Quantum Approach Research Articles

A Quantum Approach to Pattern Recognition and Machine Learning. Part II

A Quantum Approach to Pattern Recognition and Machine Learning. Part II

Rapid quantum approaches for combinatorial optimisation inspired by optimal state-transfer

We propose a new design heuristic to tackle combinatorial optimisation problems, inspired by Hamiltonians for optimal state-transfer. The result is a rapid approximate optimisation algorithm. We provide numerical evidence of the success of this new design heuristic. We find this approach results in a better approximation ratio than the Quantum Approximate Optimisation Algorithm at lowest depth for the majority of problem instances considered, while utilising comparable resources. This opens the door to investigating new approaches for tackling combinatorial optimisation problems, distinct from adiabatic-influenced approaches.

Molecularly Detailed View of Strong Coupling in Supramolecular Plexcitonic Nanohybrids.

Plexcitons constitute a peculiar example of light-matter hybrids (polaritons) originating from the (strong) coupling of plasmonic modes and molecular excitations. Here we propose a fully quantum approach to model plexcitonic systems and test it against existing experiments on peculiar hybrids formed by Au nanoparticles and a well-known porphyrin derivative, involving the Q branch of the organic dye absorption spectrum. Our model extends simpler descriptions of polaritonic systems to account for the multilevel structure of the dyes, spatially varying interactions with a given plasmon mode, and the simultaneous occurrence of plasmon-molecule and intermolecular interactions. By keeping a molecularly detailed view, we were able to gain insights into the local structure and individual contributions to the resulting plexcitons. Our model can be applied to rationalize and predict energy funneling toward specific molecular sites within a plexcitonic assembly, which is highly valuable for designing and controlling chemical transformations in the new polaritonic landscapes.

Quantum Leap: A Price Leap Mechanism in Financial Markets

This study explores the quantum leapfrog mechanism within the context of quantum finance and presents a new interpretation of established financial models through a quantum perspective. In quantum physics, the well-documented phenomenon of particles tunneling through energy barriers has a parallel in finance. We propose a quantum financial leapfrog model in which asset prices make quantum leaps, penetrating market “energy barriers” in non-sequential advances. By leveraging the Hamiltonian operator and the Schrödinger equation, our approach simulates the dynamics of asset prices in a manner akin to the trajectories of particles in quantum mechanics. We draw an analogy between financial markets and gravitational fields, and from this we derive energy equations for pricing orbits. Using path integration techniques, we map out potential price transitions between these orbits, which are guided by the calculation of minimal energy barriers. Furthermore, we introduce a market “propagator” that aligns with the uncertainty principle, identifying the optimal price pathways. Our findings provide new insights and methodologies for navigating the complexities of financial markets, underscoring the significant potential of quantum approaches in the field of finance. These findings have theoretical implications for a variety of market stakeholders, offering strategic guidance and a reference point. We expect that the advancement of the quantum financial leapfrog theory will refine analytical methods and enhance investment strategies in practical financial applications.

Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM)

Early diagnosis of pneumonia is crucial to increase the chances of survival and reduce the recovery time of the patient. Chest X-ray images, the most widely used method in practice, are challenging to classify. Our aim is to develop a machine learning tool that can accurately classify images as belonging to normal or infected individuals. A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. In this study, we offer a comparison between different methods: (1) a classical state-of-the-art implementation of SVM (LibSVM); (2) solving SVM with a classical solver (Gurobi), with and without decomposition; (3) solving SVM with simulated annealing; (4) solving SVM with quantum annealing (D-Wave); and (5) solving SVM using Graver Augmented Multi-seed Algorithm (GAMA). GAMA is tried with several different numbers of Graver elements and a number of seeds using both simulating annealing and quantum annealing. We found that simulated annealing and GAMA (with simulated annealing) are comparable, provide accurate results quickly, competitive with LibSVM, and superior to Gurobi and quantum annealing.

