Quantum Mechanical Properties Research Articles

Spin and orbital angular momentum of coherent photons in a waveguide

Spin angular momentum of a photon corresponds to a polarisation degree of freedom of lights, and such that various polarisation properties are coming from macroscopic manifestation of quantum-mechanical properties of lights. An orbital degree of freedom of lights is also manipulated to form a vortex of lights with orbital angular momentum, which is also quantised. However, it is considered that spin and orbital angular momentum of a photon cannot be split from the total orbital angular momentum in a gauge-invariant way. Here, we revisit this issue for a coherent monochromatic ray from a laser source, propagating in a waveguide. We obtained the helical components of spin and orbital angular momentum by the correspondence with the classical Ponyting vector. By applying a standard quantum field theory using a coherent state, we obtained the gauge-independent expressions of spin and orbital angular momentum operators. During the derivations, it was essential to take a finite cross-sectional area into account, which leads the finite longitudinal component along the direction of the propagation, which allows the splitting. Therefore, the finite mode profile was responsible to justify the splitting, which was not possible as far as we were using plane-wave expansions in a standard theory of quantum-electrodynamics (QED). Our results suggest spin and orbital angular momentum are well-defined quantum-mechanical freedoms at least for coherent photons propagating in a waveguide and in a vacuum with a finite mode profile.

Quantum Technologies and the Engineering of Josephson JunctionBased Sensors: Disruptive Innovation as a Strategic Differential for National Security and Defense

The so-called area of quantum technologies is a new and disruptive technological field, essentially from physics and engineering, with the ability to affect almost all human activities. They are based on the properties of quantum mechanics like quantum tunneling, quantum entanglement, and quantum superposition. Examples of areas of application of these emerging technologies are quantum computing, quantum communications, quantum sensing, quantum cryptography, quantum internet, quantum imaging, quantum metrology, quantum biology, among others. These technologies are of the interest of both the defense and security industry, and military and governmental actors. For military applications, and after a strong advanced engineering process, these technologies introduce new capabilities, improving effectiveness and increasing precision, thus leading to the so-called quantum warfare, wherein new military strategies, doctrines, policies, and ethics should be established. In particular, these military applications of quantum technologies can be described for various warfare domains like land, air, space, electronic, cyber, and underwater warfare and also for intelligence, surveillance, target acquisition and reconnaissance. Among those applications mentioned before, quantum computing is expected to have a number of important uses such as optimization and machine learning, associated to both artificial intelligence and blockchain. Quantum computers are perhaps best known for their expected ability to carry out Shor’s algorithm, which can be used to factorize large numbers and is an important process in the securing of data transmissions, essential for military applications. In this review of our own work, we show the physics and some of the applications of the Josephson junction which plays and important and essential role in the fabrication of quantum computers with superconducting qubits. Nowadays, applications of Josephson junction devices go from the most sensitive sensor to measure magnetic flux, called S.QU.I.D. (from Superconducting Quantum Interference Device) to the core of quantum computers, the quantum bits. The Josephson junction is an old quantum engine for a rising new world with incredible disruptive technological possibilities in engineering, especially in military applications where it plays a fundamental strategic differential for National Security and Defense.

APDB: a database onair pollutant characterization andsimilarity prediction.

The World Health Organization estimates that 9 out of 10 people worldwide breathe air containing high levels of pollutants. Long-term and chronic exposure to high concentrations of air pollutants is associated with deleterious effects on vital organs, including increased inflammation in the lungs, oxidative stress in the heart and disruption of the blood-brain barrier. For this reason, in an effort to find an association between exposure to pollutants and the toxicological effects observable on human health, an online resource collecting and characterizing in detail pollutant molecules could be helpful to investigate their properties and mechanisms of action. We developed a database, APDB, collecting air-pollutant-related data from different online resources, in particular, molecules from the US Environmental Protection Agency, their associated targets and bioassays found in the PubChem chemical repository and their computed molecular descriptors and quantum mechanics properties. A web interface allows (i) to browse data by category, (ii) to navigate the database by querying molecules and targets and (iii) to visualize and download molecule and target structures as well as computed descriptors and similarities. The desired data can be freely exported in textual/tabular format and the whole database in SQL format. Database URL http://apdb.di.univr.it.

