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This research paper discusses the construction of novel and better quantum codes from the direct product of t -copies of ring R (discussed in Section 2), using cyclic codes over R by employing the CSS construction technique. Here, we present an overview of the structure and essential properties of a ring R. Furthermore, we analyze the distance-preserving nature of the Gray map in Subsection 2.1. Here, we also investigate maximum distance separable (MDS) codes by using the concept of quantum singleton defect (QSD), which indicates the overall quality of codes. To demonstrate the practicality of our findings, we provide illustrative examples implemented using the Magma software. [ABSTRACT FROM AUTHOR]

The computation of excited electronic states is an important application for quantum computers. In this work, we simulate the excited state spectra of four aromatic heterocycles on IBM superconducting quantum computers, focussing on active spaces of $ \pi \to \pi ^* $ π → π ∗ and $ n \to \pi ^* $ n → π ∗ excitations. We approximate the ground state with the entanglement forging method, a qubit reduction technique that maps a spatial orbital to a single qubit, rather than two qubits. We then determine excited states using the quantum subspace expansion method. We showcase these algorithms on quantum hardware using up to 8 qubits and employing readout and gate error mitigation techniques. Our results demonstrate a successful application of quantum computing in the simulation of active-space electronic wavefunctions of substituted aromatic heterocycles, and outline challenges to be overcome in elucidating the optical properties of organic molecules with hybrid quantum-classical algorithms. [ABSTRACT FROM AUTHOR]

Precision medicine techniques pinpoint the characteristics of people with uncommon treatment outcomes or distinct medical requirements. Artificial intelligence (AI) fuels the system's ability to think and learn, generates insights through complex computing and inference, and enhances clinical decision-making through enhanced intelligence. The main advantage of AI in sports medicine is its capacity for prediction. In order to forecast possible injuries, machine learning algorithms may examine enormous volumes of data, such as an athlete's training regimen, medical history, and performance indicators. A new area of study called photonic quantum information has emerged as a result of recent advancements in technology enabling the production, control, and detection of individual single photons. Realising single photon switches, creating photonic quantum circuits with specialised uses, and using new photonic states for optical metrology that goes beyond conventional optics are some examples of this advancement. Based on the author's previous and present efforts, the current state of photonic quantum information technology is reviewed in this review paper. Sports medicine professionals will need to have a basic working understanding of the strengths in the future, much way doctors presently need to understand the business of medicine. [ABSTRACT FROM AUTHOR]

Parallel developments in the realms of AI and quantum technology have shown a great deal of promise for mutual benefit. Combining them refers to using AI methods to create quantum computing (QC) and quantum physics algorithms as well as using QC to improve AI applications. QC has the power to revolutionise a number of industries. One of the biggest barriers preventing the broad application of QC is the famously challenging nature of controlling quantum systems. AI has made it possible to automate quantum system control in novel ways. In particular, the use of AI in conjunction with the Internet of Things (IoT) can potentially ease problems that have historically been connected to QC and quantum communication while revealing invaluable insight into the intricate and multifaceted field of quantum physics. However, QC may also be applied to improve AI applications. This study tries to examine how these technologies are used in the judicial system and how they affect courtroom behaviour. Accelerators are necessary for the quick and efficient execution of artificial intelligence activities across a variety of applications. Despite a half-century of study, general-purpose optical computing methods still haven't developed into a workable technology. However, optical computing methods may be able to address certain domain-specific demands. The focus of the article is on how these technologies may increase the efficacy and efficiency of law enforcement and the legal system, rather than providing a thorough examination of the legal system. [ABSTRACT FROM AUTHOR]

Innovation, miniaturisation, advancements in cutting-edge materials, the widespread availability of the internet, and the rise of wearable technology have all contributed to an increase in the quality of this gear. The field of wearable sensing technologies has recently shifted its focus towards monitoring vital signs for health and fitness. Bioelectrical, biophysical, and biochemical signals may be used in a variety of contexts, including athletic development, medical analysis and prevention, and rehabilitation. This study suggests a revolutionary method for detecting player health in sports medicine utilising optical sensors and quantum computing. In this case, we have employed a quantum convolutional learning-enabled naïve ResNet recurrent neural network (NResNetRNN_QCL) to analyse data collected from optical sensors. The experimental analysis is performed using metrics including training accuracy, mean average precision, root mean squared error, F-1 score, and area under the curve. The primary purpose of this study is to provide a comprehensive overview of the use of wearable technologies in the measurement of biomechanical and physiological properties. An F-1 score of 65%, RMSE of 59%, and area under the curve (AUC) of 59% were all achieved using the proposed method during training. [ABSTRACT FROM AUTHOR]

