Artificial Quantum Research Articles

Contemporary approaches in matheuristics an updated survey

AbstractMatheuristics are problem independent frameworks that use mathematical programming tools to obtain high quality heuristic solutions. They are structurally general enough to be applied to different problems with little adaptation to their abstract structure, so they can be considered as new or hybrid metaheuristics based on components derived from the mathematical model of the problems of interest. In this survey, we emphasize the mathematical tools and describe how they can be used to design heuristics. We focus on mixed-integer linear programming and report representative examples from the literature of how it has been used for effective heuristic optimization. References to contributions to matheuristics deriving from neighboring research areas such as Artificial Intelligence or Quantum Computing are also included. We conclude with some ideas for possible future developments. This paper extends an original version published in 4OR with new sections on CMSA, Incremental Core, AI hybrids and Quantum Heuristics, and includes references to several recent publications.

Moiré-engineered light-matter interactions in MoS2/WSe2 heterobilayers at room temperature

Moiré superlattices in van der Waals heterostructures represent a highly tunable quantum system, attracting substantial interest in both many-body physics and device applications. However, the influence of the moiré potential on light-matter interactions at room temperature has remained largely unexplored. In our study, we demonstrate that the moiré potential in MoS2/WSe2 heterobilayers facilitates the localization of interlayer exciton (IX) at room temperature. By performing reflection contrast spectroscopy, we demonstrate the importance of atomic reconstruction in modifying intralayer excitons, supported by the atomic force microscopy experiment. When decreasing the twist angle, we observe that the IX lifetime becomes longer and light emission gets enhanced, indicating that non-radiative decay channels such as defects are suppressed by the moiré potential. Moreover, through the integration of moiré superlattices with silicon single-mode cavities, we find that the devices employing moiré-trapped IXs exhibit a significantly lower threshold, one order of magnitude smaller compared to the device utilizing delocalized IXs. These findings not only encourage the exploration of many-body physics in moiré superlattices at elevated temperatures but also pave the way for leveraging these artificial quantum materials in photonic and optoelectronic applications.

Hysteresis and self-oscillations in an artificial memristive quantum neuron

We theoretically study an artificial neuron circuit containing a quantum memristor in the presence of relaxation and dephasing. The charge transport in the quantum element is realized via tunneling of a charge through a quantum particle which shuttles between two terminals—a functionality reminiscent of classical diffusive memristors. We demonstrate that this physical principle enables hysteretic behavior in the current-voltage characteristics of the quantum device. In addition, being used in an artificial neural circuit, the quantum switcher is able to generate self-sustained current oscillations. Our analysis reveals that these self-oscillations are triggered only in quantum regimes with a moderate rate of relaxation, and cannot exist either in a purely coherent regime or at a very high decoherence. We investigate the hysteresis and instability leading to the onset of current self-oscillations and analyze their properties depending on the circuit parameters. Our results provide a generic approach to the use of quantum regimes for controlling hysteresis and generating self-oscillations. Published by the American Physical Society 2024

Legal implications for clinicians in cybersecurity incidents: A review.

Cybersecurity incidents in healthcare present significant legal implications for clinicians, necessitating careful consideration of technological advancements and regulatory frameworks. This literature examines the healthcare cybersecurity landscape, emphasizing clinicians' challenges, and legal responsibilities. It explores the impact of advanced technologies such as artificial intelligence and quantum computing, highlighting the potential benefits and risks, including biases and ethical dilemmas. The review addresses international regulatory differences, offering a comparative analysis of how various countries handle cybersecurity incidents. This analysis provides insights into best practices and identifies areas for improvement. Practical recommendations are provided, tailored to different healthcare settings, including large hospitals and small clinics, to enhance cybersecurity preparedness. Case studies illustrate real-world scenarios, offering practical guidance for clinicians in managing cybersecurity challenges. The review also identifies critical gaps in the literature, particularly concerning artificial intelligence ethics and international regulatory frameworks, suggesting specific areas for future research. These findings underscore the need for robust cybersecurity policies, comprehensive training for healthcare professionals, and a nuanced understanding of the legal landscape. This review informs policymakers, clinicians, and researchers about the evolving nature of cybersecurity challenges in healthcare, addressing key concerns raised by reviewers and contributing to a comprehensive understanding of the field.