Quantum optimization methods in energy systems

This paper discusses the application of quantum algorithms to the optimization of energy systems, focusing on solving the routing problem in the context of energy. A quantum approach that utilizes the principles of superposition and inversion with respect to the mean to efficiently find optimal energy routing is proposed. Research is carried out to develop a special quantum oracle to represent the structure of energy networks and energy flows between nodes. The application of inversion with respect to the mean provides efficient convergence to optimal solutions in the space of possible routes. We implement the quantum algorithm in the form of a quantum circuit, visualize the results and provide an analysis of the optimal energy routes. The work represents an important step in the development of quantum optimization methods for solving complex problems in the field of energy and may have prospects in practical applications in the future.

Privacy-Preserving Hybrid Federated Learning Framework for Mental Healthcare Applications: Clustered and Quantum Approaches

Privacy-Preserving Hybrid Federated Learning Framework for Mental Healthcare Applications: Clustered and Quantum Approaches

Electron transfer reaction of TEMPO-based organic radical batteries in different solvent environments: comparing quantum and classical approaches.

In this study, we delve into the complex electron transfer reactions associated with the redox-active (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO), a common component in organic radical batteries (ORBs). Our approach estimates quantum electron-transfer (ET) energies using Density Functional Theory (DFT) calculations by sampling from structures simulated classically. This work presents a comparative study of reorganization energies in ET reactions across different solvents. Furthermore, we investigate how changes in the electrolyte environment can modify the reorganization energy and, consequently, impact ET dynamics. We also explore the relationship between classical and quantum vertical energies using linear regression models. Importantly, this comparison between quantum and classical vertical energies underscores the role of quantum effects, like charge delocalization, in offering added stabilization post-redox reactions. These effects are not adequately represented by the classical vertical energy distribution. Our study shows that, although we find a significant correlation between the vertical energies computed by DFT and the classical force field, the regression parameters depend on the solvent, highlighting that classical methods should be benchmarked by DFT before applying them to novel electrolyte materials.

Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation.

We present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixel- wise segmentation problem, which assimilates spectral and spatial information of the image, as a graph-cut optimization task. Our method efficiently leverages the interconnected qubit topology of the D-Wave Advantage device, offering superior scalability over existing quantum approaches and outperforming several tested state-of-the-art classical methods. Empirical evaluations on synthetic datasets have shown that Q-Seg has better runtime performance than the state-of-the-art classical optimizer Gurobi. The method has also been tested on earth observation image segmentation, a critical area with noisy and unreliable annotations. In the era of noisy intermediate-scale quantum, Q-Seg emerges as a reliable contender for real-world applications in comparison to advanced techniques like Segment Anything. Consequently, Q-Seg offers a promising solution using available quantum hardware, especially in situations constrained by limited labeled data and the need for efficient computational runtime.

Modern finance through quantum computing-A systematic literature review.

Human intellectual restlessness originates from the need for knowledge of the modern world. The financial world is struggling to prototype accurate and fast data at low risk. The quantum approach to finance can support this desire. The goal of this paper is to provide a comprehensive review of the literature on how quantum computing can be used in finance. This research aims to expose an architecture of the state of the art in quantum finance. In terms of methodology, the PSALSAR framework was used to conduct this systematic literature review. The selection procedure followed the PRISMA guidelines and was applied in two databases (Web of Science and Scopus) without time limit. In total, 94 out of 1646 articles were included for data extraction and assessment of content evaluation covering the period 2001-2023. The current review of quantum finance literature is structured around the following themes: journals, research methods, tested data series, research topics in quantum finance, and future research directions. Within the financial sector, quantum computing is used in three main areas: simulation, optimization, and machine learning. These areas are supported by algorithms that have been created in recent years. Finally, we propose to highlight the benefits and the applications of quantum finance and to stimulate the interest in the future prospects of the debates.

Spin-polarized currents induced in antiferromagnetic polymer multilayered field-effect transistors.