Analog computing is often considered to be an outdated technology only to be found and cared for in museums but not as the basis of modern cutting edge computing. Nothing could be further from reality. Together with quantum computers, analog computers complement today's stored program digital computers, acting as co-processors for certain types of computationally intensive tasks. They are highly energy efficient and exhibit a degree of parallelism that by far surpasses classic digital computers. In contrast to quantum computers, analog computers do not require exotic technologies, no intricate cooling systems, no elaborate shielding systems, etc. In addition to this there already exists a plethora of proven programs for analog computers to solve a wide variety of problems. This paper describes the necessity of analog computing for the twenty-first century, gives a short introduction to the structure and programming of analog computers, before discussing future application areas. Analog computing will drastically alter the way we think about computing in the years to come as it will become an integral part of every high performance computer. It will be an indispensable technology for future AI systems, it will revolutionize many medical applications, it might even replace or at least complement, certain types of quantum computers, etc. [ABSTRACT FROM AUTHOR]

Quantum walks have proven to be a universal model for quantum computation and to provide speed-up in certain quantum algorithms. The discrete-time quantum walk (DTQW) model, among others, is one of the most suitable candidates for circuit implementation due to its discrete nature. Current implementations, however, are usually characterized by quantum circuits of large size and depth, which leads to a higher computational cost and severely limits the number of time steps that can be reliably implemented on current quantum computers. In this work, we propose an efficient and scalable quantum circuit implementing the DTQW on the 2 n -cycle based on the diagonalization of the conditional shift operator. For t time steps of the DTQW, the proposed circuit requires only O (n 2 + n t) two-qubit gates compared to the O (n 2 t) of the current most efficient implementation based on quantum Fourier transforms. We test the proposed circuit on an IBM quantum device for a Hadamard DTQW on the 4-cycle and 8-cycle characterized by periodic dynamics and by recurrent generation of maximally entangled single-particle states. Experimental results are meaningful well beyond the regime of few time steps, paving the way for reliable implementation and use on quantum computers. [ABSTRACT FROM AUTHOR]

The progression in quantum computing and the rapid development of quantum computation hardware has raised expectations for its application to commercially relevant use cases in the future. However, the need for high-level quantum programming abstractions and targeted use cases paired with vertical applications, which can directly benefit from quantum computing, remains an open challenge. This paper presents our vision for a decentralized architecture for swarm based IoT systems that leverages a high-level continuous development and integration quantum programming suite to support edge processing capabilities for different use cases across the edge-fog-cloud continuum. The planned Quantum DevKit provides the necessary abstractions and low-level backend interfaces for quantum computing infrastructure, enabling edge computation using quantum processing, with extensions to efficient management of Service Level Agreements (SLAs). The paper focuses on the presentation of the development kit and its coupling swarm-based architecture that automates the orchestration of the cloud-to-edge continuum, showcasing the potential of quantum technology in edge processing. [ABSTRACT FROM AUTHOR]

Over the past few decades, THz technology has made considerable progress, evidenced by the performance of current THz sources and detectors, as well as the emergence of several THz applications. However, in the realm of quantum technologies, the THz spectral domain is still in its infancy, unlike neighboring spectral domains that have flourished in recent years. Notably, in the microwave domain, superconducting qubits currently serve as the core of quantum computers, while quantum cryptography protocols have been successfully demonstrated in the visible and telecommunications domains through satellite links. The THz domain has lagged behind in these impressive advancements. Today, the current gap in the THz domain clearly concerns quantum technologies. Nonetheless, the emergence of quantum technologies operating at THz frequencies will potentially have a significant impact. Indeed, THz radiation holds significant promise for wireless communications with ultimate security owing to its low sensitivity to atmospheric disturbances. Moreover, it has the potential to raise the operating temperature of solid-state qubits, effectively addressing existing scalability issues. In addition, THz radiation can manipulate the quantum states of molecules, which are recognized as new platforms for quantum computation and simulation with long range interactions. Finally, its ability to penetrate generally opaque materials or its resistance to Rayleigh scattering are very appealing features for quantum sensing. In this perspective, we will discuss potential approaches that offer exciting prospects for generating and detecting non-classical states of THz light, thereby opening doors to significant breakthroughs in THz quantum technologies. [ABSTRACT FROM AUTHOR]