Generative discovery of de novo chemical designs using diffusion modeling and transformer deep neural networks with application to deep eutectic solvents

We report a series of deep learning models to solve complex forward and inverse design problems in molecular modeling and design. Using both diffusion models inspired by nonequilibrium thermodynamics and attention-based transformer architectures, we demonstrate a flexible framework to capture complex chemical structures. First trained on the Quantum Machines 9 (QM9) dataset and a series of quantum mechanical properties (e.g., homo, lumo, free energy, and heat capacity), we then generalize the model to study and design key properties of deep eutectic solvents (DESs). In addition to separate forward and inverse models, we also report an integrated fully prompt-based multi-task generative pretrained transformer model that solves multiple forward, inverse design, and prediction tasks, flexibly and within one model. We show that the multi-task generative model has the overall best performance and allows for flexible integration of multiple objectives, within one model, and for distinct chemistries, suggesting that synergies emerge during training of this large language model. Trained jointly in tasks related to the QM9 dataset and DESs, the model can predict various quantum mechanical properties and critical properties to achieve deep eutectic solvent behavior. Several combinations of DESs are proposed based on this framework.

Quantum-mechanical properties of long-lived optical pulses in the fourth-order KdV-type hierarchy nonlinear model

Quantum-mechanical properties of long-lived optical pulses in the fourth-order KdV-type hierarchy nonlinear model

Effects of Quantum Mechanical Properties of Complex Systems on Advanced Technological Applications in Japan

Purpose: This study investigates the effects of quantum mechanical properties of complex systems on advanced technological applications in Japan.
 Materials and Methods: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low-cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
 Results: The literature review and research findings highlight the significant effects of quantum mechanical properties of complex systems on various technological applications in Japan. The study reveals that quantum entanglement, quantum coherence, and quantum interference can be exploited for quantum computing, quantum communication, quantum sensing, quantum cryptography, quantum metrology, and other advanced technologies. The study also discusses the challenges and limitations of utilizing quantum mechanical properties in technological applications, such as decoherence, environmental interactions, and technical feasibility.
 Recommendations: This study offers a unique contribution to understanding the effects of quantum mechanical properties of complex systems on advanced technological applications in Japan. The research advances the knowledge in the field of quantum information science, quantum technology, and interdisciplinary research between quantum mechanics and technology. The findings provide insights into the potential applications of quantum mechanical properties in various technological fields, and their implications for theory, practice, and policy. The study has practical implications for researchers, engineers, policymakers, and industries involved in quantum technology research and development in Japan and beyond, guiding the future direction of quantum technology innovation and policy formulation

Simulating quantum key distribution in fiber-based quantum networks

Quantum networks exploit the unique properties of quantum mechanics to enable communication and networking tasks unavailable to existing distributed classical systems. Recently, the research community has focused considerable effort on the simulation of large-scale quantum networks with the ultimate goal of understanding their general properties, developing technical standards, and estimating their expected performance. However, comparatively little effort has been spent considering how quantum networks may impact tactical scenarios of military relevance where both quantum and classical resources may be severely constrained. Here, we develop a custom framework, called QuanTACT, for quantum network simulation explicitly designed for future integration into existing tactical simulation tools. In particular, our framework extends the existing quantum networking tool, SQUANCH, to include channel models required to simulate deployed fiber environments. Furthermore, we implement the additional subroutines needed to simulate entanglement-based quantum key distribution (QKD) and use published results from various field-deployed QKD experiments to benchmark the performance of our framework.

