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Outlined is a proposal designed to culminate in the foundry fabrication of arrays of singly addressable quantum dot sources deterministically emitting single pairs of energy-time entangled photons at C-band wavelengths, each pair having negligible spin-orbit fine structure splitting, each pair being channeled into single mode pig-tail optical fibers. Entangled photons carry quantum state information among distributed quantum servers via I/O ports having two functions: the unconditionally secure distribution of decryption keys to decrypt publicly distributed, encrypted classical bit streams as input to generate corresponding qubit excitations and to convert a stream of quantum nondemolition measurements of qubit states into a classical bit stream. Outlined are key steps necessary to fabricate arrays of on-demand quantum dot sources of entangled photon pairs; the principles are (1) foundry fabrication of arrays of isolated quantum dots, (2) generation of localized sub-surface shear strain in a semiconductor stack, (3) a cryogenic anvil cell, (4) channeling entangled photons into single-mode optical fibers, (5) unconditionally secure decryption key distribution over the fiber network, (6) resonant excitation of a Josephson tunnel junction qubits from classical bits, and (7) conversion of quantum nondemolition measurements of qubit states into a classical bit. [ABSTRACT FROM AUTHOR]

In the field of cybersecurity, hackers often enter computer systems despite current security measures, owing to the huge amount of network traffic that makes intruder identification difficult. Differentiating between authorized traffic and abnormal data produced by Distributed Denial of Service (DDoS) attackers is still a major difficulty. This research provides a unique technique that uses Quantum Machine Learning (QML) to improve security protocols for secure communication between two parties. Our strategy enhances detection accuracy and speed by using the characteristics of quantum neural networks. The approach includes creating a dataset from network traffic patterns, preprocessing it, and then turning it into quantum bits via angle embedding. The research uses outlier analysis, min-entropy, and quantum state fidelity to distinguish between normal and abnormal data patterns. The dispersed randomness of network header data is measured using entropy, which aids in identifying security concerns. The suggested QML-based methodology outperforms conventional approaches and current models, including AMM-CNN and ANN models, with a detection accuracy of 99.87% for DDoS attacks. This advancement leads to more effective and secure communication networks. [ABSTRACT FROM AUTHOR]

Hexagonal boron nitride exhibits two types of defects with great potential for quantum information technologies: single-photon emitters (SPEs) and one-dimensional grain boundaries hosting topologically-protected phonons, termed as topologically-protected phonon lines (TPL). Here, by means of a simple effective model and density functional theory calculations, we show that it is possible to use these phonons for the transmission of information. Particularly, a single SPE can be used to induce single-, two- and qubit-phonon states in the one-dimensional channel, and (ii) two distant SPEs can be coupled by the TPL that acts as a waveguide, thus exhibiting strong quantum correlations. We highlight the possibilities offered by this material-built-in nano-architecture as a phononic device for quantum information technologies. [ABSTRACT FROM AUTHOR]

We introduce a new approach for describing nonstationary quantum systems with a discrete energy spectrum. The essence of this approach is that we describe the evolution of a quantum system in a time-dependent basis. In a sense, this approach is similar to the description of the system in the interaction representation. However, the time dependence of the basic states of the representation is determined not by the evolution operator with a time-independent Hamiltonian but by the eigenstates of the time-dependent Hamiltonian defined at the current time. The time dependence of the basic states of the representation leads to the appearance of an additional term in the Schrödinger equation, which in the case of slowly changing parameters of the Hamiltonian can be considered as a small perturbation. The adiabatic representation is suitable in cases where it is impossible to apply the standard interaction representation. The application of the adiabatic representation is illustrated by the example of two spins connected by a magnetic dipole–dipole interaction in a slowly varying external magnetic field. [ABSTRACT FROM AUTHOR]

Material discovery is a phenomenon practiced since the evolution of the world. The discovery of materials has led to significant development in varied fields such as Science, Engineering and Technology. Computationally simulating molecules has been an area of interest in the industry as well as academia. However, simulating large molecules can be computationally expensive in terms of computing power and complexity. Quantum computing is a recent development that can improve the efficiency in predicting properties of atoms and molecules which will be useful for material design. The Variational Quantum Eigensolver (VQE) is one such quantum algorithm used to calculate the ground state energy of molecules or ions. In this study, we have done a comparative analysis of the parameters that constitute the VQE algorithm. This includes components such as basis, qubit mapping, ansatz, and optimizers used. We have also developed a database consisting of 79 single atoms and their variations of oxidation states and 33 molecules with the data of their Hamiltonian and ground state energy and dipole moment. [ABSTRACT FROM AUTHOR]

Quantum phenomena, including entanglement, superposition, tunneling, and spin-orbit interactions, among others, are foundational to the development of recent innovations in quantum computing, teleportation, encryption, sensing, and new modalities of electronics, such as spintronics, spinorbitronics, caloritronics, magnonics, twistronics, and valleytronics. These emerging technologies provide disruptive influences to global commercial markets. These remarkable advances in quantum technologies are nearly always enabled by the discovery of materials and their quantum behaviors. Such advances are governed by quantum principles that are strongly influenced by environmental, physical, topological, and morphological conditions such as very small length scales, short time durations, ultrahigh pressures, ultralow temperatures, etc., which lead to quantum behaviors that manifest as quantum tunneling, entanglement, superpositioning, superfluidity, low-dimensional, high-temperature and high-pressure superconductivity, quantum fluctuations, Bose-Einstein condensates, topological effects, and other phenomena that are not yet fully understood nor adequately explored. Here, we provide a review of quantum materials developed up to 2023. Remarkable advances in quantum materials occur daily, and therefore, by the time of publication, new and exciting breakthroughs will have occurred that are regrettably not covered herein. [ABSTRACT FROM AUTHOR]

Abstract In the field of cybersecurity, hackers often enter computer systems despite current security measures, owing to the huge amount of network traffic that makes intruder identification difficult. Differentiating between authorized traffic and abnormal data produced by Distributed Denial of Service (DDoS) attackers is still a major difficulty. This research provides a unique technique that uses Quantum Machine Learning (QML) to improve security protocols for secure communication between two parties. Our strategy enhances detection accuracy and speed by using the characteristics of quantum neural networks. The approach includes creating a dataset from network traffic patterns, preprocessing it, and then turning it into quantum bits via angle embedding. The research uses outlier analysis, min-entropy, and quantum state fidelity to distinguish between normal and abnormal data patterns. The dispersed randomness of network header data is measured using entropy, which aids in identifying security concerns. The suggested QML-based methodology outperforms conventional approaches and current models, including AMM-CNN and ANN models, with a detection accuracy of 99.87% for DDoS attacks. This advancement leads to more effective and secure communication networks.

