Quantum Computation Research Articles

Rozwój technologii kwantowej: wyzwania i aspekty regulacyjne. Przegląd wybranych zagadnień prawnych

In the era of the Fourth Industrial Revolution, quantum technology is entering the scene with the promise of radically changing the technological paradigm. Quantum computing, simulation, quantum communications and the combination of this technology with artificial intelligence are opening up new horizons of innovation that could revolutionize various industries. Along with its enormous potential, this technology brings legal, ethical and social challenges that require detailed analysis and an interdisciplinary approach. The industrial deployment of quantum technology entails dilemmas regarding human rights, cyber security and national security, as well as the risk of exacerbating inequality, technological exclusion and algorithmic discrimination. With regard to artificial intelligence enhanced by quantum computing technology, we can speak of a completely new facet of the problem of the opacity of algorithms, which is determined not only by the cognitive limitations of the human mind, but stems from the indefinability of the world described by the laws of quantum mechanics. In light of the above, the thesis can be advanced that the classical approach to the principle of transparency and the postulates of creating algorithms that will clearly present the path to the final result, which are part of the broad current of creating ethical and explainable artificial intelligence, may prove difficult to realize in relation to Quantum AI. In the era of the Fourth Industrial Revolution, we are therefore faced with the challenge of implementing new instruments for testing, certifying and inspecting algorithms, such as tools for analyzing and visualizing the results of quantum algorithms, which will be suited to the specifics of Quantum AI and ensure the ethical correctness of the systems. This article will identify selected areas of application of quantum technology, and thus the potential benefits and risks of quantum technology, and also analyze the need to develop ethical and legal standards that allow for the sustainable and socially responsible development of these disruptive technologies.

Advances in flight control systems for modern commercial aircraft

Abstract. In consequence of the accelerated evolution of aviation technology, the flight control systems of commercial aircraft have undergone substantial technological advancement and system optimization. This paper presents a comprehensive analysis of the recent advancements and future trends in flight control systems for commercial aircraft. The fundamental concepts and historical evolution of flight control systems are delineated, with a particular emphasis on the evolution of autopilot systems and the technological advancements of flight control systems (FCS). Furthermore, it examines innovations in flight control algorithms, including adaptive control and intelligent algorithms, and investigates the impact of fault-tolerant design on flight safety. In terms of system integration, the integration of flight control systems with navigation and communication systems, as well as performance enhancements resulting from system optimization, is explored. In addition, it examines the potential impact of intelligence and networking on future flight control systems, with a particular focus on the prospects for the application of artificial intelligence, quantum computing, and new material technologies. The synthesis of these areas provides a systematic understanding of the technological evolution and future development of flight control systems for commercial aircraft.

Quantum mechanics and statistical physics: Novel frameworks for enhancing natural language processing

Abstract. This article explores the pioneering application of principles from quantum mechanics and statistical mechanics to the field of natural language processing (NLP). By drawing analogies between physical phenomena such as quantum entanglement, phase transitions, and statistical ensembles, and linguistic concepts like semantic relationships, language use dynamics, and lexical diversity, we offer a novel perspective on language analysis and processing. Quantum linguistic models, leveraging the intricacies of entanglement and quantum probability, provide a framework for understanding complex semantic networks and enhancing computational efficiency through quantum computing. Meanwhile, statistical mechanics inspires models for capturing lexical diversity and understanding the evolution of language patterns, akin to phase transitions in physical systems. This interdisciplinary approach not only deepens our understanding of linguistic phenomena but also introduces advanced mathematical and computational techniques to improve NLP tasks.

Group frames via magic states with applications to SIC-POVMs and MUBs

We connect magic (non-stabilizer) states, symmetric informationally complete positive operator valued measures (SIC-POVMs), and mutually unbiased bases (MUBs) in the context of group frames, and study their interplay. Magic states are quantum resources in the stabilizer formalism of quantum computation. SIC-POVMs and MUBs are fundamental structures in quantum information theory with many applications in quantum foundations, quantum state tomography, and quantum cryptography, etc. In this work, we study group frames constructed from some prominent magic states, and further investigate their applications. Our method exploits the orbit of discrete Heisenberg–Weyl group acting on an initial fiducial state. We quantify the distance of the group frames from SIC-POVMs and MUBs, respectively. As a simple corollary, we reproduce a complete family of MUBs of any prime dimensional system by introducing the concept of MUB fiducial states, analogous to the well-known SIC-POVM fiducial states. We present an intuitive and direct construction of MUB fiducial states via quantum T-gates, and demonstrate that for the qubit system, there are twelve MUB fiducial states, which coincide with the H-type magic states. We compare MUB fiducial states and SIC-POVM fiducial states from the perspective of magic resource for stabilizer quantum computation. We further pose the challenging issue of identifying all MUB fiducial states in general dimensions.

