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Optimization problems pose challenges across various fields. In recent years, quantum annealers have emerged as a promising platform for tackling such challenges. To provide a new perspective, we develop a heuristic tensor-network-based algorithm to reveal the low-energy spectrum of Ising spin-glass systems with interaction graphs relevant to present-day quantum annealers. Our deterministic approach combines a branch-and-bound search strategy with an approximate calculation of marginals via tensor-network contractions. Its application to quasi-two-dimensional lattices with large unit cells of up to 24 spins, realized in current quantum annealing processors, requires a dedicated approach that utilizes sparse structures in the tensor network representation and GPU hardware acceleration. We benchmark our approach on random problems defined on Pegasus and Zephyr graphs with up to a few thousand spins, comparing it against the D-Wave Advantage quantum annealer and Simulated Bifurcation algorithm, with the latter representing an emerging class of classical Ising solvers. Apart from the quality of the best solutions, we compare the diversity of low-energy states sampled by all the solvers. For the biggest considered problems with over 5000 spins, the state-of-the-art tensor network approaches lead to solutions that are $0.1\%$ to $1\%$ worse than the best solutions obtained by Ising machines while being two orders of magnitude slower. We attribute those results to approximate contraction failures. While all three methods can output diverse low-energy solutions, e.g., differing by at least a quarter of spins with energy error below $1\%$, our deterministic branch-and-bound approach finds sets of a few such states at most. On the other hand, both Ising machines prove capable of sampling sets of thousands of such solutions., Comment: 15+7 pages, 12+7 figures

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, distributed quantum-assisted probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability., Comment: 50 pages, 42 figures

This study introduces a novel self-supervised learning approach for volumetric segmentation of defect indications captured by phased array ultrasonic testing data from Carbon Fiber Reinforced Polymers (CFRPs). By employing this self-supervised method, defect segmentation is achieved automatically without the need for labelled training data or examples of defects. The approach has been tested using artificially induced defects, including back-drilled holes and Polytetrafluoroethylene (PTFE) inserts, to mimic different defect responses. Additionally, it has been evaluated on stepped geometries with varying thickness, demonstrating impressive generalization across various test scenarios. Minimal preprocessing requirements are needed, with no removal of geometric features or Time-Compensated Gain (TCG) necessary for applying the methodology. The model's performance was evaluated for defect detection, in-plane and through-thickness localisation, as well as defect sizing. All defects were consistently detected with thresholding and different processing steps able to remove false positive indications for a 100% detection accuracy. Defect sizing aligns with the industrial standard 6 dB amplitude drop method, with a Mean Absolute Error (MAE) of 1.41 mm. In-plane and through-thickness localisation yielded comparable results, with MAEs of 0.37 and 0.26 mm, respectively. Visualisations are provided to illustrate how this approach can be utilised to generate digital twins of components., Comment: 20 pages

Portfolio diversification, traditionally measured through asset correlations and volatilitybased metrics, is fundamental to managing financial risk. However, existing diversification metrics often overlook non-numerical relationships between assets that can impact portfolio stability, particularly during market stresses. This paper introduces the lexical ratio (LR), a novel metric that leverages textual data to capture diversification dimensions absent in standard approaches. By treating each asset as a unique document composed of sectorspecific and financial keywords, the LR evaluates portfolio diversification by distributing these terms across assets, incorporating entropy-based insights from information theory. We thoroughly analyze LR's properties, including scale invariance, concavity, and maximality, demonstrating its theoretical robustness and ability to enhance risk-adjusted portfolio returns. Using empirical tests on S&P 500 portfolios, we compare LR's performance to established metrics such as Markowitz's volatility-based measures and diversification ratios. Our tests reveal LR's superiority in optimizing portfolio returns, especially under varied market conditions. Our findings show that LR aligns with conventional metrics and captures unique diversification aspects, suggesting it is a viable tool for portfolio managers.

