine learning. As our algorithm is hybrid, suitable for error mitigation and can exploit shallow quantum circuits, it can be implemented with current quantum computers.

Recent research into the spatiotemporal dynamics of eDNA in marine environments indicates that eDNA signals are highly localized and may dissipate beyond detection levels within a few hours of production. This affects whether single‐timepoint eDNA sampling, which generally occurs during daylight hours, or cyclic (day/night) interval eDNA sampling is necessary to detect both diurnal and nocturnal marine species. Our study investigated short‐term variability in eDNA derived from fishes and macroinvertebrates across three temperate reef sites in eastern Tasmania, Australia. Simultaneous eDNA and underwater visual census (UVC) diver transect surveys were conducted every 6 h over a 24‐h period to investigate whether eDNA was able to detect marine species outside of their UVC‐observed diel activity. We report that single‐timepoint eDNA sampling can detect both diurnal and nocturnal species on temperate reefscapes. A lack of eDNA compositional turnover between day and night suggests that eDNA persists beyond 12 h and/or is continuously produced by both diurnal and nocturnal reef taxa, irrespective of diel behavioral changes observed by UVC. Given high eDNA sample variability, however, we recommend a high replication level (> 10 × 1 L samples) to produce robust site community composition profiles. This study builds on emerging literature on short‐term variability in eDNA, assisting in the design of future eDNA studies at sites with pronounced variation in faunal activity between day and night.

Whether and how landscape context and habitat traits combine to shape animal assemblages and the rate and distribution of ecological functions remains unresolved in many aquatic settings. Saltmarshes are one such ecosystem in which these considerations are frequently acknowledged as important, but quantitative studies of these effects are rare, especially for ecological functions. In this study, the influence of landscape configuration and habitat traits on the composition of fish assemblages and rates of predation were quantified around 30 saltmarshes in three estuaries (i.e., 10 per estuary) in eastern Australia. Fish assemblages were surveyed using unbaited underwater video cameras, and predation was quantified using videoed “Squidpop” predation assays at 10 sites at each saltmarsh. The structure of fish assemblages was best explained by the estuary in which saltmarsh was located, the proximity of sites to estuary mouth, and the area of nearby saltmarsh and mangroves. Predation was dominated (90% of total predation events) by yellowfin bream Acanthopagrus australis (Sparidae), and so rates of predation correlated positively with yellowfin bream abundance. Predation peaked in the lower reaches of estuaries at saltmarshes with lower vegetation cover. These findings suggest that the mouths of estuaries might function as key transition zones that concentrate prey, the products of trophic relay, and the ecological effects of predators near the estuarine-sea interface.

Human pressures on ecosystems from landscape transformation and harvesting can result in changes to body size and functional traits of affected species. However, these effects remain very poorly understood in many settings. Here we examine whether and how fishing and the attributes of coastal seascapes can operate in concert to change the body size and functional traits of the giant mud crab, Scylla serrata; a prized fisheries species. We captured 65 legal sized (> 15 cm carapace width) male giant mud crabs from 13 estuaries in southeast Queensland, Australia. These estuaries span a wide range of fishing and catchment landscape transformation intensity. We made a total of 9000 external morphometric measurements in the study. There was a distinct effect of estuarine landscape context on body size, with the largest individuals captured from systems with bigger inlets and lower extent of intertidal flats. Variation in functional traits was most often associated with variation in fishing pressure and human population size in the catchment. Crabs from areas with less commercial fishing pressure and lower human populations in the catchment had the largest chelipeds. We also found effects of urbanisation (negative correlations), intertidal flats (inconsistent effects) and mangrove extent (positive correlations) on the size of some functional traits. Our results show that human pressures can have sublethal effects on animals in estuaries that alter body size and functional traits. These phenotypic responses might have consequences for the fitness and ecological roles of targeted species, and the yields of fisheries catches.

Human society is dependent on nature 1 , 2 , but whether our ecological foundations are at risk remains unknown in the absence of systematic monitoring of species’ populations 3 . Knowledge of species fluctuations is particularly inadequate in the marine realm 4 . Here we assess the population trends of 1,057 common shallow reef species from multiple phyla at 1,636 sites around Australia over the past decade. Most populations decreased over this period, including many tropical fishes, temperate invertebrates (particularly echinoderms) and southwestern Australian macroalgae, whereas coral populations remained relatively stable. Population declines typically followed heatwave years, when local water temperatures were more than 0.5 °C above temperatures in 2008. Following heatwaves 5 , 6 , species abundances generally tended to decline near warm range edges, and increase near cool range edges. More than 30% of shallow invertebrate species in cool latitudes exhibited high extinction risk, with rapidly declining populations trapped by deep ocean barriers, preventing poleward retreat as temperatures rise. Greater conservation effort is needed to safeguard temperate marine ecosystems, which are disproportionately threatened and include species with deep evolutionary roots. Fundamental among such efforts, and broader societal needs to efficiently adapt to interacting anthropogenic and natural pressures, is greatly expanded monitoring of species’ population trends 7 , 8 . A systematic census at 1,636 sites around Australia from 2008 to 2021 finds that more than 30% of shallow invertebrate species in cool latitudes exhibit a high extinction risk due to declining populations and oceanic barriers, but tropical coral species remain relatively stable.

