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Fast settling phase locked loops (PLLs) play a pivotal role in many applications requiring rapid attainment of a stable frequency and phase. In modern communication standards, these PLLs are extensively utilized to guarantee precise compliance with dynamic resource allocation requirements. In processors, these PLLs manage dynamic voltage frequency scaling. Moreover, the fast-settling PLLs expedite the scanning of frequency spectra in sophisticated electronic radar set-ups, proving particularly advantageous for imaging and scanning radar applications. The rapid response exhibited by these PLLs is also harnessed in quantum technologies, catering to the urgent need for precise frequency adjustments to manipulate qubit states effectively. The strategies employed to attain fast-settling PLLs are primarily classified into five broad techniques in this article: enhanced phase frequency detection, hybrid multiple subsystems, VCO start-up, gear shift, and look-up table or finite state machine. This article explores the fundamental operational principles encompassing these techniques and presents optimal settling times for each method reported in the literature. Finally, the architectures utilizing these techniques will be evaluated based on their figure of merit (FoM), settling time, and tuning range. [ABSTRACT FROM AUTHOR]

The open radio access network (ORAN) aims to improve network efficiency by reducing latency through the distributed unit (DU) and virtualization in the central unit (CU). However, the deployment of additional DU servers increases power usage and competition among virtual machines for user data processing. This study proposes a novel approach to address these challenges by incorporating load balancing strategies and quantum physics concepts, particularly entanglement theory in classical communication systems. The methodology enables the production of quantum information units from a single classical information unit, aiming to conserve energy. A simplified yet effective power consumption (PC) model for the ORAN architecture captures traffic fluctuations and provides a comprehensive characterization of power usage within a virtualized system. An optimization problem is formulated to select ORAN servers for quantum load balancing that are less energy-efficient, maximizing user benefits. The Lagrange multiplier method is used to deal with nonlinear objective functions. The presented problem is addressed through the use of two numerical methods, namely sequential quadratic programming (SQP) and the active-set approach. It is shown that the SQP model exhibits superior energy efficiency compared to the active-set model, with a difference of approximately 45%.

The purpose of this work is to analyze how new technologies can enhance clinical practice while also examining the physical traits of emotional expressiveness of face expression in a number of psychiatric illnesses. Hence, in this work, an automatic facial expression recognition system has been proposed that analyzes static, sequential, or video facial images from medical healthcare data to detect emotions in people’s facial regions. The proposed method has been implemented in five steps. The first step is image preprocessing, where a facial region of interest has been segmented from the input image. The second component includes a classical deep feature representation and the quantum part that involves successive sets of quantum convolutional layers followed by random quantum variational circuits for feature learning. Here, the proposed system has attained a faster training approach using the proposed quantum convolutional neural network approach that takes $\mathbb {O}{(}\text {log}{(}{n}{)}{)}$ time. In contrast, the classical convolutional neural network models have $\mathbb {O}{(}{n}^{{2}}{)}$ time. Additionally, some performance improvement techniques, such as image augmentation, fine-tuning, matrix normalization, and transfer learning methods, have been applied to the recognition system. Finally, the scores due to classical and quantum deep learning models are fused to improve the performance of the proposed method. Extensive experimentation with Karolinska-directed emotional faces (KDEF), Static Facial Expressions in the Wild (SFEW 2.0), and Facial Expression Recognition 2013 (FER-2013) benchmark databases and compared with other state-of-the-art methods that show the improvement of the proposed system.

Quantum computing offers transformative processing capabilities, particularly in data analysis. However, its adoption in developing countries like Vietnam faces significant barriers, such as financial constraints, infrastructural limitations, and skill shortages. This study aims to determine and analyze these barriers’ impact weights and evaluate and rank potential solutions for overcoming the most critical barriers. This study proposes a multi-faceted approach integrating Multi-Criteria Decision Making (MCDM) methods, including the Delphi Method (DELPHI), Decision Making Trial And Evaluation Laboratory (DEMATEL), Combined Compromise Solution (COCOSO), Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), and the Multiplicative form of Multi-objective Optimization based on Ratio Analysis method (MULTIMOORA), alongside the Quantum Spherical Fuzzy (QSF) set analysis. This combination, especially integrating QSF DELPHI and QSF COCOSO models, represents a significant methodological contribution. Results indicate that Vietnam’s primary barriers to quantum computing are limited infrastructure, high costs, and a lack of skilled workforce. Correspondingly, the most highly rated solution strategies are cost reduction initiatives, investment in infrastructure, and training and education programs, which should be prioritized and considered to remove potential barriers. These findings highlight the need to prioritize addressing issues in facilities, human resources, and financial resources. This research bridges theoretical insights with actionable recommendations, providing a comprehensive roadmap for mitigating barriers and promoting the integration of quantum computing into Vietnam’s data analysis landscape.