Phosphorene junctions as a platform for spin-selective quantum dots in next-generation devices

The impact of vacancies on spin-resolved electronic properties of quantum dots (QDs) in phosphorene-based junctions is investigated numerically. Regardless of the crystal orientation, a phosphorene nanoribbon containing a monovacancy is found to exhibit a topological quasi-flatband that emerges within the bandgap. The electronic properties of QDs, including spatial confinement and energy level distribution, can be strongly tuned by controlling the topological structure of the QDs and by applying electric fields. Additionally, these QDs exhibit remarkable spin-selective properties under a ferromagnetic exchange field, enabling the manipulation of QD features. This opens up the potential for novel applications such as quantum computing, magnetic sensing, spin-based light emission.

Leveraging artificial intelligence in economics and finance: Enhancing decision-making and market efficiency

Abstract. Artificial Intelligence (AI) has revolutionized the fields of economics and finance by providing advanced tools for decision-making, predictive analysis, and market efficiency. This paper examines the integration of AI technologies such as machine learning and natural language processing with mathematical models, specifically ARIMA for economic forecasting, Black-Scholes for option pricing, and logistic regression for credit risk assessment. By enhancing these models with AI, we demonstrate significant improvements in prediction accuracy and decision-making capabilities. Case studies illustrate a 15% improvement in GDP growth prediction accuracy, a 20% reduction in option pricing errors, and a 20% decrease in credit default rates. The paper also discusses the future prospects of AI, including advancements in quantum computing and ethical considerations like data privacy and algorithmic bias. Implementation challenges such as high costs and data integration issues are addressed, providing a comprehensive roadmap for organizations to effectively leverage AI. This study underscores the transformative potential of AI in shaping the future of economic and financial landscapes, driving innovation and operational efficiency.

The Pervasive Impact of Software Engineering and Architecture: A Multi-Industry Analysis

This article presents a comprehensive analysis of the ubiquitous role of software engineering and architecture across diverse industries in the modern digital landscape. Through a combination of literature review, industry case studies, and expert interviews, we examine how core software engineering principles—such as scalability, security, user experience, and data management—are applied and adapted in sectors ranging from e-commerce and fintech to healthcare, transportation, media, manufacturing, and government services. Our findings reveal both common challenges and industry-specific innovations in areas such as blockchain technology, telemedicine, autonomous vehicles, and industrial IoT. We also explore emerging trends, including the integration of artificial intelligence, edge computing, and quantum computing, and their potential impact on future software architectures. Furthermore, this article addresses critical ethical considerations and societal implications, including data privacy, job displacement, and the digital divide. By synthesizing these multifaceted perspectives, we provide valuable insights for both industry practitioners and academic researchers, highlighting the transformative power of software engineering in shaping the technological foundations of contemporary society and identifying key areas for future research and development.

Predicting arbitrary state properties from single Hamiltonian quench dynamics

Analog quantum simulation is an essential routine for quantum computing and plays a crucial role in studying quantum many-body physics. Typically, the quantum evolution of an analog simulator is largely determined by its physical characteristics, lacking the precise control or versatility of quantum gates. This limitation poses challenges in extracting physical properties on analog quantum simulators, an essential step of quantum simulations. To address this issue, we introduce the protocol, which uses a single quench Hamiltonian for estimating arbitrary state properties, eliminating the need for ancillary systems and random unitaries. Additionally, we derive the sample complexity of this protocol and show that it performs comparably to the classical shadow protocol. The Hamiltonian shadow protocol does not require sophisticated control and can be applied to a wide range of analog quantum simulators. We demonstrate its utility through numerical demonstrations with Rydberg atom arrays under realistic parameter settings. The new protocol significantly broadens the application of randomized measurements for analog quantum simulators without precise control and ancillary systems. Published by the American Physical Society 2024

Impact of quantum information encoding and metallic leads on dynamical multipartite correlation formation in semiconductor quantum dot arrays.