This work presents steps toward the design of Majorana zero modes (MZM) in InAsP quantum dots (QD) embedded in an InP semiconducting nanowire in contact with a p-type superconductor described by the Kitaev Hamiltonian. The single particle spectrum is obtained from million atom atomistic calculations with QNANO and many-electron spectra using exact diagonalization (ED) and the hybrid Variational Quantum Eigensolver (VQE) method. A variational ansatz is constructed to capture the ground state of the system by utilizing a generalized form of the analytical solution for a particular set of parameters. By systematically deviating from the analytically solvable regime while maintaining the system in the topological phase (TP), the effectiveness of the variational function in reproducing the correct ground state and topological properties of the system is evaluated. This is done through a quantum algorithm for a many-body state containing MZM. The results are compared with exact solution in topological phase and demonstrate the capability of VQE, along with classical simulations, to accurately model the many-body spectra in topologically nontrivial state.

Causal models seek to unravel the cause-effect relationships among variables from observed data, as opposed to mere mappings among them, as traditional regression models do. This paper introduces a novel causal discovery algorithm designed for settings in which variables exhibit linearly sparse relationships. In such scenarios, the causal links represented by directed acyclic graphs (DAGs) can be encapsulated in a structural matrix. The proposed approach leverages the structural matrix's ability to reconstruct data and the statistical properties it imposes on the data to identify the correct structural matrix. This method does not rely on independence tests or graph fitting procedures, making it suitable for scenarios with limited training data. Simulation results demonstrate that the proposed method outperforms the well-known PC, GES, BIC exact search, and LINGAM-based methods in recovering linearly sparse causal structures.

Group IV GeSn double-heterostructure (DHS) lasers offer unique advantages of a direct bandgap and CMOS compatibility. However, further improvements in laser performance have been bottlenecked by limited junction properties of GeSn through conventional epitaxy and wafer bonding. This work leverages semiconductor grafting to synthesize and characterize optically pumped ridge edge-emitting lasers (EELs) with an AlGaAs nanomembrane (NM) transfer-printed onto an epitaxially grown GeSn substrate, interfaced by an ultrathin Al2O3 layer. The grafted AlGaAs/GeSn DHS lasers show a lasing threshold of 11.06 mW at 77 K and a maximum lasing temperature of 130 K. These results highlight the potential of the grafting technique for enhancing charge carrier and optical field confinements, paving the way for room-temperature electrically injected GeSn lasers., Comment: 5 pages, 5 figures. Supplementary Information included

In-memory computing (IMC) has been shown to be a promising approach for solving binary optimization problems while significantly reducing energy and latency. Building on the advantages of parallel computation, we propose an IMC-compatible parallelism framework inspired by parallel tempering (PT), enabling cross-replica communication to improve the performance of IMC solvers. This framework enables an IMC solver not only to improve performance beyond what can be achieved through parallelization, but also affords greater flexibility for the search process with low hardware overhead. We justify that the framework can be applied to almost any IMC solver. We demonstrate the effectiveness of the framework for the Boolean satisfiability (SAT) problem, using the WalkSAT heuristic as a proxy for existing IMC solvers. The resulting PT-inspired cooperative WalkSAT (PTIC-WalkSAT) algorithm outperforms the traditional WalkSAT heuristic in terms of the iterations-to-solution in 76.3% of the tested problem instances and its na\"ive parallel variant (PA-WalkSAT) does so in 68.4% of the instances. An estimate of the energy overhead of the PTIC framework for two hardware accelerator architectures indicates that in both cases the overhead of running the PTIC framework would be less than 1% of the total energy required to run each accelerator., Comment: 21 pages, 9 figures