The manuscript portrays the investigation of a quantum plasmonic gold torus nanodipole antenna on a zinc oxide dielectric substrate for optical near-field communication. Initially, the dispersive behavior of gold and zinc oxide materials at terahertz is demonstrated through Drude and Lorentz models to confirm the suitability of the material for the design of nano-antenna. The proposed torus nanodipole antenna is analyzed in detail using substrate, torus and feeding gap design parameters of the antenna to obtain the desired plasmon resonance characteristics for optical near-field communication. The minimal reflection of -50.50 dB is observed at 71.3 THz with a directivity of 1.91 dBi using a single nanodipole element. The torus nanodipole antenna array is proposed to enhance the directivity and gain of the torus nanodipole antenna. The directivities of 9.425 dBi, 12.63 dBi and 14.63 dBi and the gain of 7.69 dBi, 10.77 dBi and 13.3 dBi are observed for the $2\times 2$ , $3\times 3$ and $4\times 4$ element nanodipole arrays, respectively. The circuit modeling for the torus nanostructure is proposed and validated using the finite integration technique.

The purpose of this work is to analyze how new technologies can enhance clinical practice while also examining the physical traits of emotional expressiveness of face expression in a number of psychiatric illnesses. Hence, in this work, an automatic facial expression recognition system has been proposed that analyzes static, sequential, or video facial images from medical healthcare data to detect emotions in people’s facial regions. The proposed method has been implemented in five steps. The first step is image preprocessing, where a facial region of interest has been segmented from the input image. The second component includes a classical deep feature representation and the quantum part that involves successive sets of quantum convolutional layers followed by random quantum variational circuits for feature learning. Here, the proposed system has attained a faster training approach using the proposed quantum convolutional neural network approach that takes $\mathbb {O}{(}\text {log}{(}{n}{)}{)}$ time. In contrast, the classical convolutional neural network models have $\mathbb {O}{(}{n}^{{2}}{)}$ time. Additionally, some performance improvement techniques, such as image augmentation, fine-tuning, matrix normalization, and transfer learning methods, have been applied to the recognition system. Finally, the scores due to classical and quantum deep learning models are fused to improve the performance of the proposed method. Extensive experimentation with Karolinska-directed emotional faces (KDEF), Static Facial Expressions in the Wild (SFEW 2.0), and Facial Expression Recognition 2013 (FER-2013) benchmark databases and compared with other state-of-the-art methods that show the improvement of the proposed system. [ABSTRACT FROM AUTHOR]

Quantum computing, communications, sensing, and simulations are radically transformative technologies, with great potential to impact industries and economies. Worldwide, national governments, industries, and universities are moving to create a new class of workforce—the Quantum Engineers. Demand for such engineers is predicted to be in the tens of thousands within a five-year timescale, far exceeding the rate at which the world’s universities can produce Ph.D. graduates in the discipline. How best to train this next generation of engineers is currently a matter of debate. Quantum mechanics—long a pillar of traditional physics undergraduate degrees—must now be merged with traditional engineering offerings. This article discusses the history, development, and the first year of operation of the world’s first undergraduate degree in quantum engineering to be grown out of an engineering curriculum. The main purpose of this article is to inform the wider discussion, now being held by many institutions worldwide, on how best to formally educate the Quantum Engineer.

