Quantum Computation Research Articles

Qubit: The Leap into Quantum Computation

Abstract. This article delves into the fundamental aspects of qubits in quantum computation, emphasizing their coordination, mathematical representation through the Bloch Sphere, and the unique advantages and challenges of superconducting, trapped ion, and photonic qubits. It highlights the transformative potential of quantum algorithms in solving complex problems in cryptography and information security, while addressing key sources of quantum errors such as decoherence and quantum noise. The study discusses advanced error correction methods and underscores the necessity of improving qubit coherence, stability, scalability, and integration for the practical application and commercialization of quantum computers. Ongoing research and collaboration are deemed essential for overcoming technical challenges and unlocking the full potential of quantum computation across various industries.

Opening the Black Box inside Grover’s Algorithm

Grover’s algorithm is one of the primary algorithms offered as evidence that quantum computers can provide an advantage over classical computers. It involves an “oracle” (external quantum subroutine), which must be specified for a given application and whose internal structure is not part of the formal scaling of the quadratic quantum speedup guaranteed by the algorithm. Grover’’s algorithm also requires exponentially many calls to the quantum oracle (approximately 2n calls where n is the number of qubits) to succeed, raising the question of its implementation on both noisy and error-corrected quantum computers. In this work, we construct a quantum-inspired algorithm executable on a classical computer that performs Grover’s task in a linear number of calls to (simulations of) the oracle—an exponentially smaller number than Grover’s algorithm—and demonstrate this algorithm explicitly for Boolean satisfiability problems. The complexity of our algorithm depends on the cost to simulate the oracle once, which may or may not be exponential, depending on its internal structure. Indeed, Grover’s algorithm does not have an quantum speedup as soon as one is given access to the “source code” of the oracle, which may reveal an internal structure of the problem. Our findings illustrate this point explicitly, as our algorithm exploits the structure of the quantum circuit used to program the quantum computer to speed up the search. There are still problems where Grover’s algorithm would provide an asymptotic speedup if it could be run accurately for large enough sizes. Our quantum-inspired algorithm provides lower bounds, in terms of the quantum-circuit complexity, for the quantum hardware to beat classical approaches for these problems. These estimates, combined with the unfavorable scaling of the success probability of Grover’s algorithm, which in the presence of noise decays as the exponential of the exponential of the number of qubits, makes a practical speedup unrealistic even under extremely optimistic assumptions of the evolution of both hardware quality and availability. Published by the American Physical Society 2024

Analysis of the Principles of Quantum Computing and State-of-the-Art Applications

Abstract. Contemporarily, quantum computing has emerged as a promising field, offering potential breakthroughs in various computational tasks that are currently limited by classical computing. With this in mind, this study delves into the principles of quantum computing, exploring the fundamental concept of quantum entanglement and its implications for computation. After outlining the historical development and research significance of quantum computing, this research presents an overview of the latest advancements in the field. The paper then focuses on the principles of quantum computation, including the use of qubits and quantum gates, illustrated with relevant mathematical formulations and diagrams. Furthermore, this study discusses the state-of-the-art applications of quantum computing, showcasing recent achievements and results obtained from these cutting-edge technologies. A comparative analysis with traditional algorithms highlights the advantages and potential gains offered by quantum computing. Finally, the current limitations of quantum computing are discussed and the insights into future research directions and prospects are proposed for this exciting field.

Quantifying entanglement for unknown quantum states via artificial neural networks

Quantum entanglement acts as a crucial part in quantum computation and quantum information, hence quantifying unknown entanglement is an important task. Due to the fact that the amount of entanglement cannot be achieved directly by measuring any physical observables, it remains an open problem to quantify entanglement experimentally. In this work, we provide an effective way to quantify entanglement for the unknown quantum states via artificial neural networks. By choosing the expectation values of measurements as input features and the values of entanglement measures as labels, we train artificial neural network models to predict the entanglement for new quantum states accurately. Our method does not require the full information about unknown quantum states, which highlights the effectiveness and versatility of machine learning in exploring quantum entanglement.

