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Trapped ions are among the leading candidates for quantum computing technologies. Interfacing ion qubits in separate traps and interfacing ion qubits with superconducting qubits are two of the many challenges to scale up quantum computers. One approach to overcome both problems is to use a conducting wire to mediate the Coulomb interaction between ions in different traps or between ions and superconducting qubits. To this end, a trapped charged particle inducing charge on a conductor has long been modeled as a system of equivalent lumped element electronic components. Our careful consideration reveals two assumptions in the derivation of this model, which are generally unjustified. We explain how these assumptions undermine the model's validity, and then introduce an improved way to use linear relationships to describe the interaction of trapped ions with nearby conductors. The new method reproduces results equivalent to those of other works that are not based on the circuit element model. This method is targeted for trouble-shooting experimental designs and allows experiments to test and compare the accuracy of different theoretical models. [ABSTRACT FROM AUTHOR]

Quantum chemistry is one of the most promising applications for which quantum computing is expected to have a significant impact. Despite considerable research in the field of electronic structure, calculating the vibrational properties of molecules on quantum computers remains a relatively unexplored field. In this work, we develop a vibrational Adaptive Derivative-Assembled Pseudo-Trotter Variational Quantum Eigensolver (vADAPT-VQE) formalism based on an infinite product representation (IPR) of anti-Hermitian excitation operators of the Full Vibrational Configuration Interaction (FVCI) wavefunction, which allows for preparing eigenstates of vibrational Hamiltonians on quantum computers. In order to establish the vADAPT-VQE algorithm using the IPR, we study the exactness of disentangled Unitary Vibrational Coupled Cluster (dUVCC) theory and show that dUVCC can formally represent the FVCI wavefunction in an infinite expansion. To investigate the performance of the vADAPT-VQE algorithm, we numerically study whether the vADAPT-VQE algorithm generates a sequence of operators that may represent the FVCI wavefunction. Our numerical results indicate frequent appearance of critical points in the wavefunction preparation using vADAPT-VQE. These results imply that one may encounter diminishing usefulness when preparing vibrational wavefunctions on quantum computers using vADAPT-VQE and that additional studies are required to find methods that can circumvent this behavior. [ABSTRACT FROM AUTHOR]

The electronic energies of molecules have been successfully evaluated on quantum computers. However, more attention is paid to the dynamics simulation of molecules in practical applications. Based on the variational quantum eigensolver (VQE) algorithm, Fedorov et al. proposed a correlated sampling (CS) method and demonstrated the vibrational dynamics of H2 molecules [J. Chem. Phys. 154, 164103 (2021)]. In this study, we have developed a quantum approach by extending the CS method based on the VQE algorithm (labeled eCS-VQE) for simulating chemical reaction dynamics. First, the CS method is extended to the three-dimensional cases for calculation of first-order energy gradients, and then, it is further generalized to calculate the second-order gradients of energies. By calculating atomic forces and vibrational frequencies for H2, LiH, H+ + H2, and Cl− + CH3Cl systems, we have seen that the approach has achieved the CCSD level of accuracy. Thus, we have simulated dynamics processes for two typical chemical reactions, hydrogen exchange and chlorine substitution, and obtained high-precision reaction dynamics trajectories consistent with the classical methods. Our eCS-VQE approach, as measurement expectations and ground-state wave functions can be reused, is less demanding in quantum computing resources and is, therefore, a feasible means for the dynamics simulation of chemical reactions on the current noisy intermediate-scale quantum-era quantum devices. [ABSTRACT FROM AUTHOR]

The operations of current quantum computers are still significantly affected by decoherence caused by interaction with the environment. In this work, we employ the non-perturbative hierarchical equations of motion (HEOM) method to simulate the operation of model quantum computers and reveal the effects of dissipation on the entangled quantum states and on the performance of well-known quantum algorithms. Multi-qubit entangled states in Shor's factorizing algorithm are first generated and propagated using the HEOM. It is found that the failure of factorization is accompanied by a loss of fidelity and mutual information. An important challenge in using the HEOM to simulate quantum computers in a dissipative environment is how to efficiently treat systems with many qubits. We propose a two-dimensional tensor network scheme for this problem and demonstrate its capability by simulating a one-dimensional random circuit model with 21 qubits. [ABSTRACT FROM AUTHOR]

The recently developed Projective Quantum Eigensolver (PQE) offers an elegant procedure to evaluate the ground state energies of molecular systems in quantum computers. However, the noise in available quantum hardware can result in significant errors in computed outcomes, limiting the realization of quantum advantage. Although PQE comes equipped with some degree of inherent noise resilience, any practical implementation with apposite accuracy would require additional routines to eliminate or mitigate the errors further. In this work, we propose a way to enhance the efficiency of PQE by developing an optimal framework for introducing Zero Noise Extrapolation (ZNE) in the nonlinear iterative procedure that outlines the PQE, leading to the formulation of ZNE-PQE. Moreover, we perform a detailed analysis of how various components involved in it affect the accuracy and efficiency of the reciprocated energy convergence trajectory. Additionally, we investigate the underlying mechanism that leads to the improvements observed in ZNE-PQE over conventional PQE by performing a comparative analysis of their residue norm landscape. This approach is expected to facilitate practical applications of quantum computing in fields related to molecular sciences, where it is essential to determine molecular energies accurately. [ABSTRACT FROM AUTHOR]

