Principles Of Quantum Mechanics Research Articles

Optimizing residential flexibility for sustainable energy management in distribution networks

In search of a sustainable and green economy, many initiatives have been undertaken to promote clean energy and enhance local flexibility. Residential flexibility, achieved through home appliances capable of adjusting their consumption profiles, offers a feasible solution for operators to address challenges such as congestion and balancing in distribution systems. This paper considered an improved approach for aggregators to provide flexibility in distribution systems. By leveraging load flexibility resources, the model facilitates the rescheduling of real-time and shifting appliances to meet the demands of Balance Responsible Parties (BRPs) or Distribution System Operators (DSOs). This study uses a number of approaches to solve the recommended model effectively despite the problem's inherent complexity. An extensive test case with twenty residential houses equipped with seven types of appliances each is run in order to confirm and compare the optimization algorithms' performance. The results show that by rescheduling home appliance loads across 24 hours, the aggregator may effectively accommodate flexibility requests from DSOs/BRPs while optimizing the expenses associated with user compensation. To further improve the optimization process, this study uses a new Reinforced Learning Quantum Inspired Grey Wolf Optimization (RLQIGWO). Through the integration of reinforcement learning and quantum mechanics principles into the original grey wolf optimizer, RLQIGWO achieves better performance in load balancing, resource utilization, and execution of tasks. The findings demonstrate that the proposed RLQIGWO improves the efficacy and competence of flexibility options in distribution networks, paving the way to a more adaptable and strong energy management strategy.

Will Quantum Medicine be a Novel Model in the Future?

Quantum medicine, a cutting-edge field at the intersection of quantum physics and healthcare, holds immense promise for revolutionizing the future of medical practice. By harnessing the principles of quantum mechanics, this novel approach seeks to explore the profound connection between energy, consciousness, and healing. In this article, we delve into the foundations of quantum medicine, its applications in diagnosis and treatment, the transformative potential of quantum healing modalities, and the challenges and opportunities in integrating quantum concepts into conventional healthcare systems. Join us on a journey to uncover the paradigm-shifting implications of quantum medicine in shaping the landscape of healthcare delivery and patient well-being.

Cooperative terahertz quantum key distribution: Secret key rate analysis and optimization

In the recent years, there has been a growing interest in quantum key distribution (QKD) as a promising alternative to conventional cryptographic methods. QKD offers potential for ensuring absolute security in communication networks, leveraging the principles of quantum mechanics. This study diverges from previous research by investigating a cooperative continuous variable QKD (CVQKD) system operating at terahertz (THz) frequencies with multiple input multiple output (MIMO) technology, wherein the source and destination are assisted by a trusted decode-and-forward (DF) relay. Our focus lies on evaluating the secret key rate (SKR) of this system under direct reconciliation conditions and subsequently optimizing power and relay location to maximize the SKR. We address the practical concern of potential eavesdropping between the relay and the destination. Specifically, our analysis centers on the SKR performance of the coherent state-based CVQKD protocol under direct reconciliation conditions. Through numerical simulations, we demonstrate the significant enhancement in SKR achievable through optimization in the cooperative QKD system, yielding several noteworthy insights.

Battle of the CH motions: aliphatic vs. aromatic contributions to astronomical PAH emission and exploration of the aliphatic, aromatic, and ethynyl CH stretches

Abstract Strong anharmonic coupling between vibrational states in polycyclic aromatic hydrocarbons (PAH) produces highly mixed vibrational transitions that challenge the current understanding of the nature of the astronomical mid-infrared PAH emission bands. Traditionally, PAH emission bands have been characterized as either aromatic or aliphatic, and this assignment is used to determine the fraction of aliphatic carbon in astronomical sources. In reality, each of the transitions previously utilized for such an attribution is highly mixed with contributions from both aliphatic and aromatic CH motions as well as non-CH motions such as CC stretches. High-resolution gas-phase IR absorption measurements of the spectra of the aromatic molecules indene and 2-ethynyltoluene at the Canadian Light Source combined with high-level anharmonic quantum chemical computations reveal the complex nature of these transitions, implying that the use of these features as a marker for the aliphatic fraction in astronomical sources is not uniquely true or actually predictive. Further, the presence of aliphatic, aromatic, and ethynyl CH groups in 2-ethynyltoluene provides an internally consistent opportunity to simultaneously study the spectroscopy of all three astronomically important groups. Finally, this study makes an explicit connection between fundamental quantum mechanical principles and macroscopic astronomical chemical physics, an important link necessary to untangle the lifecycle of stellar and planetary systems.

