Reviewerly/exHarmony huggingface dataset:
Achieving Scalable Quantum Error Correction with Union-Find on Systolic Arrays by Using Multi-Context Processing Elements

Given the currently available noisy Quantum Hardware, a reliable and fast Quantum Error Correction is required to build functional Quantum Computers. Based on the Surface Code-arrangement of Qubits, several different error decoders have been proposed to solve the underlying problem of Minimum Weight Perfect Matching, such as Union-Find on Systolic-Array-like distributed computing architectures. Since these published approaches have a relatively large hardware footprint, they show a lack of scalability for the required large code distances in the Surface Code. We therefore propose an implementation of Union-Find on a multi-context Systolic Array that allows for efficient and latency-optimized Quantum Error Correction of large Surface Codes with currently available hardware through time-multiplexing.
Embedding All Feasible Solutions of Traveling Salesman Problem by Divide-and-Conquer Quantum Search

Quantum search algorithms, such as the Grover algorithm, are investigated as one of the ways to solve a traveling salesman problem (TSP). In TSP, the quantum search starts from an equal superposition of all feasible solutions. The embedding cost of all feasible solutions is up to exponential, i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(n!)$</tex> with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> cities, and makes it difficult to solve large-scale TSP. Hence, preparing the initial state of TSP at a lower embedding cost is an important issue. Here, we propose a quantum search algorithm based on the divide-and-conquer approach, which divides each constraint term of the TSP into subproblems and applies the quantum search to each subproblem. We investigate the embedding quality of all feasible solutions by comparing our proposed method with the Grover algorithm regarding optimal iterations and success probability. The results show that our quantum divide-and-conquer approach can significantly reduce the embedding cost while the success probability decreases.
A Substrate Scheduler for Compiling Arbitrary Fault-Tolerant Graph States

Graph states are useful computational resources in quantum computing, particularly in measurement-based quantum computing models. However, compiling arbitrary graph states into executable form for fault-tolerant surface code execution and accurately estimating the compilation cost and the run-time resource cost remains an open problem. We introduce the Substrate Scheduler, a compiler module designed for fault-tolerant graph state compilation. The Substrate Scheduler aims to minimize the space-time volume cost of generating graph states. We show that Substrate Scheduler can efficiently compile graph states with thousands of vertices for “A Game of Surface Codes”-style patch-based surface code systems. The results show that our module generates graph states with the lowest execution time complexity to date, achieving graph state generation time complexity that is at or below linear in the number of vertices and demonstrating specific types of graphs to have constant generation time complexity. Moreover, it provides a solid foundation for developing compilers that can handle a larger number of vertices, up to the millions or billions needed to accommodate a wide range of post-classical quantum computing applications.
Hybrid Variational Quantum Circuit with Transformer for Classifying Medical Conditions

Reviews in the form of text available on different web-based platform, can provide very important insights analyzing the consumers behavior and their wants and needs. Such data can be used to improve the quality or assess the effectiveness of pharmaceutical product. This effort aims to assess the medical conditions of a patient by analyzing the text reviews of pharmaceutical products, and thereby increase the patient's healthcare quality. Recently new computing paradigms like quantum machine learning gained a lot of attention. However, it is not yet feasible to perform quantum operations on high dimensional data using current quantum hardware. Hence a hybrid approach using both classical and quantum machine learning methods has been proposed. We compared our results with the performance of the Bidirectional Encoder Representations from Transformer (BERT) model. The result shows the promise of quantum computing technology when used in combination with a classical machine learning approach. In comparison to the current state-of-the-art technology, where the transformer-based techniques achieved 85.7% of accuracy, our hybrid approach achieved 87.1% accuracy.
Estimating Fidelity Lower Bounds of Quantum Subroutines

We define scalable benchmarks for two important quantum subroutines, namely the quantum Fourier transform (QFT) and the multi-controlled-Z gate. Our benchmarks are based on applying the subroutines to a set of states that are sufficient for lower-bounding the process fidelity from the set of measured output state fidelities. By randomly sampling from the set of input states, the lower bound <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</sup> the process fidelity can be estimated. We implement the QFT and multi-controlled-Z circuits with up to 20 and 12 qubits, respectively, on the Quantinuum H1-1 quantum computer. Our circuits make use of parameterized-angle entangling gates, which for these circuits reduces the number of two-qubit gates significantly.
Towards Redefining the Reproducibility in Quantum Computing: A Data Analysis Approach on NISQ Devices