A theoretical construction of an antiferromagnetic polymer multilayered field-effect transistor with polymers stretched between the source and drain contacts was undertaken. The model employed a quantum approach to the on-chain spin-charge distribution, which was self-consistently coupled with the charge distribution controlled by the gate voltage. Contrary to standard field-effect transistors, we found that the current firstly increased superlinearly with the drain voltage, then it achieved the maximum for drain voltages notably lower than the gate voltage, and after that, it decreased with the drain voltage with no saturation. Such effects were coupled with the formation of the current spin-polarization ratio, where the on-chain mobility of respective spin-polarized charges was significantly dependent on the applied drain voltage. These effects arise from competition among the antiferromagnetic coupling, the intra-site spin-dependent Coulomb interaction, and the applied drain and gate voltages, which strongly influence the on-chain spin-charge distribution, varying from an alternating spin configuration to a spin-polarized configuration at both ends of the chain. Substantial control over the magnitude of spin-polarized currents was achieved by manipulating gate and drain voltages, showcasing the feasibility of practical applications in spintronics.

Towards Quantum Ray Tracing.

Rendering on conventional computers is capable of generating realistic imagery, but the computational complexity of these light transport algorithms is a limiting factor of image synthesis. Quantum computers have the potential to significantly improve rendering performance through reducing the underlying complexity of the algorithms behind light transport. This paper investigates hybrid quantum-classical algorithms for ray tracing, a core component of most rendering techniques. Through a practical implementation of quantum ray tracing in a 3D environment, we show quantum approaches provide a quadratic improvement in query complexity compared to the equivalent classical approach. Based on domain specific knowledge, we then propose algorithms to significantly reduce the computation required for quantum ray tracing through exploiting image space coherence and a principled termination criteria for quantum searching. We show results obtained using a simulator for both Whitted style ray tracing, and for accelerating ray tracing operations when performing classical Monte Carlo integration for area lights and indirect illumination.

Quantum Computing Applications in Statistics

Quantum computing promises to disrupt statistical analysis and data modeling through specialized algorithms and methodologies designed to take advantage of quantum resources. This paper explores imminent applications in areas ranging from sampling and simulation to dimensionality reduction and density estimation. Quantum approaches such as amplitude estimation, quantum principal component analysis, and quantum generative training provide demonstrable speedups and modeling improvements compared to classical techniques. However, despite great future potential, practical implementation today faces restrictions around limited qubit counts, error correction, and developing optimized hybrid quantum-classical workflows. Key near-term applications likely to deliver on quantum advantage include accelerated sampling for uncertainty quantification, quantum-enabled principal component analysis for dimensionality reduction, and quantum machine learning models for fitting complex, multimodal distributions. Longer-term, much more transformational analysis of immense, intricate datasets can be unlocked as quantum hardware progresses towards fault tolerance. Organizations should proactively update statistical pipelines to best leverage coming quantum-centered advancements in data analysis and predictive modeling.

Innovation in Living Labs: A Quantum Approach

Living lab research is a well-accepted stream of innovation management literature. Although previous research has documented living labs from a variety of perspectives, the core of living labs and their principles remain largely underexplored. The present study analyses innovation in living labs inspired by the lens of quantum theory and its key concepts, including superposition, entanglement and wave function collapse. More specifically, the study applies insights from quantum theory to improve our understanding of innovation endeavours in living labs. The framework developed in the study illustrates how and why living labs advance innovations: they enhance collisions of individuals with different backgrounds and knowledge, thereby increasing potential realities (superpositions) and their collapses. The study contributes to living lab literature by suggesting that living labs can be seen as a realisation of quantum computing in real-life environments, speeding up innovation activities. While the study explores conceptual aspects, its findings can offer valuable insights for policy makers and practitioners engaged in living labs.