Exploration and Development of Quantum Computing Algorithms for Optimization, Cryptography, and Machine Learning Applications

Quantum computing, with its potential to revolutionize computation, relies fundamentally on the development of efficient algorithms to leverage its unparalleled processing capabilities. This research delves into the creation, exploration, and refinement of quantum computing algorithms, focusing on their applications in optimization, cryptography, and machine learning. Quantum algorithms such as Shor's and Grover's have demonstrated remarkable advantages in factorization and search problems, while more recent innovations are tackling complex challenges in global optimization and data analysis. By integrating principles of quantum mechanics—such as superposition, entanglement, and interference—these algorithms promise exponential speed-ups over classical counterparts in specific domains. This study critically analyzes existing quantum algorithms, proposes advancements in hybrid quantum-classical frameworks, and explores their implications for practical problem-solving. Through simulations and theoretical evaluations, it aims to bridge the gap between quantum theory and real-world applications, contributing to the evolution of quantum computing as a transformative technology.

Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions

The Quantum Marine Predator Algorithm (QMPA) presents a groundbreaking solution to the inherent limitations of conventional Maximum Power Point Tracking (MPPT) techniques in photovoltaic systems. These limitations, such as sluggish response times and inadequate adaptability to environmental fluctuations, are particularly pronounced in regions with challenging weather patterns like Sunderland. QMPA emerges as a formidable contender by seamlessly integrating the sophisticated hunting tactics of marine predators with the principles of quantum mechanics. This amalgamation not only enhances operational efficiency but also addresses the need for real-time adaptability. One of the most striking advantages of QMPA is its remarkable improvement in response time and adaptability. Compared to traditional MPPT methods, which often struggle to keep pace with rapidly changing environmental factors, QMPA demonstrates a significant reduction in response time, resulting in up to a 30% increase in efficiency under fluctuating irradiance conditions for a resistive load of 100 Ω. These findings are derived from extensive experimentation using NASA’s worldwide power prediction data. Through a detailed comparative analysis with existing MPPT methodologies, QMPA consistently outperforms its counterparts, exhibiting superior operational efficiency and stability across varying environmental scenarios. By substantiating its claims with concrete data and measurable improvements, this research transcends generic assertions and establishes QMPA as a tangible advancement in MPPT technology.

Unsupervised representation learning of Kohn–Sham states and consequences for downstream predictions of many-body effects

Representation learning for the electronic structure problem is a major challenge of machine learning in computational condensed matter and materials physics. Within quantum mechanical first principles approaches, density functional theory (DFT) is the preeminent tool for understanding electronic structure, and the high-dimensional DFT wavefunctions serve as building blocks for downstream calculations of correlated many-body excitations and related physical observables. Here, we use variational autoencoders (VAE) for the unsupervised learning of DFT wavefunctions and show that these wavefunctions lie in a low-dimensional manifold within latent space. Our model autonomously determines the optimal representation of the electronic structure, avoiding limitations due to manual feature engineering. To demonstrate the utility of the latent space representation of the DFT wavefunction, we use it for the supervised training of neural networks (NN) for downstream prediction of quasiparticle bandstructures within the GW formalism. The GW prediction achieves a low error of 0.11 eV for a combined test set of two-dimensional metals and semiconductors, suggesting that the latent space representation captures key physical information from the original data. Finally, we explore the generative ability and interpretability of the VAE representation.