This study investigates quantum information scrambling (QIS) in a semiconductor quantum dot array. Starting with the 1D Transverse Field Ising model, we expand to more relevant quasi-2D frameworks such as the Heisenberg chain, super-extended Fermi-Hubbard and hardcore Fermi-Hubbard models. Assessing their relevance to semiconductor spin-qubit quantum computers, simulations of multipartite entanglement formation examine qubit encoding strategies' fidelity, stability, and robustness, revealing trade-offs among these aspects. Furthermore, we investigate the weakly coupled metallic injector/detector (I/D) leads' significant impact on QIS behavior by employing Ω lead N-single orbital impurities weakly coupled Anderson models. We observe sign flips in spatiotemporal tripartite mutual information I3 which result in significant effects on dynamical quantum entanglement structures and their formation. Exploring carrier number effects, we identify optimal regions for QIS enhancement. Our findings emphasize the necessity of proper qubit encoding and I/D lead influence on quantum devices amidst noise and impurities.

Polymorphism in Type-II Dirac Semimetal WSi2 under Pressure: Structural, Mechanical, and Electronic Insights.

The type-II Dirac candidate semimetal WSi2 is a promising candidate for electronic devices, quantum computing, and topological materials research, owing its distinct electronic structure and superior mechanical properties. Here, we synthesized high-quality WSi2 materials and systematically investigated their compressive behavior, and structural and electronic properties under high pressure using in-situ high pressure experiments, complemented by first-principles calculations. The results confirms that WSi2 has the properties of a type-II Dirac semimetal. Our results demonstrate that WSi2 maintains structural stability under high pressure but undergoes an electronic phase transition from a semimetal to a metal around 40 GPa. Additionally, the mechanical hardness softens discontinuously at this pressure. The structural stability of WSi2 under high pressure is attributed to the strong hybridization of Si-3p and W-5d electrons, the rigid crystal lattice, and the adaptable electronic structure. The pressure-induced electronic phase transition and softening are primarily governed by the energy band reconstruction and W-5d orbitals. This study provides valuable insights into the high-pressure behavior of type-II Dirac semimetal, highlighting their potential for advanced applications in electronic devices and topological quantum computing under extreme conditions by elucidating their structural stability and electronic phase transition mechanisms.

Exploring ground states of Fermi-Hubbard model on honeycomb lattices with counterdiabaticity

Exploring the ground state properties of many-body quantum systems conventionally involves adiabatic processes, alongside exact diagonalization, in the context of quantum annealing or adiabatic quantum computation. Shortcuts to adiabaticity by counter-diabatic driving serve to accelerate these processes by suppressing energy excitations. Motivated by this, we develop variational quantum algorithms incorporating the auxiliary counter-diabatic interactions, comparing them with digitized adiabatic algorithms. These algorithms are then implemented on gate-based quantum circuits to explore the ground states of the Fermi-Hubbard model on honeycomb lattices, utilizing systems with up to 26 qubits. The comparison reveals that the counter-diabatic inspired ansatz is superior to traditional Hamiltonian variational ansatz. Furthermore, the number and duration of Trotter steps are analyzed to understand and mitigate errors. Given the model’s relevance to materials in condensed matter, our study paves the way for using variational quantum algorithms with counterdiabaticity to explore quantum materials in the noisy intermediate-scale quantum era.