Quantum Neural Networks: A New Frontier

Abstract. In recent years, there has been remarkable progress in improving the availability of resources and refining algorithms for quantum computing. Since the late 1980s, the scientific community has been fascinated by the idea of harnessing quantum phenomena to tackle computational problems. This article provides a comprehensive exploration of the foundational theories and practical applications of quantum neural networks (QNNs), highlighting their potential to transform machine learning through unique features like quantum parallelism and entanglement. It delves into various QNN architectures, such as quantum circuits and hybrid quantum-classical models, showcasing their effectiveness in handling intricate computational tasks more efficiently than traditional neural networks. Furthermore, the article examines the current challenges and future prospects in this rapidly advancing field, emphasizing the pivotal role of QNNs in driving forward research in both quantum computing and artificial intelligence. Quantum neural networks are poised to not only enhance computational capabilities but also pave the way for groundbreaking innovations in diverse technological domains.

Schema Extraction in NoSQL Databases: A Systematic Literature Review

Introduction: Nowadays, NoSQL databases have taken on an increasingly important role in the storage of massive data within companies. Due to a common property called schema-less, NoSQL databases offer great flexibility, particularly for the storage of data in different formats. However, despite their success in data storage, schema-less databases are a major obstacle in areas requiring precise knowledge of this schema, especially in the field of data integration. Method: This study presents a Systematic Literature Review (SLR) to explore, evaluate, and discuss relevant existing research and endeavors using novel schema extraction approaches. Furthermore, we conducted this study using a well-defined methodology to examine and study the problem of schema extraction from NoSQL databases. Results: Our research results highlight and emphasize the scheme extraction approaches and provide knowledge to researchers and practitioners by proposing schema extraction approaches and their limitations, which contributes to inventing new, more efficient approaches. Conclusion: In our future work, inspired by the recent advances in quantum computing and the emergence of post-quantum cryptography (PQC), we aim to propose a schema extraction approach that blends cutting-edge technologies with a strong focus on database security.

Optomechanical entanglement induced by backward stimulated Brillouin scattering

We propose a scheme to generate robust optomechanical entanglement, i.e., the entanglement between a mechanical and an optical modes. This scheme is based on a Backward Stimulated Brillouin Scattering (BSBS) process, which is hosted within an optomechanical structure. Our benchmark system consists of an acoustic (mechanical) mode coupled to two optical modes through an electrostrictive (radiation pressure) effect. After determining the optimal acoustic parameters allowing the entanglement in our system, we have shown that both the acoustic coupling and the decay rate require a certain threshold from where the optomechanical entanglement is generated. For instance, to generate an optomechanical entanglement in our proposal, the strength of the used acoustic decay rate most exceed both the mechanical and optical decay rates, which is the figure of merit of our proposal. The generated entanglement is robust enough against thermal fluctuation. Our work provides a new scheme for entanglement generation based on BSBS effect, and can be extended to microwaves and hybrid optomechanical structures. Such a generated entangled states can be used for quantum information processing, quantum sensing, and quantum computing.

Quantum Computation of Hydride Ion using Variational Quantum Algorithm

AbstractBecause of remarkable reactivity and strong electron‐electron correlation effects, the precise prediction of ground state energy and chemical reactivity of hydride ion is an essential objective in quantum chemistry. Leveraging variational quantum algorithms offers a promising avenue for studying molecular properties using current noisy intermediate‐scale quantum devices. This work utilises the variational approach to anticipate the ground state, reactivity, and single‐electron detachment energy of the three‐body hydride ion. We investigated both Hardware‐Efficient Ansatz (HEA) and Chemistry‐inspired ansatz based on a Unitary Coupled Cluster (UCC) on both noiseless and noisy IBM simulators. Modern error‐mitigating techniques, such as Zero‐Noise Extrapolation (ZNE) with unitary folding and measurement error mitigation, have been implemented to significantly reduce errors in noisy environments. This study contributes to our understanding of the quantum computational nuances of the hydride ion and addresses the question of whether quantum computers can retain the correlation energies for these correlated ions.