Guest Editorial: Quantum Computing: A Beacon of Transformation for the Oil and Gas Industry

This past June, the United Nations proclaimed 2025 as the International Year of Quantum Science and Technology to recognize the potential of quantum science to drive innovations in sustainable development and global communications. It is also a recognition of how far the theoretical concept of quantum computing has come to become a reality, and the race to build a powerful quantum computer is accelerating every year. According to a recent report from Global Quantum Intelligence, the impact of quantum tech across the oil and gas industry could equate to $2.6 trillion by 2035. As the first chief data scientist in the oil and gas industry, I am acutely aware of the myriad challenges that our industry faces. I firmly believe that quantum computing and related quantum technologies are on the brink of revolutionizing our industry in ways we have yet to fully comprehend. The quantum revolution demands that the oil and gas and the broader energy industry rethink their approach to exponential technology and sustainability. The industry's history of adopting emerging technologies, as evidenced by the slow uptake of digital transformation, underscores the urgency of this shift. Even 30 years after the introduction of the digital oilfield concept, the industry lags in data quality, addressing data silos, and data democratization, as compared with other complex and highly regulated industries. The urgency of an energy transition towards a sustainable future necessitates the oil and gas industry to confront its complex challenges, which are beyond the scope of current computing power. Quantum computing, with its unparalleled computational power, emerges as one of the most promising technologies to optimize and accelerate solutions to the industry's intricate problems. As we discuss quantum computing in detail, we will explore how related quantum interconnected areas—quantum sensing, quantum networking, and quantum simulations—will enable unique applications for the oil and gas industry. In simple words, quantum computing is the field of computing that uses the principles of quantum mechanics to solve complex problems that are intractable for classical computers. The three main principles of quantum mechanics that are the cornerstone of quantum computing are superposition, entanglement, and quantum interference. Unlike traditional computing, where a bit (0 or 1) is the basic unit, a qubit is the unit of quantum computing. A qubit can exist in a state of 0, 1, or both simultaneously due to superposition, enabling parallel computations and the exploration of multiple solutions at once. Entanglement allows for correlated states, enabling operations on multiple qubits simultaneously for enhanced computational power. Quantum interference allows quantum states to interfere constructively or destructively with each other, amplifying correct solutions and suppressing incorrect ones, thereby empowering efficient computations. Thus, a significant quantum advantage is gained by using quantum computing for certain computationally challenging problems compared with traditional computing. Quantum computing will be performed on quantum computers, which are developed today using various technologies such as photonics, light particles, trapped ions, neutral atom traps, superconducting qubits, and controlling nitrogen vacancy centers in imperfect diamonds.

Quantum Entanglement and Qubit Interactions: The Key to Quantum Supremacy

Abstract. Quantum computing operates in a fundamentally different way from classical computing by harnessing the principles of quantum mechanics to process information. Quantum supremacy is achieved when a quantum computer can solve problems that are beyond the capabilities of classical systems, including the human brain, showcasing its superior processing power. To attain quantum supremacy, quantum entanglement and qubit interactions play a pivotal role. Quantum entanglement occurs when qubits are interconnected in a manner where the state of one qubit directly influences the state of others, enabling the quantum computer to perform multiple operations simultaneously. Moreover, effective interaction between qubits is essential for the performance of complex calculations in quantum systems, highlighting the significance of coherence and error correction. Understanding the importance of coherence in preventing and rectifying errors in quantum computations is crucial. This paper aims to explore the critical aspects of quantum entanglement and qubit interactions, which are foundational to the operation of quantum computers. By delving into these key concepts, the paper aims to elucidate their significant roles in achieving quantum supremacy. The discussion will center on how quantum entanglement, which allows enhanced computational parallelism through qubit interconnection, and efficient qubit interactions vital for complex computations, contribute to surpassing the capabilities of classical computers. Comprehending these principles is crucial for advancing quantum computing technology and overcoming the challenges to unleash its full potential.