Federated learning with tensor networks: a quantum AI framework for healthcare

Abstract The healthcare industries frequently handle sensitive and proprietary data, and due to strict
privacy regulations, they are often reluctant to share data directly. In today’s context, Federated
Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine
learning while effectively managing critical concerns regarding data privacy and governance.
The fusion of federated learning and quantum computing represents a groundbreaking interdisciplinary
approach with immense potential to revolutionize various industries, from healthcare to
finance. In this work, we propose a federated learning framework based on quantum tensor networks
that takes advantage of the principles of many-body quantum physics. Currently, there are
no known classical tensor networks implemented in federated settings. Furthermore, we investigated
the effectiveness and feasibility of the proposed framework by conducting a differential privacy analysis
to ensure the security of sensitive data across healthcare institutions. Experiments on popular
medical image datasets show that the federated quantum tensor network model achieved a mean
receiver-operator characteristic area under the curve (ROC-AUC) of 91-98%, outperforming several
state-of-the-art federated learning methods while using fewer parameters. Experimental results
demonstrate that the quantum federated global model, consisting of highly entangled tensor network
structures, showed better generalization and robustness and achieved higher testing accuracy,
surpassing the performance of locally trained clients under unbalanced data distributions among
healthcare institutions.

Benchmarking a trapped-ion quantum computer with 30 qubits

Benchmarking a trapped-ion quantum computer with 30 qubits

The Awe-Inspiring Power of Quantum Computers

Quantum computing is an emerging field of research and technology that harnesses a science called quantum mechanics to create computers with revolutionary capabilities. Although existing quantum computers are limited in size and prone to significant errors, future quantum computers might be capable of performing tasks that were once considered unimaginable using even the world’s most powerful supercomputers. This means that quantum computers could revolutionize many important areas of our lives! In this article, we will explore quantum computing by first reviewing how our current computers work. Then, we will dive into what makes quantum computers potentially far more powerful. We will especially focus on the source of their immense power: the ability of tiny particles to be in multiple states at the same time!

Simulating quantum chaos on a quantum computer

Noisy intermediate-scale quantum (NISQ) computers provide a new experimental platform for investigating the behaviour of complex quantum systems. We show that currently available NISQ devices can be used for versatile quantum simulations of chaotic systems. We introduce a classical-quantum hybrid approach for exploring the dynamics of the chaotic quantum kicked top (QKT) on a quantum computer. The programmability of this approach allows us to experimentally explore a broad range of QKT chaoticity parameter regimes inaccessible to previous studies. Furthermore, the number of gates in our simulation does not increase with the number of kicks, thus making it possible to study the QKT evolution for arbitrary number of kicks without fidelity loss. Using a publicly accessible NISQ computer (IBMQ), we observe periodicities in the evolution of the 2-qubit QKT, as well as signatures of chaos in the time-averaged 2-qubit entanglement. We also demonstrate a connection between entanglement and delocalization in the 2-qubit QKT, confirming theoretical predictions.

A Hybrid Quantum-Classical Model for Stock Price Prediction Using Quantum-Enhanced Long Short-Term Memory

The stock markets have become a popular topic within machine learning (ML) communities, with one particular application being stock price prediction. However, accurately predicting the stock market is a challenging task due to the various factors within financial markets. With the introduction of ML, prediction techniques have become more efficient but computationally demanding for classical computers. Given the rise of quantum computing (QC), which holds great promise for being exponentially faster than current classical computers, it is natural to explore ML within the QC domain. In this study, we leverage a hybrid quantum-classical ML approach to predict a company’s stock price. We integrate classical long short-term memory (LSTM) with QC, resulting in a new variant called QLSTM. We initially validate the proposed QLSTM model by leveraging an IBM quantum simulator running on a classical computer, after which we conduct predictions using an IBM real quantum computer. Thereafter, we evaluate the performance of our model using the root mean square error (RMSE) and prediction accuracy. Additionally, we perform a comparative analysis, evaluating the prediction performance of the QLSTM model against several other classical models. Further, we explore the impacts of hyperparameters on the QLSTM model to determine the best configuration. Our experimental results demonstrate that while the classical LSTM model achieved an RMSE of 0.0693 and a prediction accuracy of 0.8815, the QLSTM model exhibited superior performance, achieving values of 0.0602 and 0.9736, respectively. Furthermore, the QLSTM outperformed other classical models in both metrics.