Quantum network simulators offer the opportunity to cost-efficiently investigate potential avenues for building networks that scale with the number of users, communication distance, and application demands by simulating alternative hardware designs and control protocols. Several quantum network simulators have been recently developed with these goals in mind. As the size of the simulated networks increases, however, sequential execution becomes time-consuming. Parallel execution presents a suitable method for scalable simulations of large-scale quantum networks, but the unique attributes of quantum information create unexpected challenges. In this work, we identify requirements for parallel simulation of quantum networks and develop the first parallel discrete-event quantum network simulator by modifying the existing serial simulator SeQUeNCe. Our contributions include the design and development of a quantum state manager (QSM) that maintains shared quantum information distributed across multiple processes. We also optimize our parallel code by minimizing the overhead of the QSM and decreasing the amount of synchronization needed among processes. Using these techniques, we observe a speedup of 2 to 25 times when simulating a 1,024-node linear network topology using 2 to 128 processes. We also observe an efficiency greater than 0.5 for up to 32 processes in a linear network topology of the same size and with the same workload. We repeat this evaluation with a randomized workload on a caveman network. We also introduce several methods for partitioning networks by mapping them to different parallel simulation processes. We have released the parallel SeQUeNCe simulator as an open source tool alongside the existing sequential version. [ABSTRACT FROM AUTHOR]

Quantum computers provide a promising method to study the dynamics of many‐body systems beyond classical simulation. On the other hand, the analytical methods developed and results obtained from the integrable systems provide deep insights on the many‐body system. Quantum simulation of the integrable system not only provides a valid benchmark for quantum computers but is also the first step in studying integrable‐breaking systems. The building block for the simulation of an integrable system is the Yang–Baxter gate. It is vital to know how to optimally realize the Yang–Baxter gates on quantum computers. Based on the geometric picture of the Yang–Baxter gates, the optimal realizations of two types of Yang–Baxter gates with a minimal number of controlled NOT (CNOT) or Rzz$R_{zz}$ gates are presented. It is also shown how to systematically realize the Yang–Baxter gates via the pulse control. The different realizations on IBM quantum computers are tested and compared. It is found that the pulse realizations of the Yang–Baxter gates always have a higher gate fidelity compared to the optimal CNOT or Rzz$R_{zz}$ realizations. On the basis of the above optimal realizations, the simulation of the Yang–Baxter equation on quantum computers is demonstrated. These results provide a guideline and standard for further experimental studies based on the Yang–Baxter gate. [ABSTRACT FROM AUTHOR]

Quantum computing (QC) holds promise for next-generation applications that requires exponential time to solve NP problems. However, even after 25+ years, researchers still struggle with stable error correction solutions for delivering a stable commercialised quantum universal quantum machine. Companies like IBM, Rigetti, and D-Wave have already started offering quantum annealers, intermediate quantum devices based on the qubit. Therefore, researchers believe that QC would be introduced as an extension of classical computers (CC), where CC would offload a specific computational intensive task. Cloud companies, including Amazon, Azure, and IBM, have already begun to provide cloud services where an organisation can use a QC to work on potential applications. This paper systematically analyses the need to provide QC as a service (QCaaS) for potential applications. Furthermore, we discuss the framework on which this service can be offered. Subsequently, we provide a detailed discussion of various toolkits, libraries, and simulators available for QC. [ABSTRACT FROM AUTHOR]

