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Current quantum devices face challenges when dealing with large circuits due to error rates as circuit size and the number of qubits increase. The circuit wire-cutting technique addresses this issue by breaking down a large circuit into smaller, more manageable subcircuits. However, the exponential increase in the number of subcircuits and the complexity of reconstruction as more cuts are made poses a great practical challenge. Inspired by ancilla-assisted quantum process tomography and the MUBs-based grouping technique for simultaneous measurement, we propose a new approach that can reduce subcircuit running overhead. The approach first uses ancillary qubits to transform all quantum input initializations into quantum output measurements. These output measurements are then organized into commuting groups for the purpose of simultaneous measurement, based on MUBs-based grouping. This approach significantly reduces the number of necessary subcircuits as well as the total number of shots. Lastly, we provide numerical experiments to demonstrate the complexity reduction., Comment: Accepted in IEEE International Conference on Quantum Computing and Engineering - QCE24

Recent progress in quantum machine learning has sparked interest in using quantum methods to tackle classical control problems via quantum reinforcement learning. However, the classical reinforcement learning environments often scale to high dimensional problem spaces, which represents a challenge for the limited and costly resources available for quantum agent implementations. We propose to solve this dimensionality challenge by a classical autoencoder and a quantum agent together, where a compressed representation of observations is jointly learned in a hybrid training loop. The latent representation of such an autoencoder will serve as a tailored observation space best suited for both the control problem and the QPU architecture, aligning with the agent's requirements. A series of numerical experiments are designed for a performance analysis of the latent-space learning method. Results are presented for different control problems and for both photonic (continuous-variable) and qubit-based agents, to show how the QNN learning process is improved by the joint training., Comment: 20 pages, 7 figures

This paper investigates photonic continuous-variable Born machines (CVBMs), which utilize photonic quantum states as resources for continuous probability distributions. Implementing exact gradient descent in the CVBM training process is often infeasible, bringing forward the need to approximate the gradients using an estimator obtained from a smaller number of samples, obtaining a quantum stochastic gradient descent (SGD) method. In this work, the ability to train CVBMs is analyzed using stochastic gradients obtained using relatively few samples from the probability distribution corresponding to homodyne measurement. The main obstacle to this analysis is that classically simulating CVBMs and obtaining samples is a demanding task, while a large number of iterations are needed to achieve convergence. The present research is enabled by a novel strategy to simulate homodyne detections of generic multimode photonic states using a classical computer. With this approach, a more comprehensive study of CVBMs is made possible, and the training of multimode CVBMs is demonstrated with parametric quantum circuits considerably larger than in previous articles. More specifically, we use the proposed algorithm to demonstrate learning of multimode quantum distributions using CVBMs. Moreover, successful CVBM trainings were demonstrated with the use of stochastic gradients., Comment: 7 pages, 8 figures

Two-dimensional (2D) semiconductors are likely to dominate next-generation electronics due to their advantages in compactness and low power consumption. However, challenges such as high contact resistance and inefficient doping hinder their applicability. Here, we investigate workfunction-mediated charge transfer (modulation doping) as a pathway for achieving high-performance p-type 2D transistors. Focusing on type-III band alignment, we explore the doping capabilities of 27 candidate materials, including transition metal oxides, oxyhalides, and {\alpha}-RuCl3, on channel materials such as transition metal dichalcogenides (TMDs) and group-III nitrides. Our extensive first-principles density functional theory (DFT) reveal p-type doping capabilities of high electron affinity materials, including {\alpha}-RuCl3, MoO3, and V2O5. We predict significant reductions in contact resistance and enhanced channel mobility through efficient hole transfer without introducing detrimental defects. We analyze transistor geometries and identify promising material combinations beyond the current focus on WSe2 doping, suggesting new avenues for hBN, AlN, GaN, and MoS2. This comprehensive investigation provides a roadmap for developing high-performance p-type monolayer transistors toward the realization of 2D electronics.