The vast majority of 21st century AI workloads are based on gradient-based deterministic algorithms such as backpropagation. One of the key reasons for the dominance of deterministic ML algorithms is the emergence of powerful hardware accelerators (GPU and TPU) that have enabled the wide-scale adoption and implementation of these algorithms. Meanwhile, discrete and probabilistic Monte Carlo algorithms have long been recognized as one of the most successful algorithms in all of computing with a wide range of applications. Specifically, Markov Chain Monte Carlo (MCMC) algorithm families have emerged as the most widely used and effective method for discrete combinatorial optimization and probabilistic sampling problems. We adopt a hardware-centric perspective on probabilistic computing, outlining the challenges and potential future directions to advance this field. We identify two critical research areas: 3D integration using MOSAICs (Monolithic/Stacked/Assembled ICs) and the concept of Illusion, a hardware-agnostic distributed computing framework designed to scale probabilistic accelerators., Comment: 2 pages, 1 figure

One-third of Mars' surface has shallow-buried H$_2$O, but it is currently too cold for use by life. Proposals to warm Mars using greenhouse gases require a large mass of ingredients that are rare on Mars' surface. However, we show here that artificial aerosols made from materials that are readily available at Mars-for example, conductive nanorods that are ~9 $\mu$m long-could warm Mars >5 $\times$ 10$^3$ times more effectively than the best gases. Such nanoparticles forward-scatter sunlight and efficiently block upwelling thermal infrared. Similar to the natural dust of Mars, they are swept high into Mars' atmosphere, allowing delivery from the near-surface. For a particle lifetime of 10 years, two climate models indicate that sustained release at 30 liters/sec would globally warm Mars by $\gtrsim$30 K and start to melt the ice. Therefore, if nanoparticles can be made at scale on (or delivered to) Mars, then the barrier to warming of Mars appears to not be as high as previously thought.

GeSn-based SWIR lasers featuring imaging, sensing, and communications has gained dynamic development recently. However, the existing SiGeSn/GeSn double heterostructure lacks adequate electron confinement and is insufficient for room temperature lasing. The recently demonstrated semiconductor grafting technique provides a viable approach towards AlGaAs/GeSn p-i-n heterojunctions with better electron confinement and high-quality interfaces, promising for room temperature electrically pumped GeSn laser devices. Therefore, understanding and quantitatively characterizing the band alignment in this grafted heterojunction is crucial. In this study, we explore the band alignment in the grafted monocrystalline Al0.3Ga0.7As /Ge0.853Sn0.147 p-i-n heterojunction. We determined the bandgap values of AlGaAs and GeSn to be 1.81 eV and 0.434 eV by photoluminescence measurements, respectively. We further conducted X-ray photoelectron spectroscopy measurements and extracted a valence band offset of 0.19 eV and a conduction band offset of 1.186 eV. A Type-I band alignment was confirmed which effectively confining electrons at the AlGaAs/GeSn interface. This study improves our understanding of the interfacial band structure in grafted AlGaAs/GeSn heterostructure, providing experimental evidence of the Type-I band alignment between AlGaAs and GeSn, and paving the way for their application in laser technologies., Comment: 18 pages, 4 figures

In recent years, the negatively charged group-IV--vacancy defects in diamond, labeled as G4V($-$) or G4V centers, have received a great attention in quantum information processing. In this study, we investigate the magneto-optical properties of the G4V centers under high compressive hydrostatic pressures up to 180~GPa. The spin-orbit splitting for the electronic ground and excited states and the hyperfine tensors are calculated by means of plane wave supercell density functional theory as unique fingerprints of these defects. To this end, we developed a theory for calculating the hyperfine tensors when the electronic states are subject to Jahn--Teller effect. We find that the zero-phonon-line energy increases with adding hydrostatic pressures where the coupling strength increases from SiV($-$) to PbV($-$). On the other hand, the calculated photoionization threshold energies indicate that the operation of PbV($-$) based quantum sensor is limited up to 30~GPa of hydrostatic pressure whereas SnV($-$), GeV($-$) and SiV($-$) remain photostable up to 180~GPa of hydrostatic pressure., Comment: 16 pages, 7 figures, 7 tables