Superposition of standing waves induced ultra high-resolution atom localization

Abstract In this study, we have theoretically demonstrated ultra-high resolution two-dimensional atom localization within a three-level $\lambda$-type atomic medium via superposition of asymmetric and symmetric standing wave fields. Our analysis provides understanding into the precise spatial localization of atomic positions at the atomic level, utilizing advanced theoretical approaches and principles of quantum mechanics. The dynamical behavior of a three-level atomic system is thoroughly analyzed using the density matrix formalism within the realm of quantum mechanics. A theoretical approach is constructed to describe the interaction between the system and external fields, specifically a control field and a probe field. The absorption spectrum of the probe field is thoroughly examined to clarify the spatial
localization of the atom within the proposed configuration. We have theoretically investigated that symmetric and asymmetric superposition phenomena significantly influence the localized peaks within a two-dimensional spatial domain. Specifically, the emergence of one and two sharp localized peaks has been observed within a region of one wavelength domain. We observed notable influences of the intensity of the control field, probe field detuning, and decay rates on atom localization. Ultimately, we have achieved an unprecedented level of ultra-high resolution and precision in localizing an atom within an area smaller than λ/35×λ/35. These findings hold promise for potential applications in domains such as Bose-Einstein condensation, nanolithography, laser cooling, trapping of neutral atoms, and the measurement of center-of-mass wave-functions.

Spin-bearing molecules as optically addressable platforms for quantum technologies

Abstract Efforts to harness quantum hardware relying on quantum mechanical principles have been steadily progressing. The search for novel material platforms that could spur the progress by providing new functionalities for solving the outstanding technological problems is however still active. Any physical property presenting two distinct energy states that can be found in a long-lived superposition state can serve as a quantum bit (qubit), the basic information processing unit in quantum technologies. Molecular systems that can feature electron and/or nuclear spin states together with optical transitions are one of the material platforms that can serve as optically addressable qubits. The attractiveness of molecular systems for quantum technologies relies on the fact that molecular structures of atomically defined nature can be obtained in endless diversity of chemical compositions. Crucially, by harnessing the molecular design protocols, the optical and spin (electronic and nuclear) properties of molecules can be tailored, aiding the design of optically addressable spin qubits and quantum sensors. In this contribution, we present a concise and collective discussion of optically addressable spin-bearing molecules – namely, organic molecules, transition metal (TM) and rare-earth ion (REI) complexes – and highlight recent results such as chemical tuning of optical and electron spin quantum coherence, optical spin initialization and readout, intramolecular quantum teleportation, optical coherent storage, and photonic-enhanced optical addressing. We envision that optically addressable spin-carrying molecules could become a scalable building block of quantum hardware for applications in the fields of quantum sensing, quantum communication and quantum computing.

High extinction ratio multi-polarization states preparation based on SOI integrated chips

Quantum key distribution (QKD) leverages the principles of quantum mechanics to provide an unconditionally secure public-key cryptographic system. Polarization encoding is a commonly employed method in QKD. However, the low extinction ratio of polarization states significantly impacts the error rate in QKD systems, thereby threatening the security of communication. Here, we propose a structure and method for the preparation of high-extinction-ratio polarization states based on silicon photonic integrated chips. Our chip integrates thermo-optic modulators and silicon carrier-depletion modulators to achieve the transformation of information from path encoding to polarization encoding through a 2-D grating. We have successfully prepared six polarization states using only thermo-optic modulators, with an average polarization extinction ratio of 35.8 dB. Furthermore, we have successfully prepared six polarization states using a combination of thermo-optic and electro-optic modulators, with an average polarization extinction ratio exceeding 30.6 dB. Notably, our system demonstrated remarkable stability over a 2-h testing period, with the polarization extinction ratio remaining essentially unchanged.

Emulating quantum computing with optical matrix multiplication

Optical computing harnesses the speed of light to perform vector-matrix operations efficiently. It leverages interference, a cornerstone of quantum computing algorithms, to enable parallel computations. In this work, we interweave quantum computing with classical structured light by formulating the process of photonic matrix multiplication using quantum mechanical principles such as state superposition and subsequently demonstrate a well-known algorithm, namely, Deutsch–Jozsa’s algorithm. This is accomplished by elucidating the inherent tensor product structure within the Cartesian transverse degrees of freedom of light, which is the main resource for optical vector-matrix multiplication. To this end, we establish a discrete basis using localized Gaussian modes arranged in a lattice formation and demonstrate the operation of a Hadamard gate. Leveraging the reprogrammable and digital capabilities of spatial light modulators, coupled with Fourier transforms by lenses, our approach proves adaptable to various algorithms. Therefore, our work advances the use of structured light for quantum information processing.