Photon vortex generation by synchrotron radiation experiments in relativistic quantum approach

Photon vortex generation by synchrotron radiation experiments in relativistic quantum approach

A quantum approach for digital signal processing

A quantum approach for digital signal processing

QUANTUM METAPHYSICS OF FREEDOM

The article shows that the quantum paradigm makes it possible to break the deadlock of the free will problem of Laplacian determinism, since the phase trajectory of a quantum object has ramification points. The key point in the quantum approach is the introduction into consideration of the extraphysical Self, which observes the contents of consciousness, which are a reflection of the information processes occurring in the brain. At the same time, the contents of consciousness are connected both with the actual invironment and with the world of potential capabilities of a living being. It is pointed out that this connection of consciousness with the timeless world of potential possibilities of the Universe which should be considered as the Kantian region of the transcendental and the “chessboard” within which living beings make a free choice of their actions. It is shown that the quantum approach to the problem of freedom and consciousness allows us to explain the non-algorithmic nature of human thinking.

Optimized laser photoionization scheme of separation of complex isotopes in the separator devices

An effective approach to determining parameters of optimal schemes of the method of laser selective photoionization of heavy isotopes, in particular, lanthanides (Gd atoms as the example) with ionization at the final stage by a pulsed electric field, autoionization through the narrow autoionization resonances and due to collisions is developed for separation of heavy isotopes in the separator devices. On the basis of the theory of optimal control and previously developed theoretical quantum approaches (the energy formalism and the relativistic many-body perturbation theory with the Dirac-Kohn-Sham-Sturm zeroth approximation for computing the parameters of elementary atomic processes such as excitation, ionization amplitudes, cross-sections, energies, widths of autoionization resonances etc.) an optimized scheme for the separation of Gd isotopes by the method of 3-stage laser photoionization with ionization at the final stage by a pulsed electric field, ionization through narrow autoionization resonances is presented. For the first time, there are theoretically calculated parameters of the narrow autoionization resonances for Gd in a sufficiently weak electric field, which are in physically reasonable agreement with data of the known experiment by Letokhov et al. Narrow-width autoionization resonances in Gd (in general in spectra of any lanthanide & actinide atom) have a relatively long lifetime, correspondingly, their excitation, ionization cross sections have the same order as the excitation one at the initial stage of separation scheme. Therefore, the use of these long-lived states can ensure the optimal implementation of a laser scheme for the separation of heavy isotopes. The obtained results for Gd with using models of optimal governing confirm the perspective of construction of the optimized schemes of the laser photoionization method with ionization at the final stage by a pulsed electric field, autoionization and allow determining the optimal parameters of the separation scheme, including diagram of atomic transitions, shape of laser pulses, etc. It is hoped that this work will also provide new opportunities for further development of laser chemistry of lanthanides, as well as actinides, taking into account new physics of autoionization resonances in their spectra.

Describing P-Even T-Odd Asymmetries in Nuclear Fission Reactions with the Emission of Low-Mass Particles Using the Quantum Approach

Describing P-Even T-Odd Asymmetries in Nuclear Fission Reactions with the Emission of Low-Mass Particles Using the Quantum Approach

Privacy-Preserving Range Query Quantum Scheme With Single Photons in Edge-Based Internet of Things

Range query in cloud-based outsourcing applications is an important data search service, but it can suffer from privacy disclosure. In this paper, we generalize and redefine privacy-preserving range query in edge-based Internet of Things (IoT). Furthermore, to enhance security and privacy of sensitive data, we introduce quantum cryptographic technologies and present a novel quantum approach to solve the problem. First, we present a feasible protocol for Quantum Secret Permutating (QSP) with single photons, which will be later utilized in the final scheme as a basic block. Second, we generalize traditional privacy-preserving range query to a secure one-sided two-party computation, i.e., specific dot product. Third, we present a synchronously grouping strategy based on Quantum Key Distribution (QKD) to reduce the complexity and improve the efficiency of solving this two-party computation, so that it can easily get the desired result by repeatedly using feasible quantum privacy query group by group. Finally, adopting our previously proposed quantum protocols, we design privacy-preserving range query quantum scheme in edge-based IoT. Compared with the classical related schemes, our proposed quantum scheme has higher security, because the security of our proposed protocols is based on the basic physical principles of quantum mechanics, instead of unproven computational difficulty assumptions.