This study presents the fabrication and characterizations of an Al$_{0.3}$Ga$_{0.7}$As/Ge$_{0.87}$Sn$_{0.13}$/GeSn p-i-n double heterostructure (DHS) diode following the grafting approach for enhanced optoelectronic applications. By integrating ultra-thin Al$_2$O$_3$ as a quantum tunneling layer and enhancing interfacial double-side passivation, we achieved a heterostructure with a substantial 1.186 eV conduction band barrier between AlGaAs and GeSn, along with a low interfacial density of states. The diode demonstrated impressive electrical characteristics with high uniformity, including a mean ideality factor of 1.47 and a mean rectification ratio of 2.95E103 at +/-2 V across 326 devices, indicating high-quality device fabrication. Comprehensive electrical characterizations, including C-V and I-V profiling, affirm the diode's capability to provide robust electrical confinement and efficient carrier injection. These properties make the Al$_{0.3}$Ga$_{0.7}$As/Ge$_{0.87}$Sn$_{0.13}$/GeSn DHS a promising candidate for next-generation electrically pumped GeSn lasers, potentially operable at higher temperatures. Our results provide a viable pathway for further advancements in various GeSn-based devices., Comment: 5 pages, 4 figures

The transition to 100% renewable energy requires new techniques for managing energy networks, such as dividing them into sensible subsets of prosumers called micro-grids. Doing so in an optimal manner is a difficult optimization problem, as it can be abstracted to the Coalition Structure Generation problem in Induced Subgraph Games, a NP-complete problem which requires dividing an undirected, complete, weighted graph into subgraphs in a way that maximizes the sum of their internal weights. Recently, Venkatesh et al. (arXiv:2212.11372) published a Quantum Annealing (QA)-based iterative algorithm called GCS-Q, which they claim to be the best currently existing solver for the problem in terms of runtime complexity. As this algorithm makes the application of QA to the problem seem promising, but is a greedy one, this work proposes several less greedy QA-based approaches and investigates whether any of them can outperform GCS-Q in terms of solution quality. While we find that this is not the case yet on D-Wave hardware, most of them do when using the classical QBSolv software as a solver. Especially an algorithm we call 4-split iterative R-QUBO shows potential here, finding all optima in our dataset while scaling favorably with the problem size in terms of runtime. Thus, it appears to be interesting for future research on quantum approaches to the problem, assuming QA hardware will become more noise-resilient over time., Comment: 7 pages, 4 figures (8 if counting subfigures). To be published in the proceedings of the 2024 IEEE International Conference on Quantum Computing and Engineering (QCE)

Electric Vehicles (EVs) are emerging as battery energy storage systems (BESSs) of increasing importance for different power grid services. However, the unique characteristics of EVs makes them more difficult to operate than dedicated BESSs. In this work, we apply a data-driven learning approach to leverage EVs as a BESS to provide capacity-related services to the grid. The approach uses machine learning to predict how to charge and discharge EVs while satisfying their operational constraints. As a paradigm application, we use flexible energy commercialization in the wholesale markets, but the approach can be applied to a broader range of capacity-related grid services. We evaluate the proposed approach numerically and show that when the number of EVs is large, we can obtain comparable objective values to CPLEX and approximate dynamic programming, but with shorter run times. These reduced run times are important because they allow us to (re)optimize frequently to adapt to the time-varying system conditions.