Although the building of quantum computers has kept making rapid progress in recent years, noise is still the main challenge for any application to leverage the power of quantum computing. Existing works addressing noise in quantum devices proposed noise reduction when deploying a quantum algorithm to a specified quantum computer. The reproducibility issue of quantum algorithms has been raised since the noise levels vary on different quantum computers. Importantly, existing works largely ignore the fact that the noise of quantum devices varies as time goes by. Therefore, reproducing the results on the same hardware will even become a problem. We analyze the reproducibility of quantum machine learning (QML) algorithms based on daily model training and execution data collection. Our analysis shows a correlation between our QML models' test accuracy and quantum computer hardware's calibration features. We also demonstrate that noisy simulators for quantum computers are not a reliable tool for quantum machine learning applications.
Exact and approximate simulation of large quantum circuits on a single GPU

We benchmark the performances of Qrack, an open-source software library for the high-performance classical simulation of (gate-model) quantum computers. Qrack simulates, in the Schrödinger picture, the exact quantum state of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> qubits evolving under the application of a circuit composed of elementary quantum gates. Moreover, Qrack can also run approximate simulations in which a tunable reduction of the quantum state fidelity is traded for a significant reduction of the execution time and memory footprint. In this work, we give an overview of both simulation methods (exact and approximate), highlighting the main physics-based and software-based techniques. Moreover, we run computationally heavy benchmarks on a single GPU, executing large quantum Fourier transform circuits and large random circuits. Compared with other classical simulators, we report competitive execution times for the exact simulation of Fourier transform circuits with up to 27 qubits. We also demonstrate the approximate simulation of all amplitudes of random circuits acting on 54 qubits with 7 layers at average fidelity higher than 4%, a task commonly considered hard without super-computing resources.
Quantum Relaxation Based Branch-and-Bound Algorithm

Mathematical optimization is a field where the potential of quantum algorithms is actively being explored. The Quantum Approximate Optimization Algorithm (QAOA) serves as a notable quantum-classical hybrid approach aimed at solving combinatorial optimization challenges on Noisy Intermediate-Scale Quantum (NISQ) devices. In QAOA, classical variables find representation in qubits via a one-to-one mapping. A newer entrant, Quantum Random Access Optimization (QRAO), encodes problems using fewer qubits than QAOA by leveraging the Quantum Random Access Code (QRAC) to structure a problem into its Hamiltonian form. Notably, the QRAO Hamiltonian, dubbed the Quantum Relaxation Hamiltonian, boasts of a ground state energy inferior to that of the QAOA Hamiltonian. Nonetheless, the QRAO's ground state exhibits entanglement, making it starkly different from QAOA's ground state. As a workaround, preceding studies formulated solutions to the original problem using a rounding algorithm. Our research introduces the Quantum Relaxation based Branch-and-Bound (QR-BnB) algorithm. This novel method marries Quantum Relaxation and the classical exact solution strategy, Branch-and-Bound. Within the QR-BnB framework, a classical device shoulders the responsibility of the branch-and-bound operation, while its quantum counterpart dedicates itself to determining each ground state energy for the subproblem's Quantum Relaxation Hamiltonian. This fusion eliminates the prerequisite of the rounding algorithm by crafting the optimal solution through branching operations. Preliminary simulations of the QR-BnB approach attest to its capability in accurately solving a MaxCut problem. As future work, exploring its applicability on diverse combinatorial optimization problems holds promise. Our algorithm signals a step forward in harnessing NISQ devices for tackling expansive optimization challenges.
Hypergraphic Partitioning of Quantum Circuits for Distributed Quantum Computing

When dealing with NISQ computers (Noise Intermediate Scale Quantum), it is widely accepted that distributing circuits among processing agents is an effective way to increase scalability with small machines. This approach can improve overall computational power, but it requires using qubits for communication between partitions, dedicated for the teleportation protocol over time. The main challenge is to minimize this communication cost by using efficient circuit partitioning strategies. There are many different partitioning approaches in the literature, but a consensus is needed on the best method for distribution scenarios. Here, we explore the partitioning process of quantum circuits using a hypergraphic representation for the circuit, to run this task. First, explore and compare different approaches of hypergraphic partitioning. Then, we created a variation of the Fiduccia-Mattheyses heuristic to minimize the communication qubits between partitions. Thanks to our tunning and an applied approach, we reduced the cost of partitioning circuits of 10% to 50% with respect to the different algorithms from state of art. Our method involves three steps: logical segmentation and grouping of gates; translation of the circuit into a hypergraphic representation; and use of the Fiduccia-Mattheyses heuristic variation for hypergraphic partitioning. This research explores quantum circuits partitioning for different scenarios as multi-QPU and distributed machine over classical communication, consolidating important results for quantum development in distributed scenarios, for a set of benchmark algorithms.
Resource Management in Quantum Virtual Private Networks