We show that any partial ascending HNN extension of a free group embeds in an actual ascending HNN extension of a free group. Moreover, we can ensure that it embeds as the parabolic subgroup of a relatively hyperbolic group., Comment: 16 pages, 1 figure

Let $M_k$ be a $2n$-vertex graph with $n$ pairwise disjoint edges and let $\mathcal{H}^{(p,s)}(n)$ be the family of subsets of $V(M_n)$ that span exactly $p$ edges and $s$ isolated vertices. We prove that for $n\ge 2p+s$ this family has the Erd\H{o}s--Ko--Rado property: the size of the largest intersecting family equals to the number of sets containing a fixed vertex. The bound $n\ge 2p+s$ is the best possible, improving a recent theorem with $n\ge 2p+2s$ by Fuentes and Kamat., Comment: 4 pages

Perovskite solar cells (PSCs) offer an efficient, inexpensive alternative to current photovoltaic technologies, with the potential for manufacture via high-throughput coating methods. However, challenges for commercial-scale solution-processing of metal-halide perovskites include the use of harmful solvents, the expense of maintaining controlled atmospheric conditions, and the inherent instabilities of PSCs under operation. Here, we address these challenges by introducing a high volatility, low toxicity, biorenewable solvent system to fabricate a range of 2D perovskites, which highly effective precursor phases for subsequent transformation to alpha-formamidinium lead triiodide (FAPbI3), fully processed under ambient conditions. PSCs utilising our FAPbI3 reproducibly show remarkable stability under illumination and elevated temperature (ISOS-L-2) and "damp heat" (ISOS-D-3) stressing, surpassing other state-of-the-art perovskite compositions. We determine that this enhancement is a consequence of the 2D precursor phase crystallisation route, which simultaneously avoids retention of residual low-volatility solvents (such as DMF and DMSO) and reduces the rate of degradation of FA+ in the material. Our findings highlight both the critical role of the initial crystallisation process in determining the operational stability of perovskite materials, and that neat FA+-based perovskites can be competitively stable despite the inherent metastability of the alpha-phase.

Driven by fundamental thermodynamic efficiency considerations, an emerging trend in the energy and propulsion systems is that the working fluid operates at a pressure above the critical pressure. Energy transport is thus accompanied by dramatic and strongly nonlinear variations of thermophysical properties, which cause abnormal heat transfer behavior and non-ideal gas effects. This situation raises a crucial challenge for the heat exchanger and turbomachinery design, overall energy and exergy efficiency. We aim to provide a multi-scale overview of the flow and thermal behavior of fluid above the critical point: microscopic physics and macroscopic transport. Microscopic physics, i.e. near-critical thermophysical properties, phase transition and fluid dynamics, are introduced. A particular focus will be on the supercritical pseudo boiling process, which is a generalization of classical liquid-vapor phase transitions to non-equilibrium supercritical states. These new perspectives lead to a revised view of the state space. Further, recent results demonstrated the possibility of stable supercritical fluid interfaces without surface tension. On the macroscale, recent progress on the modeling of turbulent flow and convective heat transfer of supercritical fluids are summarized. Direct numerical simulation is able to offer insights into the physics of thermal fluids. We start with a description of fundamental fluid mechanics problems related to supercritical fluids. In addition, the heat transfer deterioration in supercritical fluids is found to be closely connected to the flow relaminarization by the non-uniform body-force. Finally, various modeling approaches such as recently developed advanced Reynolds-averaged turbulence modeling as well as machine-learning methods are summarized., Comment: Requires further modifications

Deep Reinforcement Learning (DRL) has emerged as a promising approach to enhancing motion control and decision-making through a wide range of robotic applications. While prior research has demonstrated the efficacy of DRL algorithms in facilitating autonomous mapless navigation for aerial and terrestrial mobile robots, these methods often grapple with poor generalization when faced with unknown tasks and environments. This paper explores the impact of the Delayed Policy Updates (DPU) technique on fostering generalization to new situations, and bolstering the overall performance of agents. Our analysis of DPU in aerial and terrestrial mobile robots reveals that this technique significantly curtails the lack of generalization and accelerates the learning process for agents, enhancing their efficiency across diverse tasks and unknown scenarios., Comment: IEEE 20th International Conference on Automation Science and Engineering (CASE)