Zero trust implementation in the emerging technologies era: a survey

This paper presents a comprehensive analysis of the shift from the traditional perimeter model of security to the Zero Trust (ZT) framework, emphasizing the key points in the transition and the practical application of ZT. It outlines the differences between ZT policies and legacy security policies, along with the significant events that have impacted the evolution of ZT. Additionally, the paper explores the potential impacts of emerging technologies, such as Artificial Intelligence and quantum computing, on the policy and implementation of ZT. The study thoroughly examines how Artificial Intelligence can enhance ZT by utilizing Machine Learning algorithms to analyze patterns, detect anomalies, and predict threats, thereby improving real-time decision-making processes. Furthermore, the paper demonstrates how a chaos theory-based approach, in conjunction with other technologies such as eXtended Detection and Response, can effectively mitigate cyberattacks. As quantum computing presents new challenges to ZT and cybersecurity as a whole, the paper delves into the intricacies of ZT migration, automation, and orchestration, addressing the complexities associated with these aspects. Finally, the paper provides a best practice approach for the seamless implementation of ZT in organizations, laying out the proposed guidelines to facilitate organizations in their transition towards a more secure ZT model. The study aims to support organizations in successfully implementing ZT and enhancing their cybersecurity measures.

RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison with CNN Transformer and Quantum Computing.

The aim and objective of the research are to develop an automated diagnosis system for the prediction of rheumatoid arthritis (RA) based on artificial intelligence (AI) and quantum computing for hand radiographs and thermal images. The hand radiographs and thermal images were segmented using a UNet++ model and color-based k-means clustering technique, respectively. The attributes from the segmented regions were generated using the Speeded-Up Robust Features (SURF) feature extractor and classification was performed using k-star and Hoeffding classifiers. For the ground truth and the predicted test image, the study utilizing UNet++ segmentation achieved a pixel-wise accuracy of 98.75%, an intersection over union (IoU) of 0.87, and a dice coefficient of 0.86, indicating a high level of similarity. The custom RA-X-ray thermal imaging (XTNet) surpassed all the models for the detection of RA with a classification accuracy of 90% and 93% for X-ray and thermal imaging modalities, respectively. Furthermore, the study employed quantum support vector machine (QSVM) as a quantum computing approach which yielded an accuracy of 93.75% and 87.5% for the detection of RA from hand X-ray and thermal images. In addition, vision transformer (ViT) was employed to classify RA which obtained an accuracy of 80% for hand X-rays and 90% for thermal images. Thus, depending on the performance measures, the RA-XTNet model can be used as an effective automated diagnostic method to diagnose RA accurately and rapidly in hand radiographs and thermal images.

Construction of topological quantum magnets from atomic spins on surfaces.

Artificial quantum systems have emerged as platforms to realize topological matter in a well-controlled manner. So far, experiments have mostly explored non-interacting topological states, and the realization of many-body topological phases in solid-state platforms with atomic resolution has remained challenging. Here we construct topological quantum Heisenberg spin lattices by assembling spin chains and two-dimensional spin arrays from spin-1/2 Ti atoms on an insulating MgO film in a scanning tunnelling microscope. We engineer both topological and trivial phases of the quantum spin model and thereby realize first- and second-order topological quantum magnets. We probe the many-body excitations of the quantum magnets by single-atom electron spin resonance with an energy resolution better than 100 neV. Making use of the atomically localized magnetic field of the scanning tunnelling microscope tip, we visualize various many-body topological bound modes including topological edge states, topological defects and higher-order corner modes. Our results provide a bottom-up approach for the simulation of exotic quantum many-body phases of interacting spins.

