Quantum Potential Research Articles

Revisiting the Mapping of Quantum Circuits: Entering the Multi-core Era

Quantum computing represents a paradigm shift in computation, offering the potential to solve complex problems intractable for classical computers. Although current quantum processors already consist of a few hundred qubits, their scalability remains a significant challenge. Modular quantum computing architectures have emerged as a promising approach to scale up quantum computing systems. This article delves into the critical aspects of distributed multi-core quantum computing, focusing on quantum circuit mapping, a fundamental task to successfully execute quantum algorithms across cores while minimizing inter-core communications. We derive the theoretical bounds on the number of non-local communications needed for random quantum circuits and introduce the Hungarian Qubit Assignment (HQA) algorithm, a multi-core mapping algorithm designed to optimize qubit assignments to cores with the aim of reducing inter-core communications. Our exhaustive evaluation of HQA against state-of-the-art circuit mapping algorithms for modular architectures reveals a 4.9× and 1.6× improvement in terms of execution time and non-local communications, respectively, compared to the best-performing algorithm. HQA emerges as a very promising scalable approach for mapping quantum circuits into multi-core architectures, positioning it as a valuable tool for harnessing the potential of quantum computing at scale.

Enhancing Quantum Algorithms for Quadratic Unconstrained Binary Optimization via Integer Programming

To date, research in quantum computation promises potential for outperforming classical heuristics in combinatorial optimization. However, when aiming at provable optimality, one has to rely on classical exact methods like integer programming. State-of-the-art integer programming algorithms can compute strong relaxation bounds even for hard instances, but may have to enumerate a large number of subproblems for determining an optimum solution. If the potential of quantum computing realizes, it can be expected that in particular finding high-quality solutions for hard problems can be done fast. Still, near-future quantum hardware considerably limits the size of treatable problems. In this work, we go one step into integrating the potentials of quantum and classical techniques for combinatorial optimization. We propose a hybrid heuristic for the weighted maximum-cut problem and for quadratic unconstrained binary optimization. The heuristic employs a linear programming relaxation, rendering it well-suited for integration into exact branch-and-cut algorithms. For large instances, we reduce the problem size according to a linear relaxation such that the reduced problem can be handled by quantum machines of limited size. Moreover, we improve the applicability of depth-1 QAOA, a parameterized quantum algorithm, by deriving a parameter estimate for arbitrary instances. We present numerous computational results from real quantum hardware.

Graphene Quantum Dots‐Based Materials as an Emerging Nanoplatform in Disease Diagnosis and Therapy

AbstractGraphene quantum dots are a subclass of graphene‐based materials that exhibit unique properties due to their nanoscale size and quantum confinement effects. Discovered in the early 21st century, these zero‐dimensional carbon nanomaterials have rapidly become important in nanotechnology research due to their diverse applications. In recent years, the medical community has been greatly benefited from these materials, significantly enhancing human health and well‐being with theranostic approaches. The present review explores various applications of graphene quantum dots in diagnostic and therapeutic, unraveling their potential contributions to advancing healthcare. Furthermore, this review elucidates the synthesis methods utilized for graphene quantum dots, encompassing a range of top‐down and bottom‐up approaches. Next, the unique fundamental properties including structural, optical, and electrical that make them a potent nanomaterial for use in healthcare have been elucidated for enhanced reader comprehension. Additionally, the review explores the opportunities and challenges ahead, offering valuable insights to help the scientific community strategically expand the potential of graphene quantum dot‐based materials for advanced theranostic healthcare applications.

Quantum social network analysis: Methodology, implementation, challenges, and future directions

Quantum social network analysis (QSNA) is a recent advancement in the interdisciplinary field of quantum computing and social network analysis. This manuscript comprehensively reviews QSNA, emphasizing its methodologies, implementation strategies, challenges, and potential applications. It explores the conceptual foundation of key social network analysis research problems, including link prediction, influence maximization, and community detection. The research examines how quantum algorithms can revolutionize such social network tasks by leveraging principles from quantum mechanics and information theory and highlights the advantages of quantum algorithms in handling complex social network structures. The implementation section delves into the practical aspects of QSNA, such as frameworks, experimental setups, and evaluation methods. We assess the capabilities of existing quantum programming language tools and platforms. Various case studies illustrate the potential of quantum computing to enhance the performance of social network analysis. Additionally, we identify several crucial challenges and future research directions for QSNA, including the complexity of developing quantum algorithms, the need for interdisciplinary knowledge, and the challenges of integrating quantum and classical computing resources. This paper aims to serve as a foundational resource for researchers and practitioners, providing insights into the transformative potential of quantum computing in advancing the analysis of social networks and outlining future research directions in this emerging field.