As restricted quantum computers become available, research focuses on finding meaningful applications. For example, in quantum machine learning, a special type of quantum circuit called a quantum neural network is one of the most investigated approaches. However, we know little about suitable circuit architectures or important model hyperparameters for a given task. In this work, we apply the functional ANOVA framework to the quantum neural network architectures to analyze which of the quantum machine learning hyperparameters are most influential for their predictive performance. We restrict our study to 7 open-source datasets from the OpenML-CC18 classification benchmark, which are small enough for simulations on quantum hardware with fewer than 20 qubits. Using this framework, three main levels of importance were identified, confirming expected patterns and revealing new insights. For instance, the learning rate is identified as the most important hyperparameter on all datasets, whereas the particular choice of entangling gates used is found to be the least important on all except for one dataset. In addition to identifying the relevant hyperparameters, for each of them, we also learned data-driven priors based on values that perform well on previously seen datasets, which can then be used to steer hyperparameter optimization processes. We utilize these priors in the hyperparameter optimization method hyperband and show that these improve performance against uniform sampling across all datasets by, on average, 0.53 % , up to 6.11 % , in cross-validation accuracy. We also demonstrate that such improvements hold on average regardless of the configuration hyperband is run with. Our work introduces new methodologies for studying quantum machine learning models toward quantum model selection in practice. All research code is made publicly available. [ABSTRACT FROM AUTHOR]

The construction of an all-optical frequency-encoded Toffoli gate employing a reflecting semiconductor optical amplifier (RSOA) is proposed in this article. By establishing fields such as quantum computing, optical quantum computing, quantum-dot cellular automata, and superconducting flux logic family, quantum gates have been proved to perform reliably in the present day. A nonzero-mass electron, on the other hand, moves far slower than a quantum particle with zero rest mass, such as a photon. Photons can also be utilized to store data while being sent. These photon qualities have motivated researchers to create quantum gates in the all-optical domain based on them. The RSOA-based implementation of the Toffoli gate gives a significant improvement in the case of high speed, low power, and fast switching time. MATLAB Simulink (R2018a) software is used to simulate the devised design. The theoretical prediction is satisfied by the simulation results. [ABSTRACT FROM AUTHOR]

Majorana bound states are quasiparticle excitations localized at the boundaries of a topologically nontrivial superconductor. They are zero-energy, charge-neutral, particle–hole symmetric, and spatially-separated end modes which are topologically protected by the particle–hole symmetry of the superconducting state. Due to their topological nature, they are robust against local perturbations and, in an ideal environment, free from decoherence. Furthermore, unlike ordinary fermions and bosons, the adiabatic exchange of Majorana modes is noncommutative, i.e., the outcome of exchanging two or more Majorana modes depends on the order in which exchanges are performed. These properties make them ideal candidates for the realization of topological quantum computers. In this tutorial, I will present a pedagogical review of 1D topological superconductors and Majorana modes in quantum nanowires. I will give an overview of the Kitaev model and the more realistic Oreg–Lutchyn model, discuss the experimental signatures of Majorana modes, and highlight their relevance in the field of topological quantum computation. This tutorial may serve as a pedagogical and relatively self-contained introduction for graduate students and researchers new to the field, as well as an overview of the current state-of-the-art of the field and a reference guide to specialists. [ABSTRACT FROM AUTHOR]

In this paper, we have developed a unitary variant of a double exponential coupled cluster theory, which is capable of handling molecular strong correlation with arbitrary electronic complexity. With the Hartree–Fock determinant taken as the reference, we introduce a sequential product of parameterized unitary Ansätze. While the first unitary, containing the excitation operators, acts directly on the reference determinant, the second unitary, containing a set of rank-two, vacuum-annihilating scattering operators, has nontrivial action only on certain entangled states. We demonstrate the theoretical bottleneck of such an implementation in a classical computer, whereas the same is implemented in the hybrid quantum–classical variational quantum eigensolver framework with a reasonably shallow quantum circuit without any additional approximation. We have further introduced a number of variants of the proposed Ansatz with different degrees of sophistication by judiciously approximating the scattering operators. With a number of applications on strongly correlated molecules, we have shown that all our schemes can perform uniformly well throughout the molecular potential energy surface without significant additional implementation cost over the conventional unitary coupled cluster approach with single and double excitations. [ABSTRACT FROM AUTHOR]