Emerging Trends in Financial Security Research: Innovations, Challenges, and Future Directions

In the evolving landscape of financial security, the rapid advancement of technology and the increasing sophistication of cyber threats necessitate an examination of emerging trends, innovations, and challenges. This paper explores the latest developments in financial security, focusing on technological innovations such as artificial intelligence, blockchain, and quantum computing, which are shaping the future of the field. It discusses key challenges including the evolving cyber threat landscape, regulatory and compliance issues, and ethical considerations. Additionally, the paper identifies innovative solutions such as next-generation authentication methods, Zero Trust architecture, and advanced encryption techniques, while highlighting future research directions and policy implications. By providing a comprehensive review of current trends and future possibilities, this research aims to offer insights into how financial institutions can adapt to an increasingly complex security environment.

Advancing Economic Recovery with Artificial Intelligence, Quantum Computing Technologies, and Strategic Management in Small Businesses

This abstract introduces a comprehensive approach to fostering economic recovery through the integration of cutting-edge technologies and strategic management in small businesses. In the wake of global economic challenges, the utilization of Artificial Intelligence (AI) and Quantum Computing (QC) emerges as a pivotal strategy. AI offers unparalleled capabilities in data analysis, customer engagement, and operational efficiency, empowering small businesses to adapt swiftly to market demands and optimize resource allocation. Concurrently, QC revolutionizes data processing by exponentially enhancing computational power, enabling small enterprises to tackle complex problems and innovate at unprecedented speeds. However, the effective deployment of these technologies necessitates astute strategic management. Strategic foresight and agile decision-making are crucial in harnessing AI and QC to their fullest potential, ensuring alignment with business objectives and mitigating risks. This abstract advocates for a holistic approach wherein small businesses integrate AI and QC technologies seamlessly into their operational frameworks while fostering a culture of strategic innovation. By leveraging these advancements in tandem with strategic management principles, small enterprises can not only navigate economic uncertainties but also unlock new growth opportunities, catalyzing sustainable economic recovery and long-term prosperity.

Integration of Artificial Intelligence and Quantum Transport toward Stereoselective Identification of Carbohydrate Isomers.

Detection of stereoisomers of carbohydrates with molecular resolution, a challenging goal analysts desire to achieve, is key to the full development of glycosciences. Despite the promise that analytical techniques made, including widely used nuclear magnetic resonance and mass spectrometry, high throughput de novo carbohydrate sequencing remains an unsolved issue. Notably, while next-generation sequencing technologies are readily available for DNA and proteins, they are conspicuously absent for carbohydrates due to the immense stereochemical and structural complexity inherent in these molecules. In this work, we report a novel computational technique that employs quantum tunneling coupled with artificial intelligence to detect complex carbohydrate anomers and stereoisomers with excellent sensitivity. The quantum tunneling footprints of carbohydrate isomers show high distinguishability with an in-depth analysis of underlying chemistry. Our findings open up a new route for carbohydrate sensing, which can be seamlessly integrated with next-generation sequencing technology for real-time analysis.

Enhancing the expressivity of quantum neural networks with residual connections

In noisy intermediate-scale quantum era, the research on the combination of artificial intelligence and quantum computing has been greatly developed. Here we propose a quantum circuit-based algorithm to implement quantum residual neural networks, where the residual connection channels are constructed by introducing auxiliary qubits to data-encoding and trainable blocks in quantum neural networks. We prove that when this particular network architecture is applied to a l-layer data-encoding, the number of frequency generation forms extends from one, namely the difference of the sum of generator eigenvalues, to O(l2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{{{{{\\mathcal{O}}}}}}}}({l}^{2})$$\\end{document}, and the flexibility in adjusting Fourier coefficients can also be improved. It indicates that residual encoding can achieve better spectral richness and enhance the expressivity of various parameterized quantum circuits. Extensive numerical demonstrations in regression tasks and image classification are offered. Our work lays foundation for the complete quantum implementation of classical residual neural networks and offers a quantum feature map strategy for quantum machine learning.

Unconventional superconductivity in chiral molecule-TaS2 hybrid superlattices.