On the potential of quantum walks for modeling financial return distributions

Accurate modeling of the temporal evolution of asset prices is crucial for understanding financial markets. We explore the potential of discrete-time quantum walks to model the evolution of asset prices. Return distributions obtained from a model based on the quantum walk algorithm are compared with those obtained from classical methodologies. We focus on specific limitations of the classical models, and illustrate that the quantum walk model possesses great flexibility in overcoming these. This includes the potential to generate asymmetric return distributions with complex market tendencies and higher probabilities for extreme events than in some of the classical models. Furthermore, the temporal evolution in the quantum walk possesses the potential to provide asset price dynamics.

Quantum potential energy: Adaptive facades in architecture

This paper addressed the topic of creating quantum architecture through human awareness to transform the formal and spatial elements of architecture according to its environmental sustainability compatible with nature. The research emphasizes the creation of a built environment that uses natural resources to enhance the psychological and physical comfort of humans, relying on the quantum energy inherent in the interaction of particles, which is called “kinetic energy” in quantum mechanics, and it can be considered energy due to motion. The research aims to determine the recommended standards for use in designing the building facade according to the magnetic field lines. It focuses on the classification of smart interfaces in particular. The complexity of evaluating adaptive or dynamic facades is related to evaluating the performance of the facade elements and systems, the overall performance of the building, as well as the behavior and satisfaction of the occupants. The research seeks to present a conceptual and cognitive framework by linking the concept of potential kinetic energy and quantum theory with architecture and then presents a group of literary studies that will enrich and build the theoretical framework and then apply it to the groups of projects selected for practical study and then reach the final conclusions.

Wheeler-DeWitt canonical quantum gravity in hydrogenoid atoms according to de Broglie-Bohm and the geodesic hypothesis. Einstein’s Quantum Field Equation

We explore the geodesic hypothesis of orbital trajectories of the electrons in hydrogenoid atoms, in the frame of de Broglie-Bohm quantum theory. It is intended that the space-time can be curved, at very short distances, by the effect of the joint action of the energy content of the atomic system and the contribution of the electric and quantum potentials. The geodesic hypothesis would explain the non-lose of energy in the electron orbital trajectories. So we explore a conception where particles and waves interact in a closed system: the waves guide the particles and the particles generate the spacetime perturbation that acts as a wave, beyond the pilot-wave theory. We establish the equivalence, in a local neighborhood, between the electron trajectory of an hydrogenoid atom in the Minkowskian space where the de Broglie-Bohm can be cast with its movement in a Lorentzian manifold, according to the concept of metric tangent. Through the geodesic condition and the invariance of the elemental length, we establish a relationship between some components of the metrics. But as the particles in microphysics do not follow the Einstein’s field equation, we consider the 3+1 decomposition according to ADM and the quantization in the Wheeler De Witt theory and with a so-called quantum Einstein field equation, with a decomposition of spacetime into three-dimensional sheets of a spatial character. Then we derive from the moment-energy tensor a further equation between the components of the metrics. It opens the avenue to characterize the metric by an exact solution of the Einstein’s field equations.

Determination of the Dipole Moment Variation Upon Excitation in the Chromophore of Green Fluorescent Protein From Molecular Dynamic Trajectories with QM/MM Potentials Using Machine Learning Methods

Quantum and molecular mechanics (QM/MM) potentials are used to calculate molecular dynamics trajectories for the EYFP protein of the green fluorescent protein family. Machine learning models are constructed to establish the relationship between the geometric parameters of the chromophore in the frame of its trajectory and the properties of its electronic excitation. It is shown that it is not enough to use only bridging bonds between the phenyl and imidazolidone fragments of the chromophore as a geometric parameter, and at least two more neighboring bonds must be added to the model. The proposed models allow determination of the dipole moment variation upon excitation with an average error of 0.11 a.u.