The paper presents a method for semantic data analysis by means of complex-valued matrix decomposition. The method is based on the quantum model of contextual decision-making, according to which observable probabilities are generated by qubit states, representing subjective meaning of the contexts relative to the basis decision. In the simplest three-context case, one of these qubits is decomposed to superposition of the remaining two, mathematically encoding semantic relations between the three contexts. For use in data analysis this model is translated to the matrix form, in which rows and columns correspond to the contexts and instances of experiment. The observable real-valued data then emerge from a complex-valued amplitude matrix, decomposed to a product of a real basis matrix and complex-valued matrix of superposition coefficients. This decomposition reveals stable process-semantic relations between the considered contexts, not captured by other methods of analysis. As a result, the data are approximated with higher precision and fewer parameters than singular and non-negative matrix decompositions, truncated to the same dimension. The model is experimentally approved in descriptive and prognostic regimes. The result opens prospects for development of nature-like computational architectures on novel logical grounds.

Quantum field lens coding algorithm (QF-LCA) dataset is useful for simulating systems and predict system events with high probability. This is achieved by computing QF lens distance-based variables associated to event probabilities from the dataset produced by field lenses that encode system states on a quantum level. The probability of a state transition (ST), doubles in prediction values at the decoding step, e.g., ST probabilities of P≥1/3 into P≥2/3 based on a single field (SF) transform into a quantum double-field (QDF). This transformation doubles the ST probability space via the field's scalar κ, in a published QDF method article. A QDF, as a double-field computation (DFC) model, simulates thermodynamic systems in predicting events by producing datasets from a QF-LCA, using laser cooling methods relative to high energy systems. The dataset can be used to, e.g., train quantum algorithms via quantum artificial intelligence (QAI) for a QF-LCA user. The user then decides after the algorithm predicts and suggests an efficient energy path for the system to choose. For example, an N-particle system simulated as a heat engine, by QAI classifier(s), predicts and reroutes particle energy paths on a logarithmic input-output scale. To determine system's energy change, the QDF lens code (DFC) measures entanglement entropy (EE). The algorithm's classification of EE values distinguishes entangled states in the system. The QF-LCA program employs the dataset to achieve an automated prediction and classification method, rather than dataset's manual use and analysis by the user. As the system evolves in its distribution of states, those particles not reaching a desired energy state (a target state or TS), i.e., the probability of observing a ground or excited state (GS or ES) outcome at the decoding step, can be rerouted by the heat engine to satisfy a TS outcome. This establishes a GS or ES energy profile to access and classify states by a classifier. The data points (qubits) in this profile are inverse distance-based and labelled for a specific class. After learning the profile, the classifier decodes and predicts the next system state. A QDF AI game “Alice & Bob's Quantum Doubles,” is developed to validate the dataset as the P’s map for a classical/quantum prediction where the P’s of states and the user's P’s correlate in their value difference, ΔP. Dataset validation results are mapped to an intelligent decision simulator (IDS) as a QAI map. This maximizes system efficiency on a TS by EE of energy states (distributed in the system). Future additions to the dataset from the QAI map program can improve quantum algorithms to determine which particles of the system participate in a phase transition after state prediction. QF-LCA applications are in data science, security, forensics, particle physics, etc., such as retrieving or reconstructing information by distinguishing particle states from an evidence sample. Examples are, reconstruct damaged DNA strands of cells to predict a virus's TS, or cancer cell, its spread and growth against healthy cells, identify forged documents from genuine based on QDF's P values, and so on.

ABSTRAK Pada artikel ini, teleportasi kuantum satu qubit dengan empat keadaan Bell sebagai kanal kuantum telah dikaji secara eksplisit. Empat keadaan Bell yang digunakan dinyatakan dalam bentuk kompak. Hasil kajian menunjukkan bahwa teleportasi kuantum satu qubit dengan empat keadaan Bell dapat dilakukan untuk mengirim informasi kuantum dari pengirim (Alice) ke penerima (Bob) setelah hasil pengukuran oleh Alice dikirim melalui kanal klasik. Untuk hasil pengukuran , dan dari setiap keadaan Bell , , pada persamaan 1, terdapat satu kemungkinan di mana Bob tidak perlu melakukan transformasi uniter untuk merekonstruksi qubitnya dan selebihnya Bob perlu melakukan transformasi uniter. Untuk keadaan Bell , Bob perlu melakukan transformasi uniter pada qubitnya dari setiap hasil pengukuran Alice. Kata kunci—Keadaan Bell, qubit, teleportasi kuantum. ABSTRACT In this article, one-qubit quantum teleportation with four Bell states as quantum channel has been studied explicitly. Four Bell states used are expressed in compact form. The result of study shown that one-qubit teleportation quantum with four Bell states can be carried out to send quantum information from the sender (Alice) to the receiver (Bob) after Alice’s measurement results are send via classical channel. For the measurement results are , dan from each Bell state , , in equation 1, there is one possibilty where Bob does not need to perform a unitary transformation to reconstruct his qubit and the rest Bob needs to perform a unitary transformation. For Bell state , Bob needs to perform a unitary transformation on his qubit from each of Alice’s measurements. Keywords—Bell state, qubit, quantum teleportation.