Power grid partitioning is an important requirement for resilient distribution grids. Since electricity production is progressively shifted to the distribution side, dynamic identification of self-reliant grid subsets becomes crucial for operation. This problem can be represented as a modification to the well-known NP-hard Community Detection (CD) problem. We formulate it as a Quadratic Unconstrained Binary Optimization (QUBO) problem suitable for solving using quantum computation{\color{blue}, which is expected to find better-quality partitions faster. The formulation aims to find communities with maximal self-sufficiency and minimal power flowing between them}. To assess quantum optimization for sizeable problems, we apply a hierarchical divisive method that solves sub-problem QUBOs to perform grid bisections. Furthermore, we propose a customization of the Louvain heuristic that includes self-reliance. In the evaluation, we first demonstrate that this problem examines exponential runtime scaling classically. Then, using different IEEE power system test cases, we benchmark the solution quality for multiple approaches: D-Wave's hybrid quantum-classical solvers, classical heuristics, and a branch-and-bound solver. As a result, we observe that the hybrid solvers provide very promising results, both with and without the divisive algorithm, regarding solution quality achieved within a given time frame. Directly utilizing D-Wave's Quantum Annealing (QA) hardware shows inferior partitioning.

In this note, we propose a generalized notion of distance between vacua in the theory of a scalar field $\phi$ with scalar potential $V(\phi)$ coupled to gravity. We propose the normalized tension of domain wall connecting different field values, with a varying normalization relative to a local energy scale, as the distance. We show this definition reproduces the usual moduli space distance for zero potential, as well as the $d\propto |\log \Lambda|$ behavior with the vacuum energy $\Lambda$ in the AdS case, previously proposed in the literature. In the case of large AdS we also obtain the expected exponent of mass versus distance in one particular case, when the mass of the light tower is $m\sim \sqrt \Lambda$ and there is a single extra dimension decompactifying. We also discuss the features and shortcomings of alternative but related proposals., Comment: 27 pages, 3 figures, typos corrected, references added

Recent Large Language Models (LLMs) have demonstrated impressive capabilities at tasks that require human intelligence and are a significant step towards human-like artificial intelligence (AI). Yet the performance of LLMs at reasoning tasks have been subpar and the reasoning capability of LLMs is a matter of significant debate. While it has been shown that the choice of the prompting technique to the LLM can alter its performance on a multitude of tasks, including reasoning, the best performing techniques require human-made prompts with the knowledge of the tasks at hand. We introduce a framework for what we call Combinatorial Reasoning (CR), a fully-automated prompting method, where reasons are sampled from an LLM pipeline and mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The framework investigates whether QUBO solutions can be profitably used to select a useful subset of the reasons to construct a Chain-of-Thought style prompt. We explore the acceleration of CR with specialized solvers. We also investigate the performance of simpler zero-shot strategies such as linear majority rule or random selection of reasons. Our preliminary study indicates that coupling a combinatorial solver to generative AI pipelines is an interesting avenue for AI reasoning and elucidates design principles for future CR methods., Comment: 13 pages, 3 figures

Component inspection is often the bottleneck in high-value manufacturing, driving industries like aerospace toward automated inspection technologies. Current systems often employ fixed arm robots, but they lack the flexibility in adapting to new components or orientations Advanced mobile robotic platforms with updated sensor technologies and algorithms have improved localization and path planning capabilities, making them ideal for bringing inspection processes directly to parts. However, mobile platforms introduce challenges in localization and maneuverability, leading to potential errors. Their positional uncertainty is higher than fixed systems due to the lack of a fixed calibrated location, posing challenges for position-sensitive inspection sensors. Therefore, it's essential to assess the positional accuracy and repeatability of mobile manipulator platforms. The KUKA KMR iiwa was chosen for its collaborative features, robust build, and scalability within the KUKA product range. The accuracy and repeatability of the mobile platform were evaluated through a series of tests to evaluate the performance of its integrated feature mapping, the effect of various speeds on positional accuracy, and the efficiency of the omnidirectional wheels for a range of translation orientations. Experimental evaluation revealed that enabling feature mapping substantially improves the KUKA KMR iiwa's performance, with accuracy gains and error reductions exceeding 90%. Repeatability errors were under 7 mm with mapping activated and around 2.5 mm in practical scenarios, demonstrating that mobile manipulators, incorporating both the manipulator and platform, can fulfil the precise requirements of industries with high precision needs. Providing a highly diverse alternative to traditional fixed-base industrial manipulators.