This article presents a first end-to-end application of a quantum support vector machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer Payments and IBM Quantum Computers via the Qiskit software stack. Based on real card payment data, a thorough comparison is performed to assess the complementary impact brought in by the current state-of-the-art quantum machine-learning algorithms with respect to the classical approach. A new method to search for best features is explored using the QSVM's feature map characteristics. The results are compared using fraud-specific key performance indicators, i.e., accuracy, recall, and false positive rate, extracted from analyses based on human expertise (such as rule decisions), classical machine-learning algorithms (such as random forest and XGBoost), and quantum-based machine-learning algorithms using QSVM. In addition, a hybrid classical–quantum approach is explored by using an ensemble model that combines classical and quantum algorithms to better improve the fraud prevention decision. We found, as expected, that the results highly depend on feature selections and algorithms that are used to select them. The QSVM provides a complementary exploration of the feature space that led to an improved accuracy of the mixed quantum-classical method for fraud detection, on a drastically reduced dataset to fit current state of quantum hardware.

To address the challenge of dense connectors for cryogenic systems, we describe a face-to-face cable connection approach for connecting microwave superconducting stripline cables. The approach provides a space-efficient and reliable interconnect technology for future cryogenic and quantum technology applications at microwave frequencies. A self-aligned assembly was used and was comprised of bottom and top alignment molybdenum pieces and flexible superconducting stripline cables. SU-8, a photodefinable epoxy-based photoresist, was used to form structures defined on the bottom and top Mo pieces to provide alignment and distribute mechanical pressure in the contact regions of the cables. We measured critical transition temperatures in the range of 8.6-9.0 K and an average critical current of 10.2 mA. Time-domain reflectometry measurements at 4.2 K showed a relatively small impedance discontinuity at the connection point and minimal microwave power dependency was observed. This cable-to-cable connection approach for thin, flexible superconducting cables shows promising results as a solution for dense signal integration for microwave and DC interconnects at cryogenic temperatures. [ABSTRACT FROM AUTHOR]

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies. [ABSTRACT FROM AUTHOR]

Quantum engineering in kesterite solar cells reveals a blending advancement keeping in mind that copper–zinc–tin–sulfide (CZTS)-based material requires high-research implementation as a replacement for CIGS materials. In this article, a quantum-dot-embedded kesterite solar cell is proposed, in which CZTSx $\text{Se}1-_{x}$ and CZTS are taken as dot and barrier, respectively, with different sulfur (S) and selenium (Se) content. Focus is given on the incorporation of quantum dots (QDs) and a detailed investigation is carried out through optical and electrical performance, which shows the uniqueness of this study. Quantum efficiency of the solar cell with separate internal and external absorption is also investigated. Different issues on carrier propagation, recombination, and collection are emphasized to highlight the effect of carrier quantization in material. The reliability analysis of the solar cell is also a new scope to investigate the trap-assisted recombination. The detail analysis and remarkable efficiency may bring a new path for next-generation solar cells. [ABSTRACT FROM AUTHOR]

We present a portable Rydberg-atom-based radio frequency (RF) electric ($E$) field measurement instrument (Rydberg field measurement system or RFMS). The RFMS comprises an atomic RF field probe (RFP) connected by a ruggedized fiber-optic patch cord to a portable mainframe control unit. The unit includes a software interface providing self-calibrated SI-traceable RF measurement and analysis, including real-time field and measurement uncertainty readout and spectral RF waveform visualization. We characterize the RFP and measure polar field and polarization patterns along primary axes of the RFP at 12.6 GHz, obtained by rotations of the RFP in the far-field of a standard gain horn antenna. Field pattern measurements at 2.5 GHz are also presented. The measured field patterns are in good agreement with finite-element simulations of the RFP, establishing that the atom-based RF $E$ -field probe is well-suited for SI-traceable RF measurements over multiple bands. A one-time calibration procedure and an uncertainty analysis are introduced and implemented, as required for practical atomic probe designs to realize absolute-standard SI-traceable measurement capability. This includes uncertainty contributions from the atomic-spectroscopy measurement method and analysis and from the material, geometry, and hardware design. Simultaneous RF field and polarization measurements are demonstrated. RF waveform measurement is demonstrated using example pulsed and modulated RF signals. [ABSTRACT FROM AUTHOR]