An Improved Quantum Particle Swarm Optimization Algorithm for Target Tracking Deployment in Spatial Sensor Networks

This paper presents a comprehensive review of the current research on spatial sensor networks and the node deployment methods employed for mobile target tracking. The study introduces particle swarm optimization (PSO) and its quantum behavior extension, detailing concepts such as the quantum state wave function and particle position representation. Subsequently, an improved Quantum Particle Swarm Optimization (QPSO) algorithm is proposed. This enhanced algorithm increases population diversity by incorporating quantum rotation gates and quantum mutation mechanisms, expands the search space through the superposition state and interference principles of quantum mechanics, and dynamically adjusts algorithm parameters to balance global exploration and local search. These modifications aim to improve both the convergence speed and accuracy of the algorithm. Simulation results demonstrate that the improved QPSO algorithm surpasses traditional mobile tracking deployment algorithms and the standard quantum behavior particle swarm optimization algorithm in terms of target tracking deployment within spatial sensor networks. Notably, it significantly enhances the tracking success rate and reduces tracking errors.

Harmonic analysis approach to the proof of Heisenberg inequality

the Heisenberg uncertainty Principle is a fundamental principle in quantum mechanics, which was developed by the German physicist Werner Heisenberg and was proposed by him in 1927. This principle states that for a pair of physical quantities that share phase space, such as position and momentum, it is impossible to accurately measure their values at the same time. There are several variants of it in harmonic analysis studies, and the article will introduce some of them in R^1 space and L^2 space. In the process of providing the Heisenberg inequality, the article proved the Plancherel identity and Schwartz inequality by using Fourier transform and inverse Fourier transform. Finally, author solved the equation of the wave function (x) . The famous physicists Heisenberg proposed one of the more novel ideas in quantum mechanics the existence of unobservable orbits cannot be assumed, which did bring great influence in quantum mechanics. The article will introduce the conception of Heisenberg inequality and try to finish the proof.

Quantum-based PRNG for image encryption: a comprehensive study

Quantum computing, grounded in the principles of quantum mechanics, has ushered in a new era of intriguing possibilities and fresh scientific insights. In this document, we present an innovative algorithm tailored for the encryption and decryption of images. This algorithm harnesses the quasi-probability values extracted from each state within the quantum system, represented as | ψ 〉 . These numerical values are seamlessly integrated with conventional data to facilitate image encryption and decryption processes. Our methodology undergoes comprehensive validation and evaluation through extensive simulations performed on the IBM Qiskit Aer simulator. We subject the generated numerical sequence to rigorous statistical scrutiny using the NIST SP 800-22 suite, ensuring its authenticity and reliability. Employing a quantum system comprising 3 qubits and uniform rotation gates, our algorithm produces a dataset consisting of 41,943,040 binary digits. Additionally, we conduct a thorough assessment encompassing both visual and statistical analyses to ascertain the effectiveness and robustness of our proposed algorithm. Furthermore, we emphasize the critical importance of achieving a quasi-probability error rate of 10 − 6 for real-world implementation, highlighting the practical viability and scalability of our approach. Through comparative analysis and meticulous examination of correlation patterns, our study provides invaluable insights into the distinctive characteristics and efficacy of our encryption scheme.

Architecture decisions in quantum software systems: An empirical study on Stack Exchange and GitHub

Context:Quantum computing provides a new dimension in computation, utilizing the principles of quantum mechanics to potentially solve complex problems that are currently intractable for classical computers. However, little research has been conducted about the architecture decisions made in quantum software development, which have a significant influence on the functionality, performance, scalability, and reliability of these systems. Objective:The study aims to empirically investigate and analyze architecture decisions made during the development of quantum software systems, identifying prevalent challenges and limitations by using the posts and issues from Stack Exchange and GitHub. Methods:We used a qualitative approach to analyze the obtained data from Stack Exchange Sites and GitHub projects — two prominent platforms in the software development community. Specifically, we collected data from 385 issues (from 87 GitHub projects) and 70 posts (from 3 Stack Exchange sites) related to architecture decisions in quantum software development. Results:The results show that in quantum software development (1) architecture decisions are articulated in six linguistic patterns, the most common of which are Solution Proposal and Information Giving, (2) the two major categories of architectural decisions are Implementation Decision and Technology Decision, (3) Software Development Tools are the most common application domain among the twenty application domains identified, (4) Maintainability is the most frequently considered quality attribute, and (5) Design Issues and High Error Rates are the major limitations and challenges that practitioners face when making architecture decisions in quantum software development. Conclusions:Our results show that the limitations and challenges encountered in architecture decision-making during the development of quantum software systems are strongly linked to the particular features (e.g., quantum entanglement, superposition, and decoherence) of those systems. These issues mostly pertain to technical aspects and need appropriate measures to address them effectively.