Chiral superconductors, a unique class of unconventional superconductors in which the complex superconducting order parameter winds clockwise or anticlockwise in the momentum space1, represent a topologically non-trivial system with intrinsic time-reversal symmetry breaking (TRSB) and direct implications for topological quantum computing2,3. Intrinsic chiral superconductors are extremely rare, with only a few arguable examples, including UTe2, UPt3 and Sr2RuO4 (refs. 4-7). It has been suggested that chiral superconductivity may exist in non-centrosymmetric superconductors8,9, although such non-centrosymmetry is uncommon in typical solid-state superconductors. Alternatively, chiral molecules with neither mirror nor inversion symmetry have been widely investigated. We suggest that an incorporation of chiral molecules into conventional superconductor lattices could introduce non-centrosymmetry and help realize chiral superconductivity10. Here we explore unconventional superconductivity in chiral molecule intercalated TaS2 hybrid superlattices. Our studies reveal an exceptionally large in-plane upper critical field Bc2,|| well beyond the Pauli paramagnetic limit, a robust π-phase shift in Little-Parks measurements and a field-free superconducting diode effect (SDE). These experimental signatures of unconventional superconductivity suggest that the intriguing interplay between crystalline atomic layers and the self-assembled chiral molecular layers may lead to exotic topological materials. Our study highlights that the hybrid superlattices could lay a versatile path to artificial quantum materials by combining a vast library of layered crystals of rich physical properties with the nearly infinite variations of molecules of designable structural motifs and functional groups11.

Increasing the lifetime of confined electronic states in an artificial quantum structure

Increasing the lifetime of confined electronic states in an artificial quantum structure

The Most Important Types of Cyber Attacks that France is Expected to Face in the Future and the Cyber Security Measures it Must Implement to Protect Critical Infrastructure, Telecommunication Networks and Personal Data

As the digital landscape continues to evolve, nations like France find themselves on the frontline of an escalating cyber warfare scenario. This abstract provides an insightful overview of the most significant types of cyber attacks that France is expected to face in the future, offering a comprehensive analysis based on current trends and emerging technologies. The research delves into the realm of advanced persistent threats (APTs), examining the sophisticated techniques employed by state-sponsored actors and cybercriminal organizations to compromise critical infrastructure, sensitive data, and national security. It explores the evolving landscape of ransomware attacks, focusing on the potential impact on both public and private sectors, and the looming threat of extortion campaigns that could cripple essential services. Furthermore, the abstract sheds light on the rising concern of supply chain attacks, as interconnected global networks make businesses and government entities vulnerable to malicious actors seeking to exploit weaknesses in third-party systems. The research also explores the nuances of social engineering attacks, recognizing the human element as a critical vulnerability that threat actors may exploit through targeted phishing campaigns and manipulation tactics. In addition, the abstract touches upon the growing menace of Internet of Things (IoT) vulnerabilities, acknowledging the increased interconnectivity of devices and the potential for large-scale disruptions. It also considers the implications of emerging technologies such as artificial intelligence and quantum computing in shaping the future threat landscape. By examining these diverse aspects of cyber threats, this abstract aims to provide a forward-looking perspective on the challenges France is likely to encounter in the digital realm. As Cyber security becomes an integral component of national defense, understanding these anticipated threats is crucial for policymakers, security experts, and technology professionals seeking to fortify France’s resilience against the evolving nature of cyber attacks.

How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.

The Curriculum in NDT Training Have to Include Basics Information Technology to Accomplish Reliability of Automated NDT Result's

Automation in Non-Destructive Testing (NDT) is a reality of modern manufacturing and quality assurance. Information technology applications are simultaneously developed and applied to different fields of NDT methods. The future of doing reliability checks using automated system is inevitable. The results are important to make a variety of decisions regarding the quality of a product and/or service, hence affecting safety and reliability. Depending on artificial intelligence and quantum device development, various NDT training schemes have to adopt a new curriculum to address the issue of the basic knowledge of information technology and automation in NDT. NDT operators are basically dependent on the end results of software, in which the software developer uses a set of algorithms. These are obtained in three categories; 1) Real time database; 2) Synthetic database and 3) Hybrid or combination of them. The developers should know how the NDT systems run and NDT operators in the other hand know the mechanism of the program, so that appropriate action can be taken, in case there is a missed detection, or a detected indication is not very much detrimental to the reliability of the part (Over sizing or wrongly evaluated). For example, a set of algorithms are used to create the focal laws in a phased array ultrasonic test software in space of 1˚ beam steering. Each has a limit of maximum up to 2nd Critical angle. Operators try to copy the same algorithm and set up to 89˚ refraction angle, which will just create creep wave with unreliable results to detect effective size the reflector. Programming knowledge of the operator can effectively eliminate this type of unreliability. In various Schemes of NDT Syllabus which are in common place to employ and train plus certify operators does not have it; actually, the operators do the scope of work which does includes application of Artificial Intelligence (AI) and Software. With the development of Q coupler (Quantum Computing) system, it is obvious to adopt the upcoming technology into the application of NDT industry. Training needs to update the curriculum accordingly. The current topic has intended to focus on the training gap and to address related kind of unreliability.