This paper presents a comprehensive study on the possible hybrid quantum-classical autoencoder architectures for end-to-end radio communication against noisy channel conditions using standard encoded radio signals. The hybrid scenarios include single-sided, i.e., quantum encoder (transmitter) or quantum decoder (receiver), as well as fully quantum channel autoencoder (transmitter-receiver) systems. We provide detailed formulas for each scenario and validate our model through an extensive set of simulations. Our results demonstrate model robustness and adaptability. Supporting experiments are conducted utilizing 4-QAM and 16-QAM schemes and we expect that the model is adaptable to more general encoding schemes. We explore model performance against both additive white Gaussian noise and Rayleigh fading models. Our findings highlight the importance of designing efficient quantum neural network architectures for meeting application performance constraints -- including data re-uploading methods, encoding schemes, and core layer structures. By offering a general framework, this work paves the way for further exploration and development of quantum machine learning applications in radio communication., Comment: 12 pages, 6 figures

Leveraging the intrinsic probabilistic nature of quantum systems, generative quantum machine learning (QML) offers the potential to outperform classical learning models. Current generative QML algorithms mostly rely on general-purpose models that, while being very expressive, face several training challenges. A potential way to address these setbacks involves constructing problem-informed models capable of more efficient training on structured problems. In particular, probabilistic graphical models provide a flexible framework for representing structure in generative learning problems and can thus be exploited to incorporate inductive bias in QML algorithms. In this work, we propose a problem-informed quantum circuit Born machine Ansatz for learning the joint probability distribution of random variables, with independence relations efficiently represented by a Markov network (MN). We further demonstrate the applicability of the MN framework in constructing generative learning benchmarks and compare our model's performance to previous designs, showing it outperforms problem-agnostic circuits. Based on a preliminary analysis of trainability, we narrow down the class of MNs to those exhibiting favorable trainability properties. Finally, we discuss the potential of our model to offer quantum advantage in the context of generative learning., Comment: 11+5 pages, 10 figures

Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what {\it big} means in the context of typical materials-science machine-learning problems. This concerns not only data volume, but also data quality and veracity as much as infrastructure issues. With selected examples, we ask (i) how models generalize to similar datasets, (ii) how high-quality datasets can be gathered from heterogenous sources, (iii) how the feature set and complexity of a model can affect expressivity, and (iv) what infrastructure requirements are needed to create larger datasets and train models on them. In sum, we find that big data present unique challenges along very different aspects that should serve to motivate further work.

The conventional definition of hypergraph has two major issues: (1) there is not a standard definition of directed hypergraph and (2) there is not a formal definition of nested hypergraph. To resolve these issues, we propose a new definition of hypergraph that unifies the concepts of undirected, directed and nested hypergraphs, and that is uniform in using hyperedge as a single construct for representing high-order correlations among things, i.e., nodes and hyperedges. Specifically, we define a hyperedge to be a simple hyperedge, a nesting hyperedge, or a directed hyperedge. With this new definition, a hypergraph is nested if it has nesting hyperedge(s), and is directed if it has directed hyperedge(s). Otherwise, a hypergraph is a simple hypergraph. The uniformity and power of this new definition, with visualization, should facilitate the use of hypergraph for representing (hierarchical) high-order correlations in general and chemical systems in particular. Graph has been widely used as a mathematical structure for machine learning on molecular structures and 3D molecular geometries. However, graph has a major limitation: it can represent only pairwise correlations between nodes. Hypergraph extends graph with high-order correlations among nodes. This extension is significant or essential for machine learning on chemical systems. For molecules, this is significant as it allows the direct, explicit representation of multicenter bonds and molecular substructures. For chemical reactions, this is essential since most chemical reactions involve multiple participants. We propose the use of chemical hypergraph, a multilevel hypergraph with simple, nesting and directed hyperedges, as a single mathematical structure for representing chemical systems. We apply the new definition of hypergraph to chemical hypergraph and, as simplified versions, molecular hypergraph and chemical reaction hypergraph., Comment: arXiv admin note: text overlap with arXiv:2310.03623 by other authors

Ultra-wide bandgap (UWBG) semiconductors are poised to transform power electronics by surpassing the capabilities of established wide bandgap materials, such as GaN and SiC, owing to their capability to operate at higher voltage, frequency, and temperature ranges. While bulk group-III nitrides and their alloys have been extensively studied in the UWBG realm, their two-dimensional counterparts remain unexplored. Here, we examine the stability and electronic properties of monolayers of ordered boron-based group-III nitride alloys with general formula BxM1-xN, where M = Al, Ga. On the basis of ab initio calculations we identify a number of energetically and dynamically stable structures. Instrumental to their stability is a previously overlooked out-of-plane displacement (puckering) of atoms, which induces a polar ordering and antiferroelectric ground state. Our findings reveal the energy barrier between metastable ferroelectric states is lowered by successive switching of out-of-plane displacements through an antiferroelectric state., Comment: 18 pages, 3 figures

We prove the set of growth-rates of subgroups of a rank~$r$ free group is dense in $[1,2r-1]$. Our main technical contribution is a concentration result for the leading eigenvalue of the non-backtracking matrix in the configuration model.