Entanglement of Coupled Harmonic Oscillators without and with Tunneling Effect and Correction Factor

Seeing its simplicity of processing, the system of two coupled harmonic oscillators have witnessed a quick growth of research conducted to quantum information. This approach is explicitly investigated in this paper, in particularly entanglement concept is proved to be intimately related to the tunneling effect. We examine analytically entanglement dynamics by introducing the Lewis and Riesenfeld invariant operator in the Heisenberg picture approach to compute the density matrix on the based of an exact treatment. We use Wigner function of the mixed state as an essential tool to move to linear entanglement entropies and we compute the corresponding correction factor. We follow numerically the evolution of entanglement dynamics without and under tunneling through the quantum potential barrier by introducing two particular models between simple and damped coupled harmonic oscillators. Entanglement dynamics is considered to be average without tunneling, it grows upon encountering the potential barrier and remains moderately constant inside barrier. This specificity is extended for both models but with higher values for damped coupled harmonic oscillators consequently the damping effect rapidly increases entanglement. Correction factor is also considered for both models, it show that; low temperature and high potential barriers make the system more disruptive. An increase of the coupling parameter of the system increase correction factor. Damping make the correction factor more important so damping disturbs more the system. Interference effect increase correction factor and it shows an interference between the values of the barrier penetration integral.

Mergers and Acquisitions in the Digital Age: The Role of Technology in Streamlining Financial Integration

Abstract: This comprehensive article explores the transformative role of technology in streamlining financial integration during mergers and acquisitions (M&A) in the digital age. It examines how advanced technological solutions, including cloud-based Enterprise Resource Planning (ERP) systems, automated reconciliation tools, and artificial intelligence (AI) and machine learning (ML) applications, are revolutionizing traditional M&A processes. The paper delves into the impact of these technologies on consolidating financial data, enabling real-time analysis, reducing manual errors, and providing predictive insights for risk assessment and opportunity identification. It also discusses best practices for successful financial integration, emphasizing the importance of pre-merger due diligence, phased integration approaches, and robust cybersecurity measures. Furthermore, the article investigates emerging trends and future prospects in technology-driven M&A, including the potential of blockchain, advanced analytics, and quantum computing to further enhance integration processes. Drawing on recent research and industry surveys, this study highlights the critical role of technology in realizing M&A potential, improving deal outcomes, and creating strategic value in an increasingly complex business environment. The findings underscore the necessity for organizations to embrace technological advancements to remain competitive in the evolving landscape of corporate mergers and acquisitions.

Design and analysis of parallel quantum transfer fractal priority replay with dynamic memory algorithm in quantum reinforcement learning for robotics

AbstractThis paper introduces the parallel quantum transfer fractal priority reply with dynamic memory (P‐QTFPR‐DM) algorithm, an innovative approach that combines quantum computing and reinforcement learning (RL) to enhance decision‐making in autonomous vehicles. Leveraging quantum principles such as superposition and entanglement, P‐QTFPR‐DM optimises Q‐value approximation through a custom quantum circuit (UQC), facilitating efficient exploration and exploitation in high‐dimensional state‐action spaces. This algorithm utilises a quantum neural network (QNN) with 4 qubits to encode and process Q‐values. The autonomous vehicle, equipped with GPS for real‐time navigation, uses P‐QTFPR‐DM to reach a predefined destination with coordinates 12.82,514,234,148 latitude and 80.0,451,311,962,242 longitude. Through extensive numerical simulations, P‐QTFPR‐DM demonstrates a 30% reduction in decision‐making time and a 25% improvement in navigation accuracy compared to classical RL methods. The QNN‐based approach achieves a 95% success rate in reaching the destination within a 5‐m accuracy threshold, whereas traditional RL methods achieve only an 85% success rate. Dynamic memory management in P‐QTFPR‐DM optimises computational resources, enhancing the vehicle's adaptability to environmental changes. These results highlight the potential of quantum computing to significantly advance autonomous vehicle technology by improving both efficiency and effectiveness in complex navigation tasks. Future research will focus on refining the algorithm and exploring its real‐world applications to enhance autonomous vehicle performance.