Deep neural networks have demonstrated remarkable efficacy in extracting meaningful representations from complex datasets. This has propelled representation learning as a compelling area of research across diverse fields. One interesting open question is how beneficial representation learning can be for quantum many-body physics, with its notouriosly high-dimensional state space. In this work, we showcase the capacity of a neural network that was trained on a subset of physical observables of a many-body system to partially acquire an implicit representation of the wave function. We illustrate this by demonstrating the effectiveness of reusing the representation learned by the neural network to enhance the learning process of another quantity derived from the quantum state. In particular, we focus on how the pre-trained neural network can enhance the learning of entanglement entropy. This is of particular interest as directly measuring the entanglement in a many-body system is very challenging, while a subset of physical observables can be easily measured in experiments. We show the pre-trained neural network learns the dynamics of entropy with fewer resources and higher precision in comparison with direct training on the entanglement entropy.

In this paper, we investigate the effects of the {\ae}ther field and the electric charge on the observed shadow of two types of charged black holes in the Einstein-Maxwell-{\AE}ther theory. By considering that the Einstein-Maxwell-{\AE}ther black holes surrounded by the static/infalling spherical accretion flows, as well as an optically and geometrically thin disk accretion flow, we study the shadow luminosities and the observed specific intensity of the image for these various profiles of accretion flows. We find that in the thin disk accretion model the location and the emitted model of the accretion gas affect on the optical appearance of charged Einstein-{\AE}ther black holes in contrast to the spherical accretion flows. For a thin disk profile, we show that the observer will receive more intensity for an emitted model as Gaussian function when the innermost radiation radius lies in the innermost stable circular orbit. Finally, comparing the results of the charged Einstein-{\AE}ther black holes with neutral Einstein-{\AE}ther black holes, we show that the charged Einstein-{\AE}ther black holes have smaller dark area, whereas wider lensed ring and photon ring. Comparing the charged Einstein-{\AE}ther black holes with Reissner-Nordstrom black hole gives a reverse effect., Comment: 21 pages, 13 figures

The formation of energy communities is pivotal for advancing decentralized and sustainable energy management. Within this context, Coalition Structure Generation (CSG) emerges as a promising framework. The complexity of CSG grows rapidly with the number of agents, making classical solvers impractical for even moderate sizes (number of agents>30). Therefore, the development of advanced computational methods is essential. Motivated by this challenge, this study conducts a benchmark comparing classical solvers with quantum annealing on Dwave hardware and the Quantum Approximation Optimization Algorithm (QAOA) on both simulator and IBMQ hardware to address energy community formation. Our classical solvers include Tabu search, simulated annealing, and an exact classical solver. Our findings reveal that Dwave surpasses QAOA on hardware in terms of solution quality. Remarkably, QAOA demonstrates comparable runtime scaling with Dwave, albeit with a significantly larger prefactor. Notably, Dwave exhibits competitive performance compared to the classical solvers, achieving solutions of equal quality with more favorable runtime scaling.

We present a model in which the Gauss-Bonnet invariant holds the quintessence at a fixed point, respecting an initial $Z_2$ symmetry in the radiation-dominated era. This results in an early dark energy that becomes significant around the matter-radiation equality era. However, due to $Z_2$ symmetry breaking, scalarization occurs, leading to a rapid reduction in the early dark energy density. The model then quickly behaves like the $\Lambda$CDM model. This scenario may also explain the reduction of sound horizon and aligns with the assumption that the gravitational wave speed is infinitesimally close to the speed of light., Comment: 18 pages, 6 figures. Major revision. Accepted for publication in Physics Letters B