Quantum phenomena, including entanglement, superposition, tunneling, and spin–orbit interactions, among others, are foundational to the development of recent innovations in quantum computing, teleportation, encryption, sensing, and new modalities of electronics, such as spintronics, spin-orbitronics, caloritronics, magnonics, twistronics, and valleytronics. These emerging technologies provide disruptive influences to global commercial markets. These remarkable advances in quantum technologies are nearly always enabled by the discovery of materials and their quantum behaviors. Such advances are governed by quantum principles that are strongly influenced by environmental, physical, topological, and morphological conditions such as very small length scales, short time durations, ultrahigh pressures, ultralow temperatures, etc., which lead to quantum behaviors that manifest as quantum tunneling, entanglement, superpositioning, superfluidity, low-dimensional, high-temperature and high-pressure superconductivity, quantum fluctuations, Bose–Einstein condensates, topological effects, and other phenomena that are not yet fully understood nor adequately explored. Here, we provide a review of quantum materials developed up to 2023. Remarkable advances in quantum materials occur daily, and therefore, by the time of publication, new and exciting breakthroughs will have occurred that are regrettably not covered herein.

Quantum information processing leverages the principles of quantum mechanics, utilizing qubits, to improve computational and communicative tasks. In this realm, the quantum channel’s capacity is pivotal in determining the efficiency and accuracy of quantum information handling, with its performance being significantly influenced by channel noise. Our study aims to establish a holistic hybrid quantum noise model to determine the quantum channel capacity. In this paper, we formulated a mathematical expression for this capacity and conducted simulations for both Gaussian and non-Gaussian inputs. A hybrid noise model is constructed by convolution of Poisson-distributed quantum noise with classical additive white Gaussian noise. We characterized the quantum-classical noise and the received signal using Gaussian Mixture Models. The maximum amount of quantum information that can be reliably transmitted over a quantum channel (per use of the channel) is determined by its capacity, and entropy and related quantities like mutual information play a role in calculating this capacity. Our formulation of quantum channel capacity is derived from the mutual information shared between the transmitter and receiver, encompassing the entropies of the signals. The quantum channel presents a higher capacity-to-signal-to-noise ratio for Gaussian inputs than non-Gaussian ones.

A limited number of qubits, high error rates, and limited qubit connectivity are major challenges for effective near-term quantum computations. Quantum circuit partitioning divides a quantum computation into classical postprocessing steps and a set of smaller scale quantum computations that individually require fewer qubits, lower qubit connectivity, and typically incur less error. However, as partitioning generally increases the duration of a quantum computation exponentially in the required partitioning effort, it is crucial to select optimal partitioning points, so-called cuts, and to use optimal cut realizations. In this work, we develop the first optimal partitioning method relying on quantum circuit knitting for optimal cut realizations and an optimal selection of wire cuts and gate cuts that trades off ancilla qubit insertions for a decrease in quantum computing time. Using this combination, the developed method demonstrates a reduction in quantum computing runtime by 41% on average compared to previous quantum circuit partitioning methods. Furthermore, the qubit requirement of the evaluated quantum circuits was reduced by 40% on average for a runtime budget of one hour and a sampling frequency of 1 kHz. These results highlight the optimality gap of previous quantum circuit partitioning methods and the possible extension in the computational reach of near-term quantum computers.

We introduce a new approach for describing nonstationary quantum systems with a discrete energy spectrum. The essence of this approach is that we describe the evolution of a quantum system in a time-dependent basis. In a sense, this approach is similar to the description of the system in the interaction representation. However, the time dependence of the basic states of the representation is determined not by the evolution operator with a time-independent Hamiltonian but by the eigenstates of the time-dependent Hamiltonian defined at the current time. The time dependence of the basic states of the representation leads to the appearance of an additional term in the Schrödinger equation, which in the case of slowly changing parameters of the Hamiltonian can be considered as a small perturbation. The adiabatic representation is suitable in cases where it is impossible to apply the standard interaction representation. The application of the adiabatic representation is illustrated by the example of two spins connected by a magnetic dipole–dipole interaction in a slowly varying external magnetic field.

We analyze the results of the test of π/2 qubit rotations on a public quantum computer provided by IBM. We measure a single qubit rotated by π/2 about a random axis, and we accumulate vast statistics of the results. The test performed on different devices shows systematic deviations from the theoretical predictions, which appear at level 10-3. Some of the differences, beyond 5 standard deviations, cannot be explained by simple corrections due to nonlinearities of pulse generations. The magnitude of the deviation is comparable with the randomized benchmarking of the gate, but we additionally observe a pronounced parametric dependence. We discuss other possible reasons for the deviations, including states beyond the single-qubit space. The deviations have a similar structure for various devices used at different times, so they can also serve as a diagnostic tool to eliminate imperfect gate implementations and a faithful description of the involved physical systems. [ABSTRACT FROM AUTHOR]

Since the mention of the Fourth Industrial Revolution in 2016, quantum computers and quantum computing (QC) have emerged as key technologies. Many researchers are trying to realize quantum computers and quantum computing. In particular, most of the development and application of metaheuristics algorithms using quantum computing is focused on computer engineering fields. Cases in which the developed algorithm is applied to the optimal design of a building or the optimal design results presented by expanding the algorithm in various directions are very insufficient. Therefore, in this paper, we proposed four methods of adopting qubits to perform pitch adjusting in the optimization process of the QbHS (quantum-based harmony search) algorithm and applied it to TTO (truss topology optimization) using four methods to compare the results. The four methods of adopting qubits have the same or decreased number of qubits adopted as the number of iterations changes. As a result of applying TTO using four methods, convergence performance differed depending on the adoption method, and convergence performance was superior to conventional HS (harmony search) algorithms in all methods. The optimal design of structural engineering using such QC is expected to contribute to the revitalization of future technologies in the architectural field and the field of computer information systems. [ABSTRACT FROM AUTHOR]

Having access to a unique quantum circuit that one wants to protect against use by others is a very likely scenario in industrial computing. However, currently, users rely on classical computer security schemes, which have known shortcomings. In this paper, we introduce a novel protection scheme along with a survey of other known methods to protect quantum information. In particular, we review physically unclonable functions (PUFs), obfuscation, and introduce quantum logic locking (QLL). The latter technique provisions end users to protect their circuit from an adversary through the use of a secret key.