This work is dedicated to the construction and evaluation of random number generators used in cryptography. The critical element on which the security of information is based is the cryptographic key (usually a binary sequence). In order to be resistant to brute force attacks it is necessary that it be made up of random variables with a certain degree of randomness and independence. Formally, this comes back to generate the cryptographic key through the systems which ensures a certain minimum level of entropy. The observer has access to a sample, of a certain size, and based on it he will estimate the minimum value of the entropy, in the situations in which the variables resulting from the measurement process are independent. In the situation where these variables are not independent, complex mathematical procedures also allow estimation of the minimum entropy. This article is a review of how mathematical entropy can be estimated and evaluated, of the construction mode (from technologies based on analogue procedures: thermal noise in a transistor to modern procedures: quantum devices), as well as to evaluate the security of binary sequence generators used for generating cryptographic keys or critical security parameters related to new technologies based on quantum principles. The techniques and methods used to generate binary random values as well as the methods of statistical and informational validation (Shannon entropy) are exemplified in this paper.

Radio reception relies on antennas for the collection of electromagnetic fields carrying information, and receiver elements for demodulation and retrieval of the transmitted information. Here, we demonstrate an atom-based receiver for AM and FM microwave communication with a 3 dB bandwidth in the baseband of ~100 kHz that provides optical circuit-free field pickup, multiband carrier capability, and inherently high field sensitivity. The atom-based receiver exploits field-sensitive cesium Rydberg vapors in a centimeter-sized glass cell, and electromagnetically induced transparency, a quantum-optical effect, as a readout of baseband signals modulated onto carriers with frequencies ranging over four octaves, from C-band to Q-band. Receiver bandwidth, dynamic range and sideband suppression are characterized, and acquisition of audio waveforms of human vocals demonstrated. The atomic receiver is a valuable receiver technology because it does not require antenna structures and is resilient against electromagnetic interference, while affording multiband operation in a single compact receiving element. [ABSTRACT FROM AUTHOR]

Magnetometers based on quantum mechanical processes enable high sensitivity and long-term stability without the need for re-calibration, but their integration into fieldable devices remains challenging. This article presents a CMOS quantum vector-field magnetometer that miniaturizes the conventional quantum sensing platforms using nitrogen-vacancy (NV) centers in diamond. By integrating key components for spin control and readout, the chip performs magnetometry through optically detected magnetic resonance (ODMR) through a diamond slab attached to a custom CMOS chip. The ODMR control is highly uniform across the NV centers in the diamond, which is enabled by a CMOS-generated ~2.87 GHz magnetic field with < 5% inhomogeneity across a large-area current-driven wire array. The magnetometer chip is 1.5 mm2 in size, prototyped in 65-nm bulk CMOS technology, and attached to a 300 $\times $ 80 $\mu \text{m}^{2}$ diamond slab. NV fluorescence is measured by CMOS-integrated photodetectors. This ON-chip measurement is enabled by efficient rejection of the green pump light from the red fluorescence through a CMOS-integrated spectral filter based on a combination of spectrally dependent plasmonic losses and diffractive filtering in the CMOS back-end-of-line (BEOL). This filter achieves a measured ~25 dB of green light rejection. We measure a sensitivity of 245 nT/Hz1/2, marking a 130 $\times $ improvement over a previous CMOS-NV sensor prototype, largely thanks to the better spectral filtering and homogeneous microwave generation over larger area. [ABSTRACT FROM AUTHOR]

The aim of this paper is to obtain some new Hermite-Hadamard type of inequalities via harmonic convex, strongly harmonic convex, strongly harmonic log-convex functions, and AH-convex in connection with quantum calculus. All the results reduce to ordinary calculus case when q → 1-.

The study of properties of randomly chosen quantum states has in recent years led to many insights into quantum entanglement. In this work, we study private quantum states from this point of view. Private quantum states are bipartite quantum states characterised by the property that carrying out simple local measurements yields a secret bit. This feature is shared by the maximally entangled pair of quantum bits, yet private quantum states are more general and can in their most extreme form be almost bound entangled. In this work, we study the entanglement properties of random private quantum states and show that they are hardly distinguishable from separable states and thus have low repeatable key, despite containing one bit of key. The technical tools we develop are centred around the concept of locally restricted measurements and include a new operator ordering, bounds on norms under tensoring with entangled states and a continuity bound for a relative entropy measure. [ABSTRACT FROM AUTHOR]