Quantum simulation of chemical Hamiltonians enables the efficient calculation of chemical properties. Mapping is one of the essential steps in simulating fermionic systems on quantum computers. In this work, a unified framework of transformations mapping fermionic systems to qubit systems is presented and many existing transformations—such as Jordan–Wigner, Bravyi–Kitaev, and parity transformations—are included in this framework. Based on this framework, the Multilayer Segmented Parity (MSP) transformation is proposed. The MSP transformation is a general mapping with an adjustable parameter vector, which can be viewed as a generalization of the above-mentioned mappings. Furthermore, the MSP transformation can adjust flexibly when dealing with different systems. Applying these mappings to the electronic structure Hamiltonians of various molecules, the MSP transformation is found to perform better on a number of Pauli operators and gates needed in the circuit of Hamiltonian simulation. The MSP transformation will reduce the qubit gate requirement for Hamiltonian simulation on noisy intermediate-scale quantum devices, and it will provide a much wider choice of mappings for researchers. [ABSTRACT FROM AUTHOR]

Abstract An elementary review on principles of qubits and their prospects for quantum computing is provided. Due to its rapid development, quantum computing has attracted considerable attention as a core technology for the next generation and has demonstrated its potential in simulations of exotic materials, molecular structures, and theoretical computer science. To achieve fully error-corrected quantum computers, building a logical qubit from multiple physical qubits is crucial. The number of physical qubits needed depends on their error rates, making error reduction in physical qubits vital. Numerous efforts to reduce errors are ongoing in both existing and emerging quantum systems. Here, the principle and development of qubits, as well as the current status of the field, are reviewed to provide information to researchers from various fields and give insights into this promising technology.

The quest for quantum advantage, wherein quantum computers surpass the computational capabilities of classical computers executing state-of-the-art algorithms on well-defined tasks, represents a pivotal race in the domain of quantum computing. NISQ (Noisy Intermediate-Scale Quantum) computing has witnessed remarkable advancements, culminating in significant milestones on the journey towards the realization of universal fault-tolerant quantum computers. This transformative turning point, known as quantum supremacy, has been achieved amid a series of breakthroughs, signifying the dawn of the quantum era. Quantum hardware has undergone substantial integration and architectural evolution, contrasting with its nascent stages. In this review, we critically examine the quantum supremacy experiments conducted thus far, shedding light on their implications and contributions to the evolving landscape of quantum computing. Additionally, we endeavor to illuminate a range of cutting-edge proof-of-principle investigations in the realm of applied quantum computing, providing an insightful overview of the current state of applied quantum research and its prospective influence across diverse scientific, industrial, and technological frontiers.

This article examines the current status of quantum computing (QC) in Earth observation and satellite imagery. We analyze the potential limitations and applications of quantum learning models when dealing with satellite data, considering the persistent challenges of profiting from quantum advantage and finding the optimal sharing between high-performance computing (HPC) and QC. We then assess some parameterized quantum circuit models transpiled into a Clifford+T universal gate set. The T-gates shed light on the quantum resources required to deploy quantum models, either on an HPC system or several QC systems. In particular, if the T-gates cannot be simulated efficiently on an HPC system, we can apply a quantum computer and its computational power over conventional techniques. Our quantum resource estimation showed that quantum machine learning (QML) models, with a sufficient number of T-gates, provide the quantum advantage if and only if they generalize on unseen data points better than their classical counterparts deployed on the HPC system and they break the symmetry in their weights at each learning iteration like in conventional deep neural networks. We also estimated the quantum resources required for some QML models as an initial innovation. Lastly, we defined the optimal sharing between an HPC+QC system for executing QML models for hyperspectral satellite images. These are a unique dataset compared with other satellite images since they have a limited number of input quantum bits and a small number of labeled benchmark images, making them less challenging to deploy on quantum computers.

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]

We demonstrate an implementation of the precise test of dimension on the qubit, using the public IBM quantum computer, using the determinant dimension witness. The accuracy is below 10−3 comparing to maximal possible value of the witness in higher dimension. The test involving minimal independent sets of preparation and measurement operations (gates) is applied both for specific configurations and parametric ones. The test is robust against nonidealities such as incoherent leakage and erroneous gate execution. Two of the IBM devices failed the test by more than 5 standard deviations, which has no simple explanation. [ABSTRACT FROM AUTHOR]

An elementary review on principles of qubits and their prospects for quantum computing is provided. Due to its rapid development, quantum computing has attracted considerable attention as a core technology for the next generation and has demonstrated its potential in simulations of exotic materials, molecular structures, and theoretical computer science. To achieve fully error-corrected quantum computers, building a logical qubit from multiple physical qubits is crucial. The number of physical qubits needed depends on their error rates, making error reduction in physical qubits vital. Numerous efforts to reduce errors are ongoing in both existing and emerging quantum systems. Here, the principle and development of qubits, as well as the current status of the field, are reviewed to provide information to researchers from various fields and give insights into this promising technology. [ABSTRACT FROM AUTHOR]