Synthesis, Characterization and Theoretical Calculations of a Novel Organic Corrosion Inhibitor for Mild Steel

Abstract Q235 is a carbon structural steel sheet that is widely employed in the construction and engineering sectors due to its cost-effectiveness and durability. Here, A novel organic corrosion inhibitor for Q235 sheet based on furan-thiadiazole (5-amino-2-furyl-1, 3, 4-thiadiazole, AFTZ) has been designed, successfully synthesized and characterized in this work. Results of experimental data and theoretical calculations indicated that AFTZ has an excellent corrosion inhibition property for Q235 sheet in 1.0 mol/L H2SO4. The highest efficiency of corrosion inhibitor tested by weight loss experiment can reach 91.17% and electrochemical experiment was up to 95.85% when AFTZ increased to 2 mg/mL in H2SO4 solution. The morphological characteristics of Q235 sheets with or without corrosion inhibitor showed that the target inhibitor has good corrosion inhibition performance. Quantum computation using the DFT method of guassian09 software and FT-IR date attribute the excellent corrosion inhibition to the film-forming nature of AFTZ.

A Survey of AI-Powered Proactive Threat-Hunting Techniques: Challenges and Future Directions

The study narrates the dimensions of AI-powered threat hunting techniques that keep changing and draws on their importance in cybersecurity measures while dealing with advanced cyber threats. While the cyber adversary is continuously adapting and innovating, the integration of advanced AI algorithms, including reinforcement learning and hybrid models, has become one of the key strategies for real-time threat detection and response. The research investigates the transformative potential of these algorithms to facilitate adaptive systems that can quickly recognize and act on new attack vectors. It also discusses the integration of AI with emergent technologies like blockchain, quantum computing, and IoT in the dimension of their potential to leverage threat intelligence and response mechanisms. This would naturally lead to increased efficiency in threat hunting by leveraging the strengths brought into this endeavor by both the AI systems and human analysts. The study also advocates for the development of strong policy and governance frameworks to drive home some ethical concerns in deploying AI in cybersecurity. Such a framework is very important for addressing challenges on data privacy, accountability, and transparency. The findings bring into sharp relief that, in this continuing evolution of the cybersecurity landscape, what will work best is a proactive stance-a proactive stance that incorporates innovative technologies with human ingenuity and ethics. This white paper broadly documents the considerable promise of AI in threat hunting and strategic advances needed to harden organizational defenses against a dynamic threat environment.

Unlocking financial innovation through strategic investments in information management: a systematic review

PurposeThe purpose of this study is to explore strategic investment in information management and its crucial role in driving financial innovation. By examining the integration of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Blockchain, Big Data Analytics, Cloud Computing, Large Language Models (LLMs), Robotic Process Automation (RPA), Internet of Things (IoT), Cybersecurity Technologies, and Quantum Computing, this research aims to highlight how these technologies enhance decision-making, operational effectiveness, risk management, and compliance within the financial sector.MethodologyThe study employs a comprehensive literature review of existing research to analyze the impact of strategic investment in information management on financial innovation. Key technologies are identified and their applications in finance are discussed. The methodology includes synthesizing findings from various sources to present a cohesive understanding of the relationship between information management, technology, and financial innovation.ResultsThe results indicate that strategic investment in information management significantly enhances financial innovation by leveraging advanced technologies. AI and ML improve predictive analytics and customer personalization, Blockchain ensures secure transactions and transparency, Big Data Analytics enables data-driven decision-making, and Cloud Computing provides scalable solutions. LLMs enhance natural language processing capabilities, RPA automates repetitive tasks, IoT facilitates real-time monitoring, Cybersecurity Technologies protect financial data, and Quantum Computing offers potential breakthroughs in financial modeling and encryption.ImplicationThe implications of this study suggest that financial institutions should prioritize strategic investments in information management and the adoption of advanced technologies to stay competitive and resilient in the evolving financial landscape. Effective information management practices enable better decision-making, improved operational efficiencies, enhanced risk management, and regulatory compliance, ultimately fostering financial innovation.ContributionThis study contributes to the existing body of knowledge by providing a detailed analysis of the role of strategic investment in information management and its impact on financial innovation. It highlights the importance of integrating advanced technologies in financial practices and offers insights into how these technologies can be leveraged to achieve innovative solutions and improvements in the financial sector. The findings serve as a valuable resource for financial institutions, policymakers, and researchers interested in the intersection of technology and finance.

Reducing the measurement errors in nonadiabatic holonomic quantum computers

Reducing the measurement errors in nonadiabatic holonomic quantum computers