Introduction. The hope of solving the problem of the avalanche-like growth of requirements for computing power, essential for solving complex routing problems and other problems of combinatorial optimization, relies on the latest quantum computers, in the development of which governments and corporations invest multi-billion investments. The article examines modern routing algorithms and performs their analysis and verification, if the authors of the algorithm provided appropriate test programs. The purpose of the article is to review the current state of development in the field of development of routing algorithms for hybrid quantum-classical clouds, analyze them and propose a classification of algorithms. Results. Modern quantum computers (QCs) make it possible to find approximate solutions to some of mathematical problems faster than classical computers. The inaccuracy of the solutions obtained by the QC is a consequence of physical and technological limitations: calculation errors are caused by thermal noise, a small number of computational elements - qubits and connections between them, which requires the decomposition of the problem and the use of heuristic algorithms. The analysis of approaches to the solution of optimization problems on QC allows us to single out: quantum response and variational search of eigenvalues based on quantum logic gates as the general directions of development of the vast majority of algorithms for solving routing problems. The considered algorithms reduce the vehicle routing problem to a quadratic unconstrained binary optimization problem, which is isomorphic to the Hamilton-Ising model. In this form, the problem is suitable for embedding in QC, which finds an approximate solution that has the best statistical reliability or corresponds to the quantum state with the lowest energy. As a separate class, vehicle routing algorithms for classical computers that use quantum computing to accelerate problem solving can be distinguished. For example, neural networks that calculate weighting factors using QC or an ant algorithm that calculates a pheromone trail in a hybrid cloud. It should be mentioned the quantum-inspired algorithms, which are based on software tools for the simulation of QC and the corresponding libraries and allow creating an effective class of algorithms for solving problems of vehicle routing. Conclusions. Combining hardware quantum annealing with a number of software tools for calculating optimization problems for classical computers in a hybrid quantum-classical cloud service allows to obtain advantages in speed and accuracy of some types of complex optimization problems of a commercial scale, in particular, routing vehicles, which is already bringing substantial profits to a number of corporations.

We analyze the results of the test of π/2 qubit rotations on a public quantum computer provided by IBM. We measure a single qubit rotated by π/2 about a random axis, and we accumulate vast statistics of the results. The test performed on different devices shows systematic deviations from the theoretical predictions, which appear at level 10–3. Some of the differences, beyond 5 standard deviations, cannot be explained by simple corrections due to nonlinearities of pulse generations. The magnitude of the deviation is comparable with the randomized benchmarking of the gate, but we additionally observe a pronounced parametric dependence. We discuss other possible reasons for the deviations, including states beyond the single-qubit space. The deviations have a similar structure for various devices used at different times, so they can also serve as a diagnostic tool to eliminate imperfect gate implementations and a faithful description of the involved physical systems.

Ultracold atoms in the atomic Bose-Einstein condensate (ABEC) state can form molecular Bose-Einstein condensate (MBEC) through photoassociation. In the atom-molecule Bose-Einstein condensates (BECs), two or more atoms in the ABEC can combine to form a molecule in the MBEC and again a molecule from a MBEC can decompose to atoms in the ABEC. The Bose-stimulated Raman adiabatic passage is an efficient mechanism for conversion of an atomic BEC to a molecular BEC. A three-mode atom-molecule Bose-Einstein condensates system can be prepared through the photoassociative Bose-stimulated Raman adiabatic passage. In our system, three modes are one ABEC, one excited MBEC, and one stable MBEC. The intramodal interactions due to the (3) nonlinearity is present in all three BEC modes along with ABEC-excited MBEC and excited MBEC-stable MBEC intermodal couplings. The quantum mechanical Hamiltonian of the system is constructed considering all three intraspecies interactions and intermodal couplings among the modes. The Hamiltonian of the system is solved analytically using a special intuitive approach which is more general and gives more accurate result than the well-known short time approximation method. The correctness of the solution is verified through the equal time commutation relation. Staring from a three-mode composite coherent state we compute the time evolution of the field annihilation operators of all three modes in presence of all possible interactions and couplings. Using these solutions, we investigate the quantum entanglement properties of the system for all three two-mode combinations. Entanglement is found for two combinations of modes, where as one combination is always separable. Also, we study the dependence of the entanglement properties of the system with the interaction and coupling constants. [ABSTRACT FROM AUTHOR]

An important problem in quantum computation is the generation of single-qubit quantum gates such as Hadamard (H) and π / 8 (T) gates, which are components of a universal set of gates. Qubits in experimental realizations of quantum computing devices are interacting with their environment. While the environment is often considered as an obstacle leading to a decrease in the gate fidelity, in some cases, it can be used as a resource. Here, we consider the problem of the optimal generation of H and T gates using coherent control and the environment as a resource acting on the qubit via incoherent control. For this problem, we studied the quantum control landscape, which represents the behavior of the infidelity as a functional of the controls. We considered three landscapes, with infidelities defined by steering between two, three (via Goerz–Reich–Koch approach), and four matrices in the qubit Hilbert space. We observed that, for the H gate, which is a Clifford gate, for all three infidelities, the distributions of minimal values obtained with a gradient search have a simple form with just one peak. However, for the T gate, which is a non-Clifford gate, the situation is surprisingly different—this distribution for the infidelity defined by two matrices also has one peak, whereas distributions for the infidelities defined by three and four matrices have two peaks, which might indicate the possible existence of two isolated minima in the control landscape. It is important that, among these three infidelities, only those defined with three and four matrices guarantee the closeness of the generated gate to a target and can be used as a good measure of closeness. We studied sets of optimized solutions for the most general and previously unexplored case of coherent and incoherent controls acting together and discovered that they form sub-manifolds in the control space, and unexpectedly, in some cases, two isolated sub-manifolds. [ABSTRACT FROM AUTHOR]