In this study, we develop a resource management framework for a quantum virtual private network (qVPN), which involves the sharing of an underlying public quantum network by multiple organizations for quantum entanglement distribution. Our approach involves resolving the issue of link entanglement resource allocation in a qVPN by utilizing a centralized optimization framework. We provide insights into the potential of genetic and learning-based algorithms for optimizing qVPNs, and emphasize the significance of path selection and distillation in enabling efficient and reliable quantum communication in multi-organizational settings. Our findings demonstrate that compared to traditional greedy based heuristics, genetic and learning-based algorithms can identify better paths. Furthermore, these algorithms can effectively identify good distillation strategies to mitigate potential noises in gates and quantum channels, while ensuring the necessary quality of service for end users.
Survey of Face Recognition Using Eigenfaces

Facial recognition algorithms are increasingly becoming popular in recent times. This is due to its multiple functions across diverse disciplines. As a result of its importance, most especially for authentication and verification purposes. There is a need for updated research that will provide a comprehensive summary of facial recognition systems using the eigenfaces algorithm. As a result, this research provided the latest development and overview of facial recognition systems using eigenfaces in the last five years, 2018-2022. A database search was performed to include the most recent articles. The articles were reviewed for the mathematical background, techniques and methods, and experimental results. The review of the articles shows that eigenfaces are one of such algorithms that have been explored in solving the problem of facial detection and identification over the years. The accuracy and recognition rate of the algorithm proved to be one of the significances of eigenfaces.
Heterogeneous Multi-UAV Mission Planning Considering Obstacle Avoidance and UAV Motion Model

Distributed task assignment algorithm is the key algorithm to solve multi-agent task assignment problem. In this paper, heterogeneous UAVs cooperative search and combat task is taken as the background. First, a multi-UAV task assignment model is established by comprehensively considering the constraints of UAV load resources, task time window and task type. Aiming at the obstacle threat in the task scenario, the UAV motion model is considered to improve the mobility and obstacle avoidance ability of the UAV in the assignment model. Second, UAV maneuver trajectory and path in the Consensus-Based Bundle Algorithm (CBBA) are improved by integrating dynamic window algorithm (DWA). Finally, the proposed algorithm is verfied which can improve the ability of multi-UAV task assignment in complex environment through numerical simulation.
Self Evaluation Using Zero-shot Learning

With recent advances in large language models (LLM) and visual language models (VLM), there has been great interest in extending these models to robotics and task execution. Recent studies have bridged the gap between large language models and robotic language processing by providing a real-world grounding to help the robot better understand and execute tasks. A major drawback of existing approaches is that they still require a human to monitor and evaluate the robot’s performance. As robots expand into a wide range of industries from agriculture to healthcare, it is important that they can self-monitor their performance and identify when any mistake occurs. We propose a model that combines large-language models and visual language models to self-evaluate task execution based on video data. We utilize LLMs to generate outcome descriptions for each step and then provide these as input prompts to a VLM to determine whether a step has been completed. Based on video data, our solution identifies if each step in a task has been executed correctly and notices when a mistake has been made. We design a score metric that assesses the status of completion of each step in the task. Upon testing the model on a variety of household-related tasks, it demonstrated successful evaluation of task completion. Our model could be implemented in robots to self-evaluate task execution, leading to more accurate and efficient robotic systems.
Enhanced Attention Mechanism based Image Watermarking with Simulated JPEG Compression