The year is 2022. Scientists and engineers inch ever closer to building a practical quantum computer. The excitement in the research community, that we might soon fulfil Feynman’s dream to leverage quantum mechanics in machines capable of exponentially quicker computation, is steadily growing. With promised revolutions in chemistry, condensed matter physics and machine learning, and an expected market of 1.8 billion USD by 2026, quantum computing fights for the spotlight amid international pandemics and climate catastrophe. But the journey ahead is not an easy one. Quantum computers, requiring precise control of extraordinarily delicate quantum systems, are unsurprisingly challenging to engineer and equally difficult to design. A demonstration of quantum advantage through the quantum solving of a practical problem faster than available classical means, remains a research ambition.At the forefront of this research are classical computers: the machines which crunch the numbers in our calculations, which interface with our quantum experiments, and which render this very document. Without them, quantum computation would have remained a fanciful whim on Feynman’s chalkboard. Behind the rapidly growing repertoire of quantum algorithms lies an equally impressive and expanding corpus of classical simulation strategies.This thesis is about utilising first generation quantum computers and predicting their behaviour using classical computers. It develops novel quantum algorithms to perform variational minimisation, Hamiltonian diagonalisation, and approximate circuit recompilation, all intendedly compatible with near-future machines. It also devises classical algorithms for efficiently simulating quantum variational algorithms, emulating quantum computers using high-performance supercomputing facilities, and showcases the author’s efforts in scientific software development. Incidentally, this thesis makes no direct use of current day quantum hardware facilities. We hope to convince the disappointed reader that such an endeavour is presently pointless.

Students attending school today can have a wide variety of needs in the classroom, both academically and behaviorally. One student group often regarded as challenging to support is students who have an emotional disturbance (ED; see discussion in Algozzine, 2017). Students with an ED usually require a different level of support to be successful at school and in life due to a disability that impacts their ability to manage their emotions and behavior (Reddy et al., 2009). Research also shows that students with an ED can exhibit dangerous behaviors and fail to succeed in their transition to adulthood (Cullinan et al., 2023; Lambert et al., 2021). While some research exists around supporting students with an ED, there is an opportunity for more research on the experiences of the educators who serve these children.This qualitative single case study collected the voices of 54 educators in a Texas public school district who work with students with an ED to identify the most and least effective educator approaches that help students have more positive behaviors and success in school. In addition, I used the broaden and build theory of positive emotions to determine a connection between intentional exposure to positive emotions and the benefits it can provide to students with an ED (Fredrickson, 2013).Findings from this research show specific approaches that teachers indicate are most successful for students with an ED, including building strong relationships, calm and consistent environments, clear expectations, use of positive reinforcements, and offering student choice. The findings also show that harsh communication, lack of an established relationship, focusing on punitive responses to behaviors, and unrealistic expectations are the least effective approaches when working with students with an ED. Additionally, teacher feedback on the broaden and build theory of positive emotions supports the idea that when students experience specific positive emotions, it can result in additional positive behaviors moving forward. I also provide recommendations and implications for future practice following a review and analysis of the findings.

Decomposing a matrix into a weighted sum of Pauli strings is a common chore of the quantum computer scientist, whom is not easily discouraged by exponential scaling. But beware, a naive decomposition can be cubically more expensive than necessary! In this manuscript, we derive a fixed-memory, branchless algorithm to compute the inner product between a 2^N-by-2^N complex matrix and an N-term Pauli tensor in O(2^N) time, by leveraging the Gray code. Our scheme permits the embarrassingly parallel decomposition of a matrix into a weighted sum of Pauli strings in O(8^N) time. We implement our algorithm in Python, hosted open-source on Github, and benchmark against a recent state-of-the-art method called the \"PauliComposer\" which has an exponentially growing memory overhead, achieving speedups in the range of 1.5x to 5x for N < 8. Note that our scheme does not leverage sparsity, diagonality, Hermitivity or other properties of the input matrix which might otherwise enable optimised treatment in other methods. As such, our algorithm is well-suited to decomposition of dense, arbitrary, complex matrices which are expected dense in the Pauli basis, or for which the decomposed Pauli tensors are a priori unknown.