In this paper, we present the development of a quantum computing method for calculating the value at risk ( V a R ) for a portfolio of assets managed by a finance institution. We extend the conventional Monte Carlo algorithm to calculate the V a R of an arbitrary number of assets by employing random variable algebra and Taylor series approximation. The resulting algorithm is suitable to be executed in real quantum computers. However, the noise affecting current quantum computers renders them almost useless for the task. We present a methodology to mitigate the noise impact by using neural networks to compensate for the noise effects. The system combines the output from a real quantum computer with the neural network processing. The feedback is used to fine tune the quantum circuits. The results show that this approach is useful for estimating the V a R in finance institutions, particularly when dealing with a large number of assets. We demonstrate the validity of the proposed method with up to 139 assets. The accuracy of the method is also proven. We achieved an error of less than 1 % in the empirical measurements with respect to the parametric model. [ABSTRACT FROM AUTHOR]

There are a variety of methods employed by laboratories for quantifying extracellular vesicles isolated from bacteria. As a result, the ability to compare results across published studies can lead to questions regarding the suitability of methods and buffers for accurately quantifying these vesicles. Within the literature, there are several common methods for vesicle quantification. These include lipid quantification using the lipophilic dye FM 4-64, protein quantification using microBCA, Qubit, and NanoOrange assays, or direct vesicle enumeration using nanoparticle tracking analysis. In addition, various diluents and lysis buffers are also used to resuspend and treat vesicles. In this study, we directly compared the quantification of a bacterial outer membrane vesicle using several commonly used methods. We also tested the impact of different buffers, buffer age, lysis method, and vesicle diluent on vesicle quantification. The results showed that buffer age had no significant effect on vesicle quantification, but the lysis method impacted the reliability of measurements using Qubit and NanoOrange. The microBCA assay displayed the least variability in protein concentration values and was the most consistent, regardless of the buffer or diluent used. MicroBCA also demonstrated the strongest correlation to the NTA-determined particle number across a range of vesicle concentrations. Overall, these results indicate that with appropriate diluent and buffer choice, microBCA vs. NTA standard curves could be generated and the microBCA assay used to estimate the particle number when NTA instrumentation is not readily available. [ABSTRACT FROM AUTHOR]

Having access to a unique quantum circuit that one wants to protect against use by others is a very likely scenario in industrial computing. However, currently, users rely on classical computer security schemes, which have known shortcomings. In this paper, we introduce a novel protection scheme along with a survey of other known methods to protect quantum information. In particular, we review physically unclonable functions (PUFs), obfuscation, and introduce quantum logic locking (QLL). The latter technique provisions end users to protect their circuit from an adversary through the use of a secret key. [ABSTRACT FROM AUTHOR]

Quantum bits, or qubits, are an example of coherent circuits envisioned for next-generation computers and detectors. A robust superconducting qubit with a coherent lifetime of O(100 µs) is the transmon: a Josephson junction functioning as a non-linear inductor shunted with a capacitor to form an anharmonic oscillator. In a complex device with many such transmons, precise control over each qubit frequency is often required, and thus variations of the junction area and tunnel barrier thickness must be sufficiently minimized to achieve optimal performance while avoiding spectral overlap between neighboring circuits. Simply transplanting our recipe optimized for single, stand-alone devices to wafer-scale (producing 64, 1x1 cm dies from a 150 mm wafer) initially resulted in global drifts in room-temperature tunneling resistance of 30%. Inferring a critical current ≡σIc≪Ic≫ variation from this resistance distribution, we present an optimized process developed from a systematic 38 wafer study that results in < 3.5% relative standard deviation (RSD) in critical current () for 3000 Josephson junctions (both single-junctions and asymmetric SQUIDs) across an area of 49 cm2. Looking within a 1x1 cm moving window across the substrate gives an estimate of the variation characteristic of a given qubit chip. Our best process, utilizing ultrasonically assisted development, uniform ashing, and dynamic oxidation has shown = 1.8% within 1x1 cm, on average, with a few 1x1 cm areas having < 1.0% (equivalent to < 0.5%). Such stability would drastically improve the yield of multi-junction chips with strict critical current requirements.

The signature is a behavioral-based human characteristics that is extensively used as legal evidence of identification on bank checks, credit cards, and wills. Developing a good offline signature verification (OSV) system is essential to avoid fraud. Due to the plurality of signature styles and sample contexts, feature extraction (FE) is challenging for OSV systems. Current signature verification methods present promising results in distinguishing between genuine and forged signatures, but they are far from satisfactory. Regarding feature selection (FS), stable and appropriate features substantially impact signature verification. The issue with current approaches is that they apply combination features without addressing correlation and discriminant. A genetic algorithm (GA) is popularly deployed as a method for optimization to identify the best features. Regrettably, GA has drawbacks when the search dimension is expanded, such as a lack of comprehensive exploitation, difficulties in achieving good convergence, and therefore increased computation. This study addresses the aforementioned challenges by introducing multifeature fusion and discriminant FS using a novel quantum inspired GA (QIGA). QIGAs are constructed using quantum computing concepts such as qubits (Q-bits) and superposition of states. QIGA is able to express a linear superposition of solutions with the objective of increasing gene diversity by utilizing the qubit as a representation. Quantum rotation computing (QRC) enhances population diversity and tackles computing complexity and early convergence issues in GA. The superposition concept in QRC utilizes both mutation (divergence) and crossover (convergence) procedures to expand diversity and improve population selection. QIGA performs better in a parallel structure because of its quick convergence and robust global search capabilities. This implies that QIGA embodies features of both exploration and exploitation. The studied approach is applied to three different databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Consequently, the proposed OSV based on the QIGA improved equal error rate (EER) between 10% and 20% over the GA without influencing computation complexity.