Deep learning-based image watermarking algorithms have been widely studied as an important technology for copyright protection. These methods utilize an end-to-end architecture with an encoder, a noise layer and a decoder to make the watermark robust to various distortions. However, recent algorithms present unsatisfactory visual quality and robustness against JPEG compression, which is the most common image processing operation but is non-differential thus cannot be directly included in the noise layer. To address this limitation, this study proposes a novel enhanced attention-based image watermarking algorithm with simulated JPEG compression, which leverages the channel and spatial attention mechanism to facilitate watermark embedding and simulates JPEG compression with a suitably designed function. Precisely, we design a differentiable rounding function based on the Fourier series to replace the quantization process in JPEG compression, which overcomes the non-differentiability of JPEG compression and can be incorporated in the training process. In addition, we propose an enhanced dual attention module in the encoder, which combines channel and spatial attention to improve the performance of our model. The channel attention guides the encoder to fuse the watermark into more important channels and the spatial attention further helps to embed the watermark into regions with more complex textures. The experimental results show that our method generates high quality watermarked images, with PSNR over 50 when no noise is applied. Compared with current methods, our model achieves stronger robustness to JPEG compression, with bit accuracy over 99% under the JPEG compression with quality factor of 50. Besides, the proposed framework also exhibits excellent robustness for a variety of common distortions, including cropout and dropout.
An Integrated Model for Automated Identification and Learning of Conversational Gestures in Human–Robot Interaction

Conversational gestures are an important part of communication and interaction to augment intention, emphasis, and emotion in speech, signal attention, express mental states, describe entities and actions in dialogs, and provide object localization. Cognitive psychologists have proposed many classifications of human gestures, based upon discourse analysis, head-motion, and hand-motion and shapes. Many machine and deep learning techniques have been used to detect limited head or hand-based gestures during multi-agent interactions using a combination of image and motion analysis. However, research on a general purpose integrated model to detect, train and automated learning and adaptation of conversational gestures by social robots is limited. Current models do not consider spatiotemporal synchronization and integration of dialog and motion analysis. This book chapter reviews major psycholinguistic subclasses of conversational gestures, and the application of machine learning and deep learning techniques for conversational gesture recognition. It describes a Synchronous Colored Petri Net (SCPN) model for automated training of social robots to detect and learn major subclasses of conversational gestures based upon synchronization of organ-motions and speech. The model automatically learns and adapts to conversational gestures by observation, mimicking and statistical analysis. The model integrates vision and machine learning techniques on real-time videos to extract spatiotemporal relationships between organ-movements and spoken phrases. Using synchronization reduces perceptual distortion in gesture detection and labeling. The approach also integrates contour analysis of hand-motion with conceptual dependency model for spoken phrases to identify iconic gestures. The model also applies decision trees for ambiguity resolution during gesture labeling.
Ontology-Based Similarity Estimates for Fuzzy Data: Semantic Wiki Approach

We analyze main types of dirty data processed by computing intelligence, criteria of their classification and means of their detection. Results of this analysis are represented by ontological model that contains taxonomy of classical and non-classical data and knowledge-oriented methods of their transformation. Special attention is paid to semantically incorrect data that corresponds to vague knowledge. Such data cannot be reconciled with rules and restriction imported from external knowledge sources such as domain ontologies. Incorrectness of such data can be caused by irrelevant attribute, incorrect type of value, inadmissible relation between individuals of classes, etc. We propose to use various estimations of semantic similarity between domain concepts to detect these types of data incorrectness and transform it into classical data. Sources and types of fuzzy data are considered on example of representation possibilities of the Wiki technological environment and its semantic extension Semantic MediaWiki. Wiki resources are interesting from the point of view of the fuzzy data processing by the heterogeneous information objects, knowledge schemas and subjects of data entering. We analyze types of dirty data are detected automatically or semi-automatically in this technology and define what external means of knowledge management can be used for processing of these data.
Traffic Sign Recognition Robustness in Autonomous Vehicles Under Physical Adversarial Attacks

Nowadays we are all witnesses of the technological development of the so-called 4th industrial revolution (Industry 4.0). In this context, a daily living environment of smart cities is formed in which artificial intelligence applications play a dominant role. Autonomous (pilotless) vehicles are a shining example of the application of artificial intelligence, based on which vehicles are allowed to move autonomously in both residential and rural areas. The proposed article examines the robustness, in adversarial attacks in the physical layer, of the deep learning models used in autonomous vehicles for the recognition of road traffic signals. As a case study the roads of Greece, having traffic signs highly contaminated not on purpose, is considered. Towards investigating this direction, a novel dataset with clear and attacked images of traffic signs is proposed and used in the evaluation of popular deep learning models. This study investigates the level of readiness of autonomous vehicles to perform in noisy environments that affect their ability to recognize road signs. This work highlights the need for more robust deep learning models in order to make the use of autonomous vehicles a reality with maximum safety for citizens.