Full quantum tomography study of Google’s Sycamore gate on IBM’s quantum computers

The potential of achieving computational hardware with quantum advantage depends heavily on the quality of quantum gate operations. However, the presence of imperfect two-qubit gates poses a significant challenge and acts as a major obstacle in developing scalable quantum information processors. Google’s Quantum AI and collaborators claimed to have conducted a supremacy regime experiment. In this experiment, a new two-qubit universal gate called the Sycamore gate is constructed and employed to generate random quantum circuits (RQCs), using a programmable quantum processor with 53 qubits. These computations were carried out in a computational state space of size 9×1015\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$9 \ imes 10^{15}$\\end{document}. Nevertheless, even in strictly-controlled laboratory settings, quantum information on quantum processors is susceptible to various disturbances, including undesired interaction with the surroundings and imperfections in the quantum state. To address this issue, we conduct both quantum state tomography (QST) and quantum process tomography (QPT) experiments on Google’s Sycamore gate using different artificial architectural superconducting quantum computer. Furthermore, to demonstrate how errors affect gate fidelity at the level of quantum circuits, we design and conduct full QST experiments for the five-qubit eight-cycle circuit, which was introduced as an example of the programability of Google’s Sycamore quantum processor. These quantum tomography experiments are conducted in three distinct environments: noise-free, noisy simulation, and on IBM Quantum’s genuine quantum computer. Our results offer valuable insights into the performance of IBM Quantum’s hardware and the robustness of Sycamore gates within this experimental setup. These findings contribute to our understanding of quantum hardware performance and provide valuable information for optimizing quantum algorithms for practical applications.

Converging Artificial Intelligence and Quantum Technologies: Accelerated Growth Effects in Technological Evolution

One of the fundamental problems in the field of technological studies is to clarify the drivers and dynamics of technological evolution for sustaining industrial and economic change. This study confronts the problem by analyzing the converging technologies to explain effects on the evolutionary dynamics over time. This paper focuses on technological interaction between artificial intelligence and quantum technologies using a technometric model of technological evolution based on scientific and technological information (publications and patents). Findings show that quantum technology has a growth rate of 1.07, artificial intelligence technology has a rate of growth of 1.37, whereas the technological interaction of converging quantum and artificial intelligence technologies has an accelerated rate of growth of 1.58, higher than trends of these technologies taken individually. These findings suggest that technological interaction is one of the fundamental determinants in the rapid evolution of path-breaking technologies and disruptive innovations. The deductive implications of results about the effects of converging technologies are: (a) accelerated evolutionary growth; (b) a disproportionate (allometric) growth of patents driven by publications supporting a fast technological evolution. Our results support policy and managerial implications for the decision making of policymakers, technology analysts, and R&D managers that can direct R&D investments towards fruitful inter-relationships between radical technologies to foster scientific and technological change with positive societal and economic impcats.

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

Engineering holography with stabilizer graph codes

The discovery of holographic codes established a surprising connection between quantum error correction and the anti-de Sitter-conformal field theory correspondence. Recent technological progress in artificial quantum systems renders the experimental realization of such holographic codes now within reach. Formulating the hyperbolic pentagon code in terms of a stabilizer graph code, we give gate sequences that are tailored to systems with long-range interactions. We show how to obtain encoding and decoding circuits for the hyperbolic pentagon code, before focusing on a small instance of the holographic code on twelve qubits. Our approach allows to verify holographic properties by partial decoding operations, recovering bulk degrees of freedom from their nearby boundary.