In this study, SiN-based gratings with various radius of curvature (r) are designed and fabricated for the optical addressing of trapped Sr+ ion. The beam width of the light coupled out from the gratings is investigated using a self-developed Python-based data analysis technique. It is found that the r values have insignificant influence on the beam width along x-axis; whereas the beam waist along y-axis reduces from 13.96 to 12.3 μm as r increases from 15 to 20 μm, then increases gradually to 20.51 μm as the r value further increases to 60 μm. The obtained results can provide a versatile solution on the optical addressing of trapped Sr+ for applications in quantum engineering.

Despite the growing impact of emissions on our health and the environment, there remains an unmet need for emission concentration prediction and forecasting. The accumulating monitoring station and satellite data make the problem well-suited for quantum machine learning. This work takes a quantum machine learning approach to the spatiotemporal prediction of emission concentration. A quantum quanvolutional neural network model was developed and compared to a classical spatiotemporal ConvLSTM model using an evaluation framework of baseline models and metrics of per-pixel loss and intersection over union accuracy. The quantum quanvolutional neural network developed successfully generates one-hour-ahead emission concentration forecasts with increasingly lower loss (6.5% and 30.5% less) and higher accuracy (18.4% and 18.6% higher) compared to the input-independent and random baselines at the end of training. The quantum model was also comparable to the classical ConvLSTM model, with slightly lower loss (4%) but also slightly lower accuracy (3.7%). The study's results suggest that the quantum machine learning approach has the potential to improve emission concentration modeling and could become a powerful tool for accurately predicting air pollution. [ABSTRACT FROM AUTHOR]

For searching an item in unstructured databases, Grover's quantum search algorithm offers quadratic speedup over classical search algorithms. This paper reports 2 to 5 quantum-bit (Qubit) implementations of Grover's search algorithm using the phase-flip method for oracle function realization without any extra ancilla qubit. A comprehensive estimation and analysis of the theoretical and physical accuracies of the algorithm have been presented. The impact of increasing qubits on accuracy has been computed and analyzed. The metrics delineated for comparison are the number of qubits and gates, depth of the circuit, execution time, and theoretical/physical accuracy. The results revealed a greater disparity between theoretical and physical accuracy using a higher number of qubits perceived to be caused by noisy qubits utilized in computations. The novelty of the work is the investigation of variations caused by the noise in the accuracy and execution time of Grover's search algorithm. The results indicate that because of noise, the accuracy of 2- and 3-qubit implementations declined by 14.49% and 33.86%, whereas the execution time increased by 50% and 80%; respectively. [ABSTRACT FROM AUTHOR]

The objective of this article is to present a simulation of quantum information. We take advantage of quantum entanglement to ensure the spontaneous transfer of a state from one qubit to another similar qubit. Similarly, the principle of quantum teleportation is conceived in the application; it is not about transfer of matter, but of information. Indeed, two systems already exist, and at the end of the teleportation, the process is destructive—the first system will no longer be in the same state as the initial state and the second system takes the state of the first, which does not contradict the no-cloning theorem. [ABSTRACT FROM AUTHOR]

Currently, many problems such as variable signal resources, complex execution environments, and low efficiency of scheduling algorithms are faced by heterogeneous signal processing platforms. The task scheduling algorithm is one of the key factors that directly affect the performance of the processing platform. In order to solve the problems of low efficiency of task scheduling algorithms and high computational cost of processors, a heterogeneous platform scheduling algorithm based on the quantum genetic algorithm is proposed in this paper. The algorithm constructs a task scheduling model by using a directed acyclic graph. This paper quantifies the mapping relationship between the quantum genetic algorithm and task scheduling. It corresponds qubits to binary, chromosomes to processor numbers, and individuals to processor scheduling strategies. In this paper, a new way of coding chromosomes using quantum coherence properties is designed to reduce the population size and increase population diversity. Crossover operations are performed on all individuals using full-interference crossover to avoid the results falling into local optimal solutions. The population of slow convergence is solved by implementing mutation operations on populations through quantum rotation gates. In addition, a task pre-ordering stage is designed based on the table scheduling algorithm. The task scheduling priority developed at this stage is used as the reference value for the initial encoding of the population, so that the search space for solutions is reduced. Finally, experiments are conducted using randomly generated task graphs. The algorithm is compared with improved genetic algorithms and existing intelligent scheduling algorithms. The results show that the algorithm can still obtain better results when the number of populations and iterations is small. It is more appropriate for heterogeneous platforms and computation-intensive tasks. [ABSTRACT FROM AUTHOR]

A device called the qubit energy tuner (QET), based on single flux quantum (SFQ) circuits, has been proposed for Z control of superconducting qubits. The QET is created by improving flux digital-to-analog converters (flux DACs). It can set the energy levels or frequencies of qubits, particularly flux-tunable transmons, and perform gate operations requiring Z control. The circuit structure of the QET is elucidated, consisting of an inductor loop and flux bias units for coarse or fine-tuning. The key feature of the QET is analyzed to understand how SFQ pulses change the inductor loop current, which provides external flux for qubits. Three simulations were performed to verify QET functionality. The first simulation verified the responses of the inductor loop current to SFQ pulses, showing a relative deviation of approximately 4.259% between the analytical solutions of the inductor loop current and the solutions from the WRSpice time-domain simulation. The second and third simulations, using QuTip, demonstrated how to perform a Z gate and an iSWAP gate using the QET, respectively, with corresponding fidelities of 99.99884% and 99.93906% for only one gate operation to specific initial states. These simulations indicate that the SFQ-based QET could act as an efficient component of SFQ-based quantum–classical interfaces for digital Z control of large-scale superconducting quantum computers.