Quantum support vector machines: theory and applications

Abstract. Quantum Support Vector Machines (QSVMs) combine the fundamental principles of quantum computing and classical Support Vector Machines (SVMs) to improve machine learning performance. In this paper, the author will further explore QSVM. Firstly, introduce the basics of classical SVM, including hyperplane, margin, support vector, and kernel methods. Then, introduce the basic theories of quantum computing, including quantum bits, entanglement, quantum states, superposition, and some related quantum algorithms. Focuses on the concept of QSVM, quantum kernel methods, and how SVM runs on a quantum computer. Key topics include quantum state preparation, measurement, and output interpretation. Theoretical advantages of QSVMs, such as faster computation speed, stronger performance to process high-dimensional data, and kernal computation. In addition, the author discussed the implementation of QSVM, quantum algorithms, quantum gradient descent, and optimization techniques for SVM training. The article also discusses practical issues such as error mitigation and quantum hardware requirements. The purpose of this paper is to show the advantages of QSVM by comparing SVM and QSVM.

Quantum Entanglement and Nonlocality: Challenging the Local Realism of Classical Physics

Abstract. This paper delves into the significance and impact of quantum entanglement across the fields of physics, information science, and philosophy. Utilizing a literature review approach, the paper first introduces the concept and historical background of quantum entanglement, emphasizing its challenge to the limitations of classical physics and its potential applications in quantum communication and quantum computing. This paper thoroughly explains the theoretical foundations of the Einstein-Podolsky-Rosen experiment and Bell's inequality, along with related experimental validations and key results. In particular, it elucidates how quantum entanglement reveals the nonlocality in quantum mechanics and the characteristics of nonlocal information transfer. The discussion then shifts to the challenge quantum entanglement poses to physical ontology, exploring the philosophical implications and the new perspectives it offers in information theory. Finally, this study concludes by summarizing the profound impact of quantum entanglement as one of the most challenging and inspiring research areas in contemporary physics on scientific theory and philosophical thought, as well as anticipating future research directions and potential applications.

Antioxidant capacity of edaravone, quercetin, and myricetin involving probabilistic fluctuations using eosin-Y and eosin-B as fluorescent probes in the ORAC assay

The oxygen radical absorbance capacity (ORAC) assay measures the antioxidant activity of the antioxidants through competitive consumption of peroxyl radicals relative to a fluorescent probe. This study evaluated fluorescein (FLH), eosin Y, and eosin B in ORAC assays using quantum computations. In ORAC-FLH assays, trolox (TRO) and ascorbic acid (ASC) showed fluorescence decay kinetics with a lag time, blocking initial reactions in the quenching cascade. Eosins were more reactive towards peroxyl radicals, making TRO assessment infeasible in ORAC-eosin assays. Quercetin and myricetin exhibited sigmoidal curve drifts proportional to squared and 3/2-ordered incubation times, indicating probabilistic fluorescence decay. Edaravone (EDA) weakly inhibited initial reactions, with accelerated quenching over time. In ORAC-eosin assays, flavonoids and EDA showed indistinguishable behavior due to high eosin reactivity. ORAC-FLH did not significantly assess BHT, though a dose-dependent change in half-life was noted. This study suggests broad applications for ORAC-eosin assays and potential for future comparative research.