Gate-Based Variational Quantum Algorithm for Truss Structure Size Optimization Problem

Quantum computing has become a pivotal innovation in computational science, offering novel avenues for tackling the increasingly complex and high-dimensional optimization challenges inherent in engineering design. This paradigm shift is particularly pertinent in the domain of structural optimization, where the intricate interplay of design variables and constraints necessitates advanced computational strategies. In this vein, the gate-based variational quantum algorithm utilizes quantum superposition and entanglement to improve search efficiency in large solution spaces. This paper delves into the gate-based variational quantum algorithm for the discrete variable truss structure size optimization problem. By reformulating this optimization challenge into a quadratic, unconstrained binary optimization framework, we bridge the gap between the discrete nature of engineering optimization tasks and the quantum computational paradigm. A detailed algorithm is outlined, encompassing the translation of the truss optimization problem into the quantum problem, the initialization and iterative evolution of a quantum circuit tailored to this problem, and the integration of classical optimization techniques for parameter tuning. The proposed approach demonstrates the feasibility and potential of quantum computing to transform engineering design and optimization, with numerical experiments validating the effectiveness of the method and paving the way for future explorations in quantum-assisted engineering optimizations.

Simulating thermodynamic properties of dinuclear metal complexes using Variational Quantum Algorithms

Abstract In this paper, we investigate the use of variational quantum algorithms for simulating the thermodynamic properties of dinuclear metal complexes. Our study highlights the potential of quantum computing to transform advanced simulations and provide insights into the physical behavior of quantum systems. The results demonstrate the effectiveness of variational quantum algorithms in simulating thermal states and exploring the thermodynamic properties of low-dimensional molecular magnetic systems. The findings from this research contribute to broadening our understanding of quantum systems and pave the way for future advancements in materials science through quantum computing.

On the emerging potential of quantum annealing hardware for combinatorial optimization

Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not provide an irrefutable performance gain over state-of-the-art optimization methods. However, as this hardware continues to evolve, each new iteration brings improved performance and warrants further benchmarking. To that end, this work conducts an optimization performance assessment of D-Wave Systems’ Advantage Performance Update computer, which can natively solve sparse unconstrained quadratic optimization problems with over 5,000 binary decision variables and 40,000 quadratic terms. We demonstrate that classes of contrived problems exist where this quantum annealer can provide run time benefits over a collection of established classical solution methods that represent the current state-of-the-art for benchmarking quantum annealing hardware. Although this work does not present strong evidence of an irrefutable performance benefit for this emerging optimization technology, it does exhibit encouraging progress, signaling the potential impacts on practical optimization tasks in the future.

Madelung mechanics and superoscillations

In single-particle Madelung mechanics, the single-particle quantum state Ψ(x→,t)=R(x→,t)eiS(x→,t)/ℏ is interpreted as comprising an entire conserved fluid of classical point particles, with local density R(x→,t)2 and local momentum ∇→S(x→,t) (where R and S are real). The Schrödinger equation gives rise to the continuity equation for the fluid, and the Hamilton–Jacobi equation for particles of the fluid, which includes an additional density-dependent quantum potential energy term Q(x→,t)=−ℏ22m∇→R(x→,t)R(x→,t) , which is all that makes the fluid behavior nonclassical. In particular, the quantum potential can become negative and create a nonclassical boost in the kinetic energy. This boost is related to superoscillations in the wavefunction, where the local frequency of Ψ exceeds its global band limit. Berry showed that for states of definite energy E, the regions of superoscillation are exactly the regions where Q(x→,t)<0 . For energy superposition states with band-limit E+ , the situation is slightly more complicated, and the bound is no longer Q(x→,t)<0 . However, the fluid model provides a definite local energy for each fluid particle which allows us to define a local band limit for superoscillation, and with this definition, all regions of superoscillation are again regions where Q(x→,t)<0 for general superpositions. An alternative interpretation of these quantities involving a reduced quantum potential is reviewed and advanced, and a parallel discussion of superoscillation in this picture is given. Detailed examples are given which illustrate the role of the quantum potential and superoscillations in a range of scenarios.

On the quantum Guerra–Morato action functional

Given a smooth potential W:Tn→R on the torus, the Quantum Guerra–Morato action functional is given by I(ψ)=∫(DvDv*2(x)−W(x))a(x)2dx, where ψ is described by ψ=aeiuℏ, u=v+v*2, a=ev*−v2ℏ, v, v* are real functions, ∫a2(x)dx = 1, and D is the derivative on x ∈ Tn. It is natural to consider the constraint div(a2Du) = 0, which means flux zero. The a and u obtained from a critical solution (under variations τ) for such action functional, fulfilling such constraints, satisfy the Hamilton-Jacobi equation with a quantum potential. Denote ′ =ddτ. We show that the expression for the second variation of a critical solution is given by ∫a2D[v′] D[(v*)′] dx. Introducing the constraint ∫a2Du dx = V, we also consider later an associated dual eigenvalue problem. From this follows a transport and a kind of eikonal equation.