Coherent-Phonon-Driven Hot-Carrier Effect in a Superlattice Solar Cell

Carrier thermalization in a superlattice solar cell made of polar semiconductors is studied theoretically by considering a minimal model where electron-phonon scattering is the principal channel of carrier energy loss. Importantly, the effect of an intrinsic quantum mechanical property; the phonon coherence, on carrier thermalization is investigated, within semiclassical picture in terms of phonon wave packet. It turns out that coherent longitudinal optical (LO) phonons weaken the effective electron-phonon coupling, thus supposedly lowering the carrier energy loss rate in solar cell. The resulting thermalization power is indeed significantly reduced by the coherent phonons, resulting in enhanced hot carrier effect, particularly for thin enough well layer where carrier confinement is also strong. A recent experiment on superlattice solar cell prototype is shown to manifest the coherent phonons-driven phenomenon. Our results demonstrate the practical implications of the fundamental quantum coherence property of phonons in semiconductors for improving superlattice solar cell performance, via hot carrier effect.

First-principles calculations for structural, elastic, electronic, and phonon properties of topological WC-type TMO (TM = Ti, Zr and Hf; O) family

Through first-principles calculation, we have explored the first three (TiO, ZrO, and HfO) WC-type topological semimetal compounds in transition metal oxides and studied their structural, thermodynamic, elastic, electronic, and dynamical properties systematically. Among these three compounds, only TiO has been experimentally reported as a WC-type, and the other two, ZrO and HfO, are supposed to be WC-type materials. Our density functional theory derived results confirmed that these three compounds are structurally, thermodynamically, mechanically, and dynamically stable, which means they can be easily synthesized. The R. Hill approximation is used to calculate the actual bulk and shear modulus of the single crystal phase with the help of resultant elastic constants and the bulk and shear modulus of the W. Voigt and A. Reuss schemes. Among these compounds, TiO has a high bulk, shear modulus, and low passion ratio, which are directly linked to the high hardness of this compound. Furthermore, TiO is quite an unusual candidate due to the co-existence of 3-fold fundamental particles (3-fold fermions and 3-fold bosons) in the electronic band structure and phonon dispersion along the same Γ-A direction in the Brillouin zone, respectively, providing a perfect platform to explore the probable multi-functional quantum-mechanical properties of these two fundamental particles in a single crystal at the atomistic level. The spin-orbital coupling effect is ignored in TiO due to the light atomic masses of Ti and O elements, and their electronic and topological properties have been more studied.

Quantum asymmetric key crypto scheme using Grover iteration

Here, we propose a quantum asymmetric key cryptography scheme using Grover’s quantum search algorithm. In the proposed scheme, Alice generates a pair of public and private keys, keeps the private keys safe, and only discloses public keys to the outside. Bob uses Alice's public key to send a secret message to Alice and Alice uses her private key to decrypt the secret message. Furthermore, we discuss the safety of quantum asymmetric key encryption techniques based on quantum mechanical properties.

Terpenic Constituents of Essential Oils with Larvicidal Activity against Aedes Aegypti: A QSAR and Docking Molecular Study.

Aedes aegypti is a vector for the arbovirus responsible for yellow fever, Zika and Chikungunya virus. Essential oils and their constituents are known for their larvicidal properties and are strong candidates for mosquito control. This work aimed to develop a quantitative structure-activity study and molecular screening for the search and design of new larvicidal agents. Twenty-five monoterpenes with previously evaluated larvicidal activity were built and optimized using computational tools. QSAR models were constructed through genetic algorithms from the larvicidal activity and the calculation of theoretical descriptors for each molecule. Docking studies on acetylcholinesterase (AChE) and sterol carrier protein (SCP-2) were also carried out. Results demonstrate that the epoxide groups in the structure of terpenes hinder larvicidal activity, while lipophilicity plays an important role in enhancing biological activity. Larvicidal activity correlates with the interaction of the sterol-carrier protein. Of the 25 compounds evaluated, carvacrol showed the highest larvicidal activity with an LC50 of 8.8 µg/mL. The information included in this work contributes to describing the molecular, topological, and quantum mechanical properties related to the larvicidal activity of monoterpenes and their derivatives.

Construction of efficient Schmidt-number witnesses for high-dimensional quantum states

Recent progress in quantum optics has led to setups that are able to prepare high-dimensional quantum states for quantum information processing tasks. As such, it is of importance to benchmark the states generated by these setups in terms of their quantum mechanical properties, such as their Schmidt numbers, i.e., the number of entangled degrees of freedom. In this paper, we develop an iterative algorithm that finds Schmidt number witnesses tailored to the measurements available in specific experimental setups. We then apply the algorithm to find a witness that requires the measurement of a number of density matrix elements that scales linearly with the local dimension of the system. As a concrete example, we apply our construction method to an implementation with photonic temporal modes.

Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties.

Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning methods. In this work, we use both quantum mechanically (QM) predicted and experimentally measured UV-vis spectra as input to devise four different machine learning architectures, UVvis-SchNet, UVvis-DTNN, UVvis-Transformer, and UVvis-MPNN, and assess the performance of each method. We find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UV-vis spectra with a training RMSE of 0.06 and validation RMSE of 0.08. Most importantly, our model can be used for the challenging task of predicting differences in the UV-vis spectral signatures of regioisomers.

WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets

Multidimensional surfaces of quantum chemical properties, such as potential energies and dipole moments, are common targets for machine learning, requiring the development of robust and diverse databases extensively exploring molecular configurational spaces. Here we composed the WS22 database covering several quantum mechanical (QM) properties (including potential energies, forces, dipole moments, polarizabilities, HOMO, and LUMO energies) for ten flexible organic molecules of increasing complexity and with up to 22 atoms. This database consists of 1.18 million equilibrium and non-equilibrium geometries carefully sampled from Wigner distributions centered at different equilibrium conformations (either at the ground or excited electronic states) and further augmented with interpolated structures. The diversity of our datasets is demonstrated by visualizing the geometries distribution with dimensionality reduction as well as via comparison of statistical features of the QM properties with those available in existing datasets. Our sampling targets broader quantum mechanical distribution of the configurational space than provided by commonly used sampling through classical molecular dynamics, upping the challenge for machine learning models.

Generative Music with Partitioned Quantum Cellular Automata

Cellular automata (CA) are abstract computational models of dynamic systems that change some features with space and time. Music is the art of organising sounds in space and time, and it can be modelled as a dynamic system. Hence, CA are of interest to composers working with generative music. The art of generating music with CA hinges on the design of algorithms to evolve patterns of data and methods to render those patterns into musical forms. This paper introduces methods for creating original music using partitioned quantum cellular automata (PQCA). PQCA consist of an approach to implementing CA on quantum computers. Quantum computers leverage properties of quantum mechanics to perform computations differently from classical computers, with alleged advantages. The paper begins with some explanations of background concepts, including CA, quantum computing, and PQCA. Then, it details the PQCA systems that we have been developing to generate music and discusses practical examples. PQCA-generated materials for Qubism, a professional piece of music composed for London Sinfonietta, are included. The PQCA systems presented here were run on real quantum computers rather than simulations thereof. The rationale for doing so is also discussed.

Physical Chemistry in Context: Using Quantum Mechanics to Understand the Greenhouse Effect

The concepts taught in an undergraduate physical chemistry course are directly related to the molecular processes governing climate change, yet the connections between quantum mechanical molecular properties and global warming are rarely made explicit. To improve student knowledge about climate change, we developed a three-part activity that computationally and experimentally explores blackbody radiation, the greenhouse effect, and the relationship between IR absorption properties and global warming potentials for the anthropogenic greenhouse gases defined in the Paris Agreement. The activities addressed the traditional learning objectives of a quantum-focused physical chemistry course by exploring topics such as the quantized nature of energy in Planck’s Law, the selection rules for rovibrational transitions, and discrepancies between theoretical models and observed experimental results. However, our approach was unique in that each concept was addressed within the context of the atmospheric phenomena related to climate change. A previously published climate science survey instrument was administered pre- and postactivity to assess student learning gains. The average percent of correct responses on this survey increased from 44% to 68% as a result of completing the activities.

Quantum Computing in Insurance Capital Modelling

This paper proposes a quantum computing approach for insurance capital modelling. Using an open-source software development kit, Qiskit, an algorithm for working on a superconducting type IBM quantum computer is developed and implemented to predict the capital of insurance companies in the classical surplus process. With the fundamental properties of quantum mechanics, Dirac notation and Feynman’s path calculation are shown. Furthermore, custom quantum insurance premium and claim gates are investigated in order to build a quantum circuit with respect to initial reserve, premium and claim amounts. Some numerical results are presented and discussed at the end of the paper.