Highly Efficient Single Photon Coupling via Surface Plasmons into Single-Mode Optical Fiber

Quanta of light (single photon) play as one of the building blocks of photonic based quantum computations, sensing, and communications. This makes a necessity to develop practical approaches for tuning, guiding, and coupling of single photons in photonic circuits for viable applications. Interaction and coupling efficiency of single photon into optical fibers is a technical bottleneck of quantum optics and should be addressed by novel design and materials. Here, we introduce a fiber-based micro-photonic design to directly coupling of the emitted single-photon to the core of a single mode fiber (SMF). The results of the simulation indicate that the emission of single photon source on a D-shaped SMF coated by a thin plasmonic film, provide remarkable amplifying of the evanescent field by confined surface plasmons into the SMF. The numerical analysis of different types and thicknesses of plasmonic materials by finite element method (FEM) is conducted to study the propagation vectors along the SMF as a function of the emission angle and wavelength of the single photon source. The results revealed that by the optimum thickness of the tantalum layer as novel plasmonic material, the best record of coupling efficiency can be achieved in the fiber optics communication region (λ ∼ 1550 nm). This approach sheds light on novel plasmonic-assisted coupling and promises a functional strategy for single photon manipulation in various fields of quantum optics.

The service trade with AI and energy efficiency: Multiplier effect of the digital economy in a green city by using quantum computation based on QUBO modeling

This research examines the energy efficiency of city districts through the Malmquist–DEA model and investigates the spatial effects of the service trade and the digital economy on energy efficiency in urban green development. The study also delves into the specific context of the AI service trade to gain insights into and align with the emerging digital intelligence industry. The interplay of the service trade with the digital economy, alongside the AI service trade with innovation, significantly enhances urban energy efficiency and demonstrates positive externalities. Building on the empirical findings, this research employs cluster analysis to explore the green development of city districts and uses AI technology to program green communication and cooperation mechanisms across district clusters, employing quantum computation based on QUBO modeling. This study contributes to a deeper understanding of the cofunction of the service trade and the digital economy in terms of energy efficiency and aids in developing new quality productivities for green cities through quantum-based AI advancements. This research has clear implications for cutting-edge interdisciplinary applications of green AI technologies in social computing science.

Prediction of chaotic dynamics and extreme events: A recurrence-free quantum reservoir computing approach

In chaotic dynamical systems, extreme events manifest in time series as unpredictable large-amplitude peaks. Although deterministic, extreme events appear seemingly randomly, which makes their forecasting difficult. By learning the dynamics from observables (data), reservoir computers can time accurately predict extreme events and chaotic dynamics, but they may require many degrees of freedom (large reservoirs). In this paper, by exploiting quantum-computer ansätze and entanglement, we design reservoir computers with compact reservoirs and accurate prediction capabilities. First, we propose the recurrence-free quantum reservoir computer (RF-QRC) architecture. By developing quantum feature maps and removing recurrent connections, the RF-QRC has quantum circuits with smaller depths. This allows the RF-QRC to scale well with higher-dimensional chaotic systems, which makes it suitable for hardware implementation. Second, we forecast the temporal chaotic dynamics and their long-term statistics of low- and higher-dimensional dynamical systems. We find that RF-QRC requires smaller reservoirs than classical reservoir computers for higher-dimensional systems and the same predictability. Third, we apply the RF-QRC to the time prediction of extreme events in a model of a turbulent shear flow with turbulent bursts. We find that the RF-QRC has longer predictability than the classical reservoir computer for extreme events forecasting. The results and analyses indicate that quantum-computer ansätze offers nonlinear expressivity and computational scalability, which are useful for forecasting chaotic dynamics and extreme events. This work opens new opportunities for using quantum machine learning on near-term quantum computers. Published by the American Physical Society 2024

Hybrid Hamiltonian Simulation for Excitation Dynamics.

Hamiltonian simulation is one of the most anticipated applications of quantum computing. Quantum circuit depth for implementing Hamiltonian simulation is commonly time dependent using Trotter-Suzuki product formulas so that long time quantum dynamic simulations (QDSs) become impratical for near-term quantum processors. Hamiltonian simulation based on Cartan decomposition (CD) provides an appealing scheme for QDSs with fixed-depth circuits, while it is limited to a time-independent Hamiltonian. In this work, we generalize this CD-based Hamiltonian simulation algorithm for studying time-dependent systems by combining it with variational quantum algorithms. The time-dependent and time-independent parts of the Hamiltonian are treated by using variational and CD-based Hamiltonian simulation algorithms, respectively. As such, this hybrid Hamiltonian simulation requires only fixed-depth quantum circuits to handle time-dependent cases while maintaining a high accuracy. We apply this new algorithm to study the response of spin and molecular systems to δ-kick electric fields and obtain accurate spectra for these excitation processes.