In this paper, we study the exotic Landau problem at the classical level where two conserved quantities are derived. At the quantum level, the corresponding quantum operators of the conserved quantities provide two oscillator representations from which we derive two Boson Fock spaces. Using the normalized coherent states which are the minimum uncertainty states on noncommutative configuration space isomorphic to each of the boson Fock space, we form entangled coherent states which are Bell- like states labeled quasi-Bell states. The effect of non-maximality of a quasi-Bell state based quantum channel is investigated in the context of a teleportation of a qubit.

Since the qubit’s performance of solid-state spin centers depends highly on the host material, spin centers using new host materials may offer new qubit applications. We investigate the optical properties of Ce-implanted MgO and MgAl _2 O _4 as potential materials holding the optically accessible qubit. We find that the photoluminescence of Ce-implanted MgAl _2 O _4 is more than 10 times brighter than that of Ce-implanted MgO and observe polarization-dependent emission of Ce center in MgAl _2 O _4 with 2% at 4 K under 500 mT, suggesting that the properties required for initializing and reading the state of the spin qubit have been achieved.

Quantum computing is still a developing, but an extremely promising area. The article lays out the main ideas behind quantum computing in simple terms. The topic of quantum computers based on qudits - multidimensional analogues of qubits, which have recently received much attention due to their efficiency, is also covered The fundamentals of quantum mechanics, which are necessary for understanding the principles of operation of a quantum computer, such concepts as qubits and qudits, linear operators, the measurement process, etc are introduced. As an example of quantum computing, the principle of operation of the Deutsch-Jozsa algorithm, one of the first quantum algorithms to demonstrate their advantages, and its generalization to qudits, are analyzed in detail. The process of writing the simplest quantum computer emulator in the Python programming language is described step by step. The emulator operates with an arbitrary number of qubits and allows you to apply arbitrary operators to them and carry out multiple measurements of the final state of the qubit. A generalization of this emulator for working with qudits is given after that. To demonstrate the emulator we have written, we present programs that implement the Deutsch-Jozsa algorithm and its generalizations on it, and test them.

The earlier prediction of heart diseases and appropriate treatment are important for preventing cardiac failure complications and reducing the mortality rate. The traditional prediction and classification approaches have resulted in a minimum rate of prediction accuracy and hence to overcome the pitfalls in existing systems, the present research is aimed to perform the prediction of heart diseases with quantum learning. When quantum learning is employed in ML (Machine Learning) and DL (Deep Learning) algorithms, complex data can be performed efficiently with less time and a higher accuracy rate. Moreover, the proposed ML and DL algorithms possess the ability to adapt to predictions with alterations in the dataset integrated with quantum computing that provides robustness in the earlier detection of chronic diseases. The Cleveland heart disease dataset is being pre-processed for the checking of missing values to avoid incorrect predictions and also for improvising the rate of accuracy. Further, SVM (Support Vector Machine), DT (Decision Tree) and RF (Random Forest) are used to perform classification. Finally, disease prediction is performed with the proposed instance-based quantum ML and DL method in which the number of qubits is computed with respect to features and optimized with instance-based learning. Additionally, a comparative assessment is provided for quantifying the differences between the standard classification algorithms with quantum-based learning in order to determine the significance of quantum-based detection in heart failure. From the results, the accuracy of the proposed system using instance-based quantum DL and instance-based quantum ML is found to be 98% and 83.6% respectively. [ABSTRACT FROM AUTHOR]

To build a secure cryptography system in the post-quantum era, one must find the minimum security parameters against quantum attacks by estimating the quantum resources of a fault-tolerant quantum computer. In a fault-tolerant quantum computer, errors must reach an acceptable level for practical uses according to error detection and error correction processes. However, these processes utilize additional quantum resources. As the depth of the quantum circuit increases, the computation time per qubit increases together with the processing errors. Therefore, in terms of errors in quantum circuits, it is a fundamental requirement to reduce the depth by trading off the number of qubits. This paper proposes novel low-depth SHA3 quantum circuit implementations for fault-tolerant quantum computers to reduce errors. The proposed SHA3 quantum circuit was implemented with the aim of optimizing the quantum circuit depth through a trade-off between the number of qubits, the quantum gate, and the quantum depth in each function. Compared to other state-of-art techniques, the proposed method achieved T-depth and full-depth reductions of 30.3% and 80.05%, respectively. We believe that this work will contribute to the establishment of minimum security parameters for SHA3 in the quantum era. [ABSTRACT FROM AUTHOR]

The signature is a behavioral-based human characteristics that is extensively used as legal evidence of identification on bank checks, credit cards, and wills. Developing a good offline signature verification (OSV) system is essential to avoid fraud. Due to the plurality of signature styles and sample contexts, feature extraction (FE) is challenging for OSV systems. Current signature verification methods present promising results in distinguishing between genuine and forged signatures, but they are far from satisfactory. Regarding feature selection (FS), stable and appropriate features substantially impact signature verification. The issue with current approaches is that they apply combination features without addressing correlation and discriminant. A genetic algorithm (GA) is popularly deployed as a method for optimization to identify the best features. Regrettably, GA has drawbacks when the search dimension is expanded, such as a lack of comprehensive exploitation, difficulties in achieving good convergence, and therefore increased computation. This study addresses the aforementioned challenges by introducing multifeature fusion and discriminant FS using a novel quantum inspired GA (QIGA). QIGAs are constructed using quantum computing concepts such as qubits (Q-bits) and superposition of states. QIGA is able to express a linear superposition of solutions with the objective of increasing gene diversity by utilizing the qubit as a representation. Quantum rotation computing (QRC) enhances population diversity and tackles computing complexity and early convergence issues in GA. The superposition concept in QRC utilizes both mutation (divergence) and crossover (convergence) procedures to expand diversity and improve population selection. QIGA performs better in a parallel structure because of its quick convergence and robust global search capabilities. This implies that QIGA embodies features of both exploration and exploitation. The studied approach is applied to three different databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Consequently, the proposed OSV based on the QIGA improved equal error rate (EER) between 10% and 20% over the GA without influencing computation complexity. [ABSTRACT FROM AUTHOR]