TBMaLT, a flexible toolkit for combining tight-binding and machine learning.

Tight-binding approaches, especially the Density Functional Tight-Binding (DFTB) and the extended tight-binding schemes, allow for efficient quantum mechanical simulations of large systems and long-time scales. They are derived from ab initio density functional theory using pragmatic approximations and some empirical terms, ensuring a fine balance between speed and accuracy. Their accuracy can be improved by tuning the empirical parameters using machine learning techniques, especially when information about the local environment of the atoms is incorporated. As the significant quantum mechanical contributions are still provided by the tight-binding models, and only short-ranged corrections are fitted, the learning procedure is typically shorter and more transferable as it were with predicting the quantum mechanical properties directly with machine learning without an underlying physically motivated model. As a further advantage, derived quantum mechanical quantities can be calculated based on the tight-binding model without the need for additional learning. We have developed the open-source framework-Tight-Binding Machine Learning Toolkit-which allows the easy implementation of such combined approaches. The toolkit currently contains layers for the DFTB method and an interface to the GFN1-xTB Hamiltonian, but due to its modular structure and its well-defined interfaces, additional atom-based schemes can be implemented easily. We are discussing the general structure of the framework, some essential implementation details, and several proof-of-concept applications demonstrating the perspectives of the combined methods and the functionality of the toolkit.

Classification of quantum correlation using deep learning.

Quantum correlation, as an intrinsic property of quantum mechanics, has been widely employed to test the fundamental physical principles and explore the quantum-enhanced technologies. However, such correlation would be drowned and even destroyed in the conditions of high levels of loss and noise, which drops into the classical realm and renders quantum advantage ineffective. Especially in low light conditions, conventional linear classifiers are unable to extract and distinguish quantum and classical correlations with high accuracy. Here we experimentally demonstrate the classification of quantum correlation using deep learning to meet the challenge in the quantum imaging scheme. We design the convolutional neural network to learn and classify the correlated photons efficiently with only 0.1 signal photons per pixel. We show that decreasing signal intensity further weakens the correlation and makes an accurate linear classification impossible, while the deep learning method has a strong robustness of such task with the accuracy of 99.99%. These results open up a new perspective to optimize the quantum correlation in low light conditions, representing a step towards diverse applications in quantum-enhanced measurement scenarios, such as super-resolution microscope, quantum illumination, etc.

Multitask Deep Ensemble Prediction of Molecular Energetics in Solution: From Quantum Mechanics to Experimental Properties.

The past few years have witnessed significant advances in developing machine learning methods for molecular energetics predictions, including calculated electronic energies with high-level quantum mechanical methods and experimental properties, such as solvation free energy and logP. Typically, task-specific machine learning models are developed for distinct prediction tasks. In this work, we present a multitask deep ensemble model, sPhysNet-MT-ens5, which can simultaneously and accurately predict electronic energies of molecules in gas, water, and octanol phases, as well as transfer free energies at both calculated and experimental levels. On the calculated data set Frag20-solv-678k, which is developed in this work and contains 678,916 molecular conformations, up to 20 heavy atoms, and their properties calculated at B3LYP/6-31G* level of theory with continuum solvent models, sPhysNet-MT-ens5 predicts density functional theory (DFT)-level electronic energies directly from force field-optimized geometry within chemical accuracy. On the experimental data sets, sPhysNet-MT-ens5 achieves state-of-the-art performances, which predict both experimental hydration free energy with a RMSE of 0.620 kcal/mol on the FreeSolv data set and experimental logP with a RMSE of 0.393 on the PHYSPROP data set. Furthermore, sPhysNet-MT-ens5 also provides a reasonable estimation of model uncertainty which shows correlations with prediction error. Finally, by analyzing the atomic contributions of its predictions, we find that the developed deep learning model is aware of the chemical environment of each atom by assigning reasonable atomic contributions consistent with our chemical knowledge.