Different electronic devices nowadays exploit random number generators either to create the seed for simulations, for gaming or to encrypt the communications. Particularly for cryptography applications, there is an increasing effort to realize a truly random number generator, based not on mathematical algorithms but exploiting randomness of physical processes. Among them quantum processes, provide intrinsic unpredictability, for example exploiting the arrival time, the number or the properties of photons. In this work we focus on photonic quantum random number generator (QRNG), and we analyze different approaches and entropy extraction methods to realize a fully integrated, CMOS compatible, QRNG with photon-emitter realized on the same chip and very close to the detectors of single photons. Non-idealities of the emitter and the detector have to be studied and depends on the structure. They can be mitigated with robust extraction methods which however should not introduce complex algorithms or secondary entropy sources that can make the device no longer quantum based. The ultimate goal is to realize a CMOS building block that can be implemented as building block, with low power consumption and robust to environmental conditions. [ABSTRACT FROM AUTHOR]

We propose an updated compact model for mobility in Nanosheet FETs. This is necessary since Nanosheet FETs exhibit significant mobility degradation with thickness and width scaling caused by centroid shift, changing effective mass due to quantum confinement as well as various crystal orientations of the various conduction planes. The model takes all of these effects into account. It has been implemented in Verilog-A and validated with experimental data. To the best of our knowledge, this is the first compact model capturing the effect of nanosheet scaling on mobility. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient and deterministic quantum key distribution protocol for establishing a secret key between two untrusted users. In this protocol, a secret key is distributed to a sender and a receiver who share entangled states with a third trusted party but not with each other. This secret key is distributed by the means of special pure quantum states using remote state preparation and controlled gates. In addition, we employ the parity bits of the entangled pairs and the ancillary states to assist in preparing and measuring the secret states. Distributing a state to two users requires two maximally entangled pairs as the quantum channel and a two-particle von Neumann projective measurement. The proposed protocol is exact and deterministic. It distributes a secret key of d qubits by the means of 2d entangled pairs and, on average, d bits of classical communication. We demonstrate the security of this protocol against entanglement attacks and present a method of privacy amplification.

In this paper, we analyzed the electrical characteristics of amorphous oxide-based thin-film transistors (TFTs) with extremely thin active layers using a quantum-mechanical method (density gradient method) and a technology computer-aided design simulator. We observed that the evaluation of the TFT performance using the classical method resulted in errors, especially, when the interfacial-defect density of states was high. The electrical characteristics of the TFTs are influenced differently by the bulk and interfacial states when the quantum-mechanical effect is considered. Therefore, it is essential to apply the quantum effect to analyze the electrical characteristics of amorphous oxide-based TFTs with extremely thin active layers.

This letter demonstrates the impact of carrier confinement on the radiation response of Si-based SOI finFETS toward the scaling limit. A comparison between the semi-classical and quantum (i.e., self-consistent Schrödinger–Poisson) calculations establishes the significance of confinement effects in determining the experimentally observed radiation response as fin width is scaled to a few nanometers. This letter reveals that reduction in ionizing radiation sensitivity as a function of scaling is not only due to improved electrostatic control, but also due to reduced oxide fields resulting from size quantization and carrier confinement inside the channel of the device. [ABSTRACT FROM PUBLISHER]

Power and performance tradeoff optimization is one of the most significant issues on heterogeneous multiprocessor or multicomputer systems (HMCSs) with dynamically variable voltage. In this paper, the problem is defined as energy-constrained performance optimization and performance-constrained energy optimization. Task scheduling for precedence-constrained parallel applications represented by a directed acyclic graph (DAG) in HMCSs is an NP-HARD problem. Over the last three decades, several task scheduling techniques have been developed for energy-aware scheduling. However, it is impossible for a single task scheduling technique to outperform all other techniques for all types of applications and situations. Motivated by these observations, hyper-heuristic framework is introduced. Moreover, a quantum-inspired high-level learning strategy is proposed to improve the performance of this framework. Meanwhile, a fast solution evaluation technique is designed to reduce the computational burden for each iteration step. Experimental results show that the fast solution evaluation technique can improve average algorithm search speed by 38 percent and that the proposed algorithm generally exhibits outstanding convergence performance. [ABSTRACT FROM PUBLISHER]