We study the influence of the phase-change material VO2 on transverse magnetic (TM) surface plasmon (SP) modes in metallic arm-chair graphene nanoribbon (AGNR) qubits in the Lindhard approximation. We assess the effects of temperature as a dynamic knob for the transition from the insulating to the metallic phase on the TM SP modes in single-band (SB) and two-band (TB) transitions. We show that a VO2 substrate leads to TM SP modes in both SB and TB transitions. In addition, we observe that the SP modes have a lower frequency than those for a substrate of constant permittivity. In addition, we study the influence of the substrate-induced band gap Δ ′ on SP modes in TB transitions for the insulating and metallic phases of VO2. [ABSTRACT FROM AUTHOR]

We introduce the quantum-house effect, a non-local quantum phenomenon which goes against classical intuition. We show how the effect can be achieved with any bipartite quantum state where neither subsystem is in a pure state. Besides its theoretical description, the quantum-house effect is also demonstrated on SpinQ Gemini, a 2-qubit liquid-state NMR desktop quantum computer. [ABSTRACT FROM AUTHOR]

Quantum computing is a modern technology that uses the laws of quantum mechanics to tackle issues like irreversibility and power dissipation, which are beyond the scope of traditional computing paradigms. To exploit quantum physics in many application fields, circuit design using reversible gates is a crucial task. In this paper, quantum implementation of three-input/three-output (3 × 3) reversible gates is presented. The functional matrices of most of the gates are presented for the first time in this paper. In addition, the quantum implementation of URG, FRSG1, R and JTF1 gates, which find importance in various practical applications, is presented for the first time in this paper. The paper concludes with a comparison of the performance parameters of reversible gates for efficient quantum circuit realization. It is shown that each gate has additional advantages in valid perspectives. The paper thus provides a useful library of 3 × 3 reversible gates for the implementation of higher-order quantum circuit design. [ABSTRACT FROM AUTHOR]

We report the step-by-step construction of the exact, closed and explicit expression for the evolution operator U (t) of a localized and isolated qubit in an arbitrary time-dependent field, which for concreteness we assume to be a magnetic field. Our approach is based on the existence of two independent dynamical invariants that enter the expression of S U (2) by means of two strictly related time-dependent, real or complex, parameters. The usefulness of our approach is demonstrated by exactly solving the quantum dynamics of a qubit subject to a controllable time-dependent field that can be realized in the laboratory. We further discuss possible applications to any S U (2) model, as well as the applicability of our method to realistic physical scenarios with different symmetry properties. [ABSTRACT FROM AUTHOR]

The radial motion of an electron in a Coulomb attracting field based on a new theory of the interaction of a particle with an electromagnetic field is studied. It is shown that the electron can perform radial oscillations due to a repulsive force that exists in the vicinity of the center and prevents the electron from approaching the center. This concept of a previously unknown repulsive force significantly shifts the paradigm of the interaction of a charge with an electromagnetic field. A laboratory experiment is proposed to test the new phenomenon.

Intensive research is currently being carried out to develop and create quantum computers and their software. This work is devoted to study of the influence of the environment on the quantum system of qubits. Quantum error correction is a set of methods for protecting quantum information and quantum state from unwanted interactions of the environment (decoherence) and other forms and types of noise. The article discusses the solution to the problem of research and development of corrective codes for rectifying several types of quantum errors that occur during computational processes in quantum algorithms and models of quantum computing devices. The aim of the work is to study existing methods for correcting various types of quantum errors and to create a corrective code for quantum error rectification. The scientific novelty is expressed in the exclusion of one of the shortcomings of the quantum computing process.

Quantum multi-hop teleportation is important in the field of quantum communication. In this paper, we proposed a Multi-hop quantum teleportation scheme of the 6-qubit entangled state. In the proposed scheme, the sender teleports the desired information by transmitting a 6-qubit entangled quantum state to the receiver through the N intermediate nodes between the source node and destination node. Therefore, the sender and receiver are not in direct connection with each other, and the information is teleported in Multi-hop between the N neighbor nodes. Also, in this scheme, the 7-qubit entangled states as quantum channels are shared between neighbor nodes. Always, teleportation processes over long distances are problematic because of the existence of the environmental noise which is almost inescapable and adversely affects the entangled quantum channel. This scheme explains the quantum teleportation between N nodes in N-1 steps and it is suitable for teleportation processes over long distances. Also, we calculate the efficiency of this scheme and indicate the advantages of this protocol.

Since the mention of the Fourth Industrial Revolution in 2016, quantum computers and quantum computing (QC) have emerged as key technologies. Many researchers are trying to realize quantum computers and quantum computing. In particular, most of the development and application of metaheuristics algorithms using quantum computing is focused on computer engineering fields. Cases in which the developed algorithm is applied to the optimal design of a building or the optimal design results presented by expanding the algorithm in various directions are very insufficient. Therefore, in this paper, we proposed four methods of adopting qubits to perform pitch adjusting in the optimization process of the QbHS (quantum-based harmony search) algorithm and applied it to TTO (truss topology optimization) using four methods to compare the results. The four methods of adopting qubits have the same or decreased number of qubits adopted as the number of iterations changes. As a result of applying TTO using four methods, convergence performance differed depending on the adoption method, and convergence performance was superior to conventional HS (harmony search) algorithms in all methods. The optimal design of structural engineering using such QC is expected to contribute to the revitalization of future technologies in the architectural field and the field of computer information systems.