This work presents a novel approach to quantum computing by proposing a customizable hardware design of a dedicated processor that emulates the execution of quantum algorithms. Unlike software-based quantum computation simulators, which run on standard general-purpose computers and suffer from reduced performance, this hardware design, which is based on classical concepts of bits, registers and memories, aims to leverage pure parallelism and pipelined execution for efficient quantum computations via emulation. The architecture includes several key components: memories, computation unit, measurement unit and control unit. The quantum state memory stores the individual and group states of qubits. This memory is crucial for maintaining the quantum information required for quantum operations. Basic operators are stored in dedicated operator memory. Additionally, a scratch memory allows for larger operators to be dynamically built at runtime. The computation unit is responsible for performing complex number multiplications, which form the basis of tensor and matrix products necessary for executing quantum operations. A measurement unit enables quantum state sampling, which is an essential aspect of quantum computation. Furthermore, a control unit is incorporated to ensure the correct operation of the quantum processor's data path. It utilizes a microprogram to manage and coordinate the functional units. All the functional units communicate with each other through dedicated and shared data buses, depending on the frequency of information exchange. This enables efficient data transfer and coordination among the components. The proposed hardware design has been simulated and proved to be effective in executing quantum operations. By exploiting parallelism and employing a pipelined execution, this architecture overcomes the limitations of software-based simulators, delivering improved performance for emulating quantum algorithms. We use Grover's search algorithm as a benchmark to evaluate the performance of the proposed hardware design and compare it to software-based simulation and to hardware-based algorithm-dedicated emulation. [ABSTRACT FROM AUTHOR]

With the recent advances in experimental realization of multi-qubit systems the idea of delegating certain real-life optimization tasks to a quantum computer becomes viable. In particular, we herein examine a variational quantum algorithm applied to a three-dimensional problem of open-pit mining, where the core of the classical optimization loop is provided by the probabilistic tensor sampling method. The developed technique is challenged against conventional optimization routines subjected to the essential optimization issues in variational quantum algorithms such as barren plateaus and multiple local minima. We demonstrate that the probabilistic tensor train-based approach allows one to steadily identify the ground state of a given system. [ABSTRACT FROM AUTHOR]

The properties of strongly-coupled lattice gauge theories at finite density as well as in real time have largely eluded first-principles studies on the lattice. This is due to the failure of importance sampling for systems with a complex action. An alternative to evade the sign problem is quantum simulation. Although still in its infancy, a lot of progress has been made in devising algorithms to address these problems. In particular, recent efforts have addressed the question of how to produce thermal Gibbs states on a quantum computer. In this study, we apply a variational quantum algorithm to a low-dimensional model which has a local abelian gauge symmetry. We demonstrate how this approach can be applied to obtain information regarding the phase diagram as well as unequal-time correlation functions at non-zero temperature. [ABSTRACT FROM AUTHOR]

Information spreads in time. For example, correlations dissipate when the correlated system locally couples to a third party, such as the environment. This simple but important fact forms the known quantum data-processing inequality. Here we theoretically uncover the quantum fluctuation theorem behind the quantum informational inequality. The fluctuation theorem quantitatively predicts the statistics of the underlying stochastic quantum process. To fully capture the quantum nature, the fluctuation theorem established here is extended to the quasiprobability regime. We also experimentally apply an interference-based method to measure the amplitudes composing the quasiprobability and verify our established fluctuation theorem by the IBM quantum computer. With the increase of the use of quantum information in everyday life, its dynamics requires deeper understanding. Here, the quantum fluctuation theorem, that accounts for a quantum system interacting with the environment, is established within the framework of quasi-probability, a powerful tool for studying quantum statistics. [ABSTRACT FROM AUTHOR]