Novel Wafer-Level Ta-Ta Direct Thermocompression Bonding for 3D Integration of Superconducting Interconnects for Scalable Quantum Computing System

Novel Wafer-Level Ta-Ta Direct Thermocompression Bonding for 3D Integration of Superconducting Interconnects for Scalable Quantum Computing System

Fermi Arcs Dominating the Electronic Surface Properties of Trigonal PtBi2

AbstractMaterials combining topologically non‐trivial behavior and superconductivity offer a potential route for quantum computation. However, the set of available materials intrinsically realizing these properties are scarce. Recently, surface superconductivity has been reported in PtBi2 in its trigonal phase and an inherent Weyl semimetal phase has been predicted. Here, based on scanning tunneling microscopy experiments, the signature of topological Fermi arcs are revealed in the normal state patterns of the quasiparticle interference. It is shown that the scattering between Fermi arcs dominates the interference spectra, providing conclusive evidence for the relevance of Weyl fermiology for the surface electronic properties of trigonal PtBi2.

Unlocking Complexity: D-Wave\'s Role in Quantum Computing Breakthroughs

In this new era of Quantum Computing, a transformative paradigm is emerging for the enhancement of Cyber Security using the special-purpose systems like D-Wave's quantum annealers which helps the encryption system become more resilient. As quantum computing evolves, it provides new paradigms for breaking the traditional encryption methods, as the recent breakthroughs is accomplished by researchers from Shanghai University that uses D-Wave's quantum annealing capabilities in providing the researchers to successfully demonstrated the potential to compromise widely used encryption systems, including RSA and AES. The usage of quantum annealing (QA) that makes the D-Wave’s a specialized quantum system which enables an efficient computation in tackling cryptographic problems, demonstrates the implications for the data security and privacy by applying the quantum-classical hybrid architectures. This research aims to get attention on the urgent need for developing a new security measures in response to the vulnerabilities exposed by the quantum advancements which emphasizes the crucial role of D-Wave's in unlocking the complexities of quantum computing and its implications for the future of cybersecurity.

Survey Paper on Quantum Deep Reinforcement Learning for Robot Navigation Tasks

Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation

Single-Step Synthesis of An Ideal Chain Antiferromagnet [H2(4,4'-bipyridyl)](H3O)2Fe2F10 with Spin S=5/2.

One-dimensional (1D) magnets are of great interest owing to their intriguing quantum phenomena and potential application in quantum computing. We successfully synthesized an ideal antiferromagnetic spin S=5/2 chain compound [H2(4,4'-bpy)](H3O)2Fe2F10 (4,4'-bpy=4,4'-bipyridyl) 1, using a single-step low-temperature hydrothermal method under conditions that favors the protonation of the bulky bidentate ligand 4,4'-bpy. Compound 1 consists of well-separated (Fe3+-F-)∞ chains with a large Fe-F-Fe angle of 174.8°. Both magnetic susceptibility and specific heat measurements show that 1 does not undergo a magnetic long-range ordering down to 0.5 K, despite the strong Fe-F-Fe intrachain spin exchange J with J/kB=-16.2(1) K. This indicates a negligibly weak interchain spin exchange J'. The J'/J value estimated for 1 is extremely small (<2.8×10-6), smaller than those reported for all other S=5/2 chain magnets. Our hydrothermal synthesis incorporates both [H2(4,4'-bpy)]2+ and (H3O)+ cations into the crystal lattice with numerous hydrogen bonds, hence effectively separating the (Fe3+-F-)∞ spin chains. This single-step hydrothermal synthesis under conditions favoring the protonation of bulky bidentate ligands offers an effective synthetic strategy to prepare well-separated 1D spin chain systems of magnetic ions with various spin values.