A Mathematical Modelling for Three-Factor Quantum Biometric Authentication Using Machine Learning, Double-Layer Encryption, Cryptographic Algorithms

Traditional authentication mechanisms are vulnerable to sophisticated attacks in the ever-changing cybersecurity paradigms. In this paper, a new three-factor quantum biometric system is proposed that were analysed to increase security level and reduce threats. This model employs multiple strategies, like quantum key distribution, a biometric identification system and machine learning which encrypts data based on their unique properties in an innovative way to provide not only one but two layers of encryption from unauthorized access. The system in its essence combines three critical elements: quantum generated cryptographic keys, biometric data (fingerprint or retinal scans) and dynamic behaviour-based identification using machine learning algorithms. By adding QKD to the hardware, an encryption key that should be practically impossible for anyone else (except sender and receiver) to intercept is securely generated and distributed through quantum mechanics principles of detection preventing possible eavesdropper. Secondly, the biometric data means that an imposter would have to not only steal your smartphone but also be able to authenticate themselves using unique physiology which takes even more of the likelihood out. Machine learning models study user behaviour patterns and work to adapt against evolving threats, gradually improving the accuracy of authentication with time. For added security, the system uses a double-layer encryption routine. The newly proposed system has a two-level encryption, and in the first layer, quantum keys encrypted bio-metric data while classical algorithms are applied to encrypt overall communication as well storage process. Besides ensuring the safety of private biometric information through dual encryption, this added redundancy also allows them to function as extra fail safe so that if one layer is breached, the entire system will still stand uncompromised. We evaluate the implementation of this new authentication scheme to a series-simulations and real-world test, showing the performance in different scenarios including high threat level environments. The results show major security enhancement compared to the traditional implementation of authentication methods with an observed amount decrease in data breaches and unsanctioned access attempts.

A secure dynamic quantum anonymous secret sharing protocol utilizing GHZ states

Quantum secret sharing enables participants to share secrets grounded in the principles of quantum mechanics, ensuring the secrets recovery solely through collaborative efforts within an authorized subset of participants. Quantum anonymous secret sharing fulfills the fundamental requirements of quantum secret sharing while also ensuring the anonymity of the secret receivers. In order to address the turnover of personnel in practical scenarios, this paper propose a secure dynamic quantum anonymous secret sharing protocol utilizing Greenberger-Horne-Zeilinger states. In our scheme, on the premise of not reveal the identities of participants, the dynamic update of the participants can be realized, and the shared secret will not be altered. Furthermore, an identity authentication mechanism using single particles is introduced in this protocol, ensuring that only authenticated participants can engage in the sharing process. The proposed protocol is secure and can resist both internal and external attacks. Experimental validation conducted on the IBM quantum computing platform demonstrates the feasibility of our scheme.

Near-space radiation environment analysis and protective measures

As a matter of fact, near space radiation environment analysis plays a crucial role in near space physics. With this in mind, this study introduces the types of radiation damage in the near-space radiation environment as well as corresponding protective measures. To be specific, the protective strategy for Total Ionizing Dose (TID) and for Displacement Damage (DD) are similar, focusing on material shielding to mitigate the effects. According to the analysis, the guiding principle for the protection strategy against Single Event Effects (SEE) is risk management, aiming to minimize the probability of catastrophic SEE and to detect and mitigate the impact of non-destructive SEE. Based on the analysis, some measures are proposed accordingly. In fact, current strategies for future radiation protection may involve the use of biological membranes to absorb radiation or the application of quantum mechanics principles to eliminate the effects brought about by radiation. These results shed light on guiding further exploration if near space radiation.