We propose a quantum processor architecture, the qubit 'pipeline', in which run-time scales additively as functions of circuit depth and run repetitions. Run-time control is applied globally, reducing the complexity of control and interconnect resources. This simplification is achieved by shuttling N-qubit states through a large layered physical array of structures which realise quantum logic gates in stages. Thus, the circuit depth corresponds to the number of layers of structures. Subsequent N-qubit states are 'pipelined' densely through the structures to efficiently wield the physical resources for repeated runs. Pipelining thus lends itself to noisy intermediate-scale quantum (NISQ) applications, such as variational quantum eigensolvers, which require numerous repetitions of the same or similar calculations. We illustrate the architecture by describing a realisation in the naturally high-density and scalable silicon spin qubit platform, which includes a universal gate set of sufficient fidelity under realistic assumptions of qubit variability. [ABSTRACT FROM AUTHOR]

The fulcrum of this work is knowledge: what it is and how it is generated within the context of a capitalist society. First, Marx’s analysis of the objective labour process is extended to the mental labour process. Then, objective and mental labour processes are defined in terms of objective and mental transformations, with consideration paid to which of the two types of transformation is determinant. This requires a discussion of dialectical logic and formal logic. Within dialectical logic, two types of processes are introduced: open ended and pre-determined. It is argued that computers (both traditional and quantum) and Artificial Intelligence cannot generate new knowledge, because they (a) rely on formal logic, i.e. they cannot engage in open-ended dialectical processes, and (b) are impervious to social determination. Connectedly, Artificial Intelligence systems such as ChatGPT cannot be a substitute for human thought or writing, because of the inevitability of ‘model collapse’. Next, focus is shifted to a specific form of knowledge: the ‘Copenhagen interpretation’ of quantum mechanics. It is shown that this interpretation is steeped in irrationalism and that it is a variant of pro-capitalist ideology. Finally, the social-historical roots of this ideology are revealed. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications. [ABSTRACT FROM AUTHOR]

To defend against quantum computer attacks, the National Institute of Standards and Technology (NIST) has been exploring post-quantum cryptography (PQC). Now, NIST has standardized only two PQC algorithms, one of which is the Leighton-Micali signature (LMS). However, the performance of LMS limits its practical application. In this paper, we propose a parallel LMS implementation on multiple nodes. Considering different application scenarios, we provide two parallel schemes: algorithmic parallelism and data parallelism. The main part of our work is the two-tier parallel structure for the LMS tree. Targeting the x86/64 multiple nodes, our work introduces vectorization to present the three-tier parallel structure. We also design communication optimization, including the selection of communication primitives and the creation of communicators for multi-node running. Experimental evidence shows that our code effectively reduces the latency, and is 19.04 × faster than the fastest implementation on the same platform when running key pair generation for LMS_SHA256_M32_H20(20). [ABSTRACT FROM AUTHOR]

Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks. However, they require extensive computational resources both for training and deployment. The problem is exacerbated as the amount and complexity of the data increases. Quantum-based vision transformer models could potentially alleviate this issue by reducing the training and operating time while maintaining the same predictive power. Although current quantum computers are not yet able to perform high-dimensional tasks, they do offer one of the most efficient solutions for the future. In this work, we construct several variations of a quantum hybrid vision transformer for a classification problem in high-energy physics (distinguishing photons and electrons in the electromagnetic calorimeter). We test them against classical vision transformer architectures. Our findings indicate that the hybrid models can achieve comparable performance to their classical analogs with a similar number of parameters. [ABSTRACT FROM AUTHOR]

Photonic interconnects between quantum systems will play a central role in both scalable quantum computing and quantum networking. Entanglement of remote qubits via photons has been demonstrated in many platforms; however, improving the rate of entanglement generation will be instrumental for integrating photonic links into modular quantum computers. We present an ion trap system that has the highest reported free-space photon collection efficiency for quantum networking. We use a pair of in-vacuum aspheric lenses, each with a numerical aperture of 0.8, to couple 10(1)% of the 493 nm photons emitted from a 138Ba+ ion into single-mode fibers. We also demonstrate that proximal effects of the lenses on the ion position and motion can be mitigated. [ABSTRACT FROM AUTHOR]