The Impact of Cloud Computing, Quantum Computing, and Blockchain on Data Security and Business Efficiency

This study explores the impact of cloud computing, quantum computing, and blockchain on data security and business efficiency. The primary objective is to qualitatively analyze the literature to understand how these emerging technologies contribute to enhancing security measures and operational efficiency within businesses. The research employs a qualitative literature review methodology, synthesizing findings from academic articles, industry reports, case studies, and empirical studies to provide a comprehensive overview of current knowledge in this field. The literature review methodology involves systematically collecting and analyzing scholarly sources that discuss various aspects of cloud computing, quantum computing, and blockchain. The study categorizes the literature into key themes, such as the benefits of cloud computing in providing scalable and flexible data storage solutions, the potential of quantum computing to revolutionize encryption and problem-solving capabilities, and the role of blockchain in ensuring transparency and immutability of data transactions. Thematic analysis is used to identify patterns and trends in how these technologies influence data security and business efficiency. The findings indicate that cloud computing enhances business efficiency by enabling seamless data access, reducing IT costs, and improving collaboration. Quantum computing holds promise for significantly advancing data security through quantum encryption, which offers higher levels of security compared to classical encryption methods. Blockchain technology ensures data integrity and transparency, reducing the risk of fraud and enhancing trust in digital transactions.

Quantum Computation of Conical Intersections on a Programmable Superconducting Quantum Processor.

Conical intersections (CIs) are pivotal in many photochemical processes. Traditional quantum chemistry methods, such as the state-average multiconfigurational methods, face computational hurdles in solving the electronic Schrödinger equation within the active space on classical computers. While quantum computing offers a potential solution, its feasibility in studying CIs, particularly on real quantum hardware, remains largely unexplored. Here, we present the first successful realization of a hybrid quantum-classical state-average complete active space self-consistent field method based on the variational quantum eigensolver (VQE-SA-CASSCF) on a superconducting quantum processor. This approach is applied to investigate CIs in two prototypical systems─ethylene (C2H4) and triatomic hydrogen (H3). We illustrate that VQE-SA-CASSCF, coupled with ongoing hardware and algorithmic enhancements, can lead to a correct description of CIs on existing quantum devices. These results lay the groundwork for exploring the potential of quantum computing to study CIs in more complex systems in the future.

Complex Fluid Models of Mixed Quantum–Classical Dynamics

Several methods in nonadiabatic molecular dynamics are based on Madelung’s hydrodynamic description of nuclear motion, while the electronic component is treated as a finite-dimensional quantum system. In this context, the quantum potential leads to severe computational challenges and one often seeks to neglect its contribution, thereby approximating nuclear motion as classical. The resulting model couples classical hydrodynamics for the nuclei to the quantum motion of the electronic component, leading to the structure of a complex fluid system. This type of mixed quantum–classical fluid models has also appeared in solvation dynamics to describe the coupling between liquid solvents and the quantum solute molecule. While these approaches represent a promising direction, their mathematical structure requires a certain care. In some cases, challenging higher-order gradients make these equations hardly tractable. In other cases, these models are based on phase-space formulations that suffer from well-known consistency issues. Here, we present a new complex fluid system that resolves these difficulties. Unlike common approaches, the current system is obtained by applying the fluid closure at the level of the action principle of the original phase-space model. As a result, the system inherits a Hamiltonian structure and retains energy/momentum balance. After discussing some of its structural properties and dynamical invariants, we illustrate the model in the case of pure-dephasing dynamics. We conclude by presenting some invariant planar models.

Relativistic Bohmian mechanics revisited: A covariant reformulation for spin-1/2 particles

We present a novel covariant relativistic guidance formula à la de Broglie-Bohm derived from conserved quantities associated with the Lagrangian of the Dirac equation in a 1+1-dimensional spacetime. In line with the standard guidance formula of Bohmian mechanics, this generalized guidance formula incorporates space-time derivatives of the Dirac spinor phases and consistently reproduces relativistic features such as mass-dependent trajectories and zitterbewegung. We formally show that this formulation explicitly reveals how the relativistic quantum potential affects the trajectories. Using numerical simulations, we compare these trajectories with those presented in the usual approaches. Finally, we show how the guidance formula naturally transitions to the standard de Broglie-Bohm theory in the non-relativistic limit and gives additional contributions in the expansions giving new insight into the zitterbewegung in pilot-wave theories.