Conditional Neural Processes (CNPs) constitute a family of probabilistic models that harness the flexibility of neural networks to parameterize stochastic processes. Their capability to furnish well-calibrated predictions, combined with simple maximum-likelihood training, has established them as appealing solutions for addressing various learning problems, with a particular emphasis on meta-learning. A prominent member of this family, Convolutional Conditional Neural Processes (ConvCNPs), utilizes convolution to explicitly introduce translation equivariance as an inductive bias. However, ConvCNP's reliance on local discrete kernels in its convolution layers can pose challenges in capturing long-range dependencies and complex patterns within the data, especially when dealing with limited and irregularly sampled observations from a new task. Building on the successes of Fourier neural operators (FNOs) for approximating the solution operators of parametric partial differential equations (PDEs), we propose Spectral Convolutional Conditional Neural Processes (SConvCNPs), a new addition to the NPs family that allows for more efficient representation of functions in the frequency domain.

In this project summary paper, we summarize the key results and use-cases explored in the German Federal Ministry of Education and Research (BMBF) funded project "Q-GRID" which aims to assess potential quantum utility optimization applications in the electrical grid. The project focuses on two layers of optimization problems relevant to decentralized energy generation and transmission as well as novel energy transportation/exchange methods such as Peer-2-Peer energy trading and microgrid formation. For select energy grid optimization problems, we demonstrate exponential classical optimizer runtime scaling even for small problem instances, and present initial findings that variational quantum algorithms such as QAOA and hybrid quantum annealing solvers may provide more favourable runtime scaling to obtain similar solution quality. These initial results suggest that quantum computing may be a key enabling technology in the future energy transition insofar that they may be able to solve business problems which are already challenging at small problem instance sizes., Comment: 12 pages. arXiv admin note: text overlap with arXiv:2309.05502

Physics-based Ising machines (IM) have been developed as dedicated processors for solving hard combinatorial optimization problems with higher speed and better energy efficiency. Generally, such systems employ local search heuristics to traverse energy landscapes in searching for optimal solutions. Here, we quantify and address some of the major challenges met by IMs by extending energy-landscape geometry visualization tools known as disconnectivity graphs. Using efficient sampling methods, we visually capture landscapes of problems having diverse structure and hardness manifesting as energetic and entropic barriers for IMs. We investigate energy barriers, local minima, and configuration space clustering effects caused by locality reduction methods when embedding combinatorial problems to the Ising hardware. To this end, we sample disconnectivity graphs of PUBO energy landscapes and their different QUBO mappings accounting for both local minima and saddle regions. We demonstrate that QUBO energy landscape properties lead to the subpar performance for quadratic IMs and suggest directions for their improvement.

This paper confronts the pressing challenges of sixth-generation (6G) wireless communication networks by harnessing the unique capabilities of Unmanned Aerial Vehicles (UAVs). With the ambitious promises of 6G, including ultra-reliable 1 Tbps data delivery and ultra-low latency, the demand for innovative solutions becomes imperative. Traditional terrestrial base stations, though effective, exhibit limitations in scenarios requiring ubiquitous connectivity, prompting the integration of UAVs. In response to these challenges, we introduce a comprehensive solution. This involves UAVs collaboratively downloading desired content from service providers, and subsequently establishing secure connections with users for efficient content exchange. Accordingly, we introduce two new protocols: a collaborative group data downloading scheme among UAVs called SeGDS, and SeDDS for secure direct data sharing through out-of-band autonomous Device-to-Device (D2D) communication. Leveraging certificateless signcryption and certificateless multi-receiver encryption, these protocols offer lightweight, certificate-free solutions with features such as user revocation, non-repudiation, and mutual authentication. Prioritizing high availability, the proposed protocols effectively detect Denial of Service (DoS) and free riding attacks. A thorough evaluation underscores the superiority of the proposed protocols in both security and efficiency over existing models; SeDDS reduces overall computation by 3x, imposing a lighter communication load on UAVs, while SeGDS meets swarm UAV security requirements, reducing communication costs by 4x with low computation cost.