Quantum blockchain (QBC) is a novel decentralised concept anticipated to offer an alternative to the classical blockchain to provide transaction security and transparency. The QBC frameworks can offer the most tangible advantage against the security threat posed by quantum computers on the classical blockchain. The proposed scheme offers a new QBC framework in which voting is performed by the Quantum-Secured Yet Another Consensus (QSYAC) algorithm to create a fast decentralised QBC. QSYAC algorithm is also used to ensure the reliability and fault tolerance of the blockchain framework. The classical information and the chaining are provided using a single qubit state and quantum entanglement. The transactions are signed via a cyclic permutation of the computational distinguishability of the quantum states problem, and quantum key distribution protocol is used for secure key sharing. Assuring the security of the key and the blockchain, the suggested model is more effective and safe from potential quantum assaults than earlier systems. [ABSTRACT FROM AUTHOR]

Quantum multidimensional scaling is a quantum dimensionality reduction algorithm. Its complex quantum circuit design structure and excessive qubits consumption make it difficult to run on the current quantum computers. In order to solve this problem, this paper proposes the variational quantum multidimensional scaling algorithm based on the variational quantum algorithm. Utilizing the parallel advantages of quantum computing to quickly compute low-dimensional embeddings of high-dimensional data, the variational quantum multidimensional scaling algorithm can provide lower time complexity; compared with the non-variational quantum multidimensional scaling algorithm, the variational quantum multidimensional scaling algorithm provides a simpler quantum circuit. In the noisy intermediate scale quantum era, the algorithm can run on a quantum computer. In addition, the article finally implemented the variational quantum multidimensional scaling algorithm on the Qiskit framework, proving the correctness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper promotes reinforcement machine learning for route-finding tasks in quantum communication networks, where, due to the non-additivity of quantum errors, classical graph path or tree-finding algorithms cannot be used. We propose using a proximal policy optimization algorithm capable of finding routes in teleportation-based quantum networks. This algorithm is benchmarked against the Monte Carlo search. The topology of our network resembles the proposed 6 G topology and analyzed that quantum errors correspond to typical errors in realistic quantum channels. [ABSTRACT FROM AUTHOR]

Classical encryption protocols that are currently used to secure the internet and transmission control protocol/internet protocol (TCP/IP) protocols will be subjected to high risks with the development and expansion of quantum computers with high processing power. Therefore, in recent years, secure quantum communication has been suggested as an alternative solution. Quantum secure communications based on quantum keys in fiber optics have high potential in modern communications. However, for designing a quantum key distribution (QKD) network, there will be widespread issues and problems that must be resolved in a way to establish secure and reliable communication between the two communication nodes in the network. Therefore, in this paper, for the first time, a seven-layer reference model called QTCP/IP is proposed in the partially trusted-node-based QKD-secured optical networks. In the proposed model, the layers are divided by functional independence, and each has protocols that specify its performance. In addition, the QTCP/IP model provides a practical solution for secure communication in current networks by being compatible with the TCP/IP model. Furthermore, in this proposed model, a QKD service is supplied, which provides quantum keys to the end users and the applications on demand to establish timely security requirements. Moreover in this reference model, a dynamic routing algorithm is proposed for transferring quantum keys. Our work has the potential to become a reference standard for a practical QKD network. [ABSTRACT FROM AUTHOR]

The emergence of quantum computers is having an impact on almost every field of engineering. Electrical drives, in particular, can benefit from the power of quantum computing. This paper presents a quantum version of the pulse-width modulation (PWM) algorithm, which is used widely to control electrical motors. The proposed algorithm is implemented using a real number comparator, which is novel to the authors' knowledge. Unlike other quantum comparators in the literature, which provide quantum versions of binary-based comparators, the suggested comparator compares real numbers ranging from 0 to 100%, making it more usable in practice for many industrial applications, particularly control. When applied to DC motor speed control, the quantum PWM controller achieved similar speed precision to the classical version, but with lower energy consumption and less switching. It could therefore significantly increase the lifetime of power devices while achieving similar control performance at lower energy consumption. [ABSTRACT FROM AUTHOR]

Quantum abstract detecting systems (QADS) provide a common framework to address detection problems in quantum computers. A particular QADS family, that of combinatorial QADS, has been proved to be useful for decision problems on eigenvalues or phase estimation methods. In this paper, we consider functional QADS, which not only have interesting theoretical properties (intrinsic detection ability, relation to the QFT), but also yield improved decision and phase estimation methods, as compared to combinatorial QADS. A first insight into the comparison with other phase estimation methods also shows promising results. [ABSTRACT FROM AUTHOR]