We introduce the Piquasso quantum programming framework, a full-stack open-source software platform for the simulation and programming of photonic quantum computers. Piquasso can be programmed via a high-level Python programming interface enabling users to perform efficient quantum computing with discrete and continuous variables. Via optional high-performance C++ backends, Piquasso provides state-of-the-art performance in the simulation of photonic quantum computers. The Piquasso framework is supported by an intuitive web-based graphical user interface where the users can design quantum circuits, run computations, and visualize the results., Comment: The source code is made available at https://github.com/Budapest-Quantum-Computing-Group/piquasso, and the documentation is published at https://piquasso.readthedocs.io/; November 2024 update: code examples adapted to Piquasso v5.0.0, presentation improved, minor errors corrected, references updated, new "Benchmarks" section added

BiTeI is a polar semiconductor with strong spin-orbit coupling (SOC) that produces large Rashba spin splitting. Due to its potential utility in spintronics and magnetoelectrics, it is essential to understand how defects impact the spin transport in this material. Using scanning tunneling microscopy and spectroscopy, we image ring-like charging states of single-atom defects on the iodine surface of BiTeI. We observe nanoscale variations in the Rashba energy around each defect, which we correlate with the local electric field extracted from the bias dependence of each ring radius. Our data demonstrate the local impact of atomic defects on the Rashba effect, which is both a challenge and an opportunity for the development of future nanoscale spintronic devices.

The switching and optical properties of phase-change thin films are actively investigated for future smart optical devices. The possibility of having more than one stable state, the large optical contrast between phases, and the fast and reversible switching are some attractive properties driving the research interest. Optical devices based on phase change alloys are considered the frontier contenders for tunable photonics. The combination of vivid structural color formation, with partial amorphization/crystallization of phase change alloys, and the associated optical tunability could be integrated into an energy-efficient reflective display device with high pixel density. This work demonstrates a contrast formation due to relative height differences from isolated pixelated structures. A reflective heterostructure device consisting of a low-loss Sb2Se3 alloy on a gold substrate was produced. With a focused ion beam, a pixelated metasurface structure was produced. Moreover, the ability to create local height differences using an ion beam was employed to create a structural color combination mimicking traditional LED like RGB pixels. We believe our approach in creating metapixels on phase change thin film surfaces could open up research interest in phase change alloys and moving away from semi/static plasmonic systems into truly dynamic display devices.

The non-linear and tunable optical constants of phase-change materials associated with their phase-switching have been utilized in reconfigurable optical devices. For example, one possible application of phase-change thin films is for tunable perfect absorption designs, where p- polarized light reflectance vanishes at a specific incidence angle known as the Brewster angle. This work demonstrates a generalized Brewster effect (s- and p- polarized light absorption) for a multilayered heterostructure design based on the strong interference effect. The proposed design comprises a low-loss phase-change material, Sb2Se3, coated on a gold substrate. We experimentally and theoretically show the coexistence of vanishing reflectance values for both s- and p- polarized lights in the visible and near IR wavelength range at a single Brewster angle. Such vanishing reflectance values (points of darkness) are associated with phase singularities with abrupt changes. Moreover, we show that additional phase singularities can be realized by switching the active Sb2Se3 layer between amorphous and crystalline structures, extending the functionality of our design. The realized phase singularities are susceptible to small optical constant changes and can be utilized for ultra-sensing applications. As a proof of concept, we demonstrate our design's CO2 gas sensing capabilities, showing a linearly dependent optical response with gas flow rate. We believe our lithography-free design with multiple phase singularities, from the coexisting generalized Brewster effect and from the additional structural switching, is highly promising for tunable optical sensing applications.