First-order derivatives of energies with respect to atomic coordinates are widely computed and used in quantum chemistry simulations. The rapidly emerging technology of quantum computing offers a new paradigm for solving relevant quantum chemistry equations. In this work, we have achieved analytical calculations of atomic forces based on the Hellmann–Feynman theorem within the framework of the variational quantum eigensolver. The accuracy of the approach is demonstrated by calculating the atomic forces of H2, LiH, H2O, and NH3 molecules, which are in excellent agreement with values obtained from full configuration interaction calculations. In particular, for systems with degenerate molecular orbitals, the analytical approach has a significant accuracy advantage over finite-difference-based methods and will not involve additional computational effort on a quantum computer. The calculated forces are further used to optimize the geometries of NH3 and CH4 molecules and to perform ab initio molecular dynamics simulations for the umbrella inversion of NH3, demonstrating the feasibility of the approach in practical quantum chemistry simulations. [ABSTRACT FROM AUTHOR]

In this work, we test a recently developed method to enhance classical auxiliary-field quantum Monte Carlo (AFQMC) calculations with quantum computers against examples from chemistry and material science, representative of classes of industry-relevant systems. As molecular test cases, we calculate the energy curve of H4 and the relative energies of ozone and singlet molecular oxygen with respect to triplet molecular oxygen, which is industrially relevant in organic oxidation reactions. We find that trial wave functions beyond single Slater determinants improve the performance of AFQMC and allow it to generate energies close to chemical accuracy compared to full configuration interaction or experimental results. In the field of material science, we study the electronic structure properties of cuprates through the quasi-1D Fermi–Hubbard model derived from CuBr2, where we find that trial wave functions with both significantly larger fidelities and lower energies over a mean-field solution do not necessarily lead to AFQMC results closer to the exact ground state energy. [ABSTRACT FROM AUTHOR]

A major roadblock to scalable quantum computing is phase decoherence and energy relaxation caused by qubits interacting with defect-related two-level systems (TLSs). Native oxides present on the surfaces of superconducting metals used in quantum devices are acknowledged to be a source of TLS that decrease qubit coherence times. Reducing microwave loss by "surface engineering" (i.e., replacing the uncontrolled native oxide of superconducting metals with a thin, stable surface with predictable characteristics) can be a key enabler for pushing performance forward with devices of higher intrinsic quality factor. In this work, we present a novel approach to replace the native oxide of niobium (typically formed in an uncontrolled fashion when its pristine surface is exposed to air) with an engineered oxide, using a room-temperature process that leverages accelerated neutral atom beam (ANAB) technology at 300 mm wafer scale. This ANAB is composed of a mixture of argon and oxygen, with tunable energy per atom, which is rastered across the wafer surface. The ANAB-engineered Nb-oxide thickness was found to vary from 2 to 6 nm depending on ANAB process parameters. The modeling of variable-energy x-ray photoelectron spectroscopy data confirms the thickness and compositional control of Nb surface oxide by the ANAB process. These results correlate well with those from transmission electron microscopy and x-ray reflectometry. Since ANAB is broadly applicable to material surfaces, the present study indicates its promise for modification of the surfaces of superconducting quantum circuits to achieve longer coherence times. [ABSTRACT FROM AUTHOR]

Using quantum computers for computational chemistry and materials science will enable us to tackle problems that are intractable on classical computers. In this paper, we show how the relative energy of defective graphene structures can be calculated by using a quantum annealer. This simple system is used to guide the reader through the steps needed to translate a chemical structure (a set of atoms) and energy model to a representation that can be implemented on quantum annealers (a set of qubits). We discuss in detail how different energy contributions can be included in the model and what their effect is on the final result. The code used to run the simulation on D-Wave quantum annealers is made available as a Jupyter Notebook. This Tutorial was designed to be a quick-start guide for the computational chemists interested in running their first quantum annealing simulations. The methodology outlined in this paper represents the foundation for simulating more complex systems, such as solid solutions and disordered systems. [ABSTRACT FROM AUTHOR]

Nuclear quantum phenomena beyond the Born–Oppenheimer approximation are known to play an important role in a growing number of chemical and biological processes. While there exists no unique consensus on a rigorous and efficient implementation of coupled electron–nuclear quantum dynamics, it is recognized that these problems scale exponentially with system size on classical processors and, therefore, may benefit from quantum computing implementations. Here, we introduce a methodology for the efficient quantum treatment of the electron–nuclear problem on near-term quantum computers, based upon the Nuclear–Electronic Orbital (NEO) approach. We generalize the electronic two-qubit tapering scheme to include nuclei by exploiting symmetries inherent in the NEO framework, thereby reducing the Hamiltonian dimension, number of qubits, gates, and measurements needed for calculations. We also develop parameter transfer and initialization techniques, which improve convergence behavior relative to conventional initialization. These techniques are applied to H2 and malonaldehyde for which results agree with NEO full configuration interaction and NEO complete active space configuration interaction benchmarks for ground state energy to within 10−6 hartree and entanglement entropy to within 10−4. These implementations therefore significantly reduce resource requirements for full quantum simulations of molecules on near-term quantum devices while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

Circuit depth reduction is of critical importance for quantum chemistry simulations on current and near term quantum computers. This issue is tackled by introducing a chemically aware strategy for the unitary coupled cluster ansatz. The objective is to use the chemical description of a system to aid in the synthesis of a quantum circuit. We combine this approach with two flavors of symmetry verification for the reduction of experimental noise. These methods enable the use of Quantinuum's System Model H1 ion trap quantum computer for a 6-qubit quantum subspace expansion calculation. We present (i) calculations to obtain methane's optical spectra; (ii) an atmospheric gas reaction simulation involving [ C H 3 ⋅ —H—OH]‡. Using our chemically aware unitary coupled cluster state-preparation strategy in tandem with state of the art symmetry verification methods, we improve device yield for CH4 at 6 qubits. This is demonstrated by a 90% improvement in two-qubit gate count and a reduction in relative error to 0.2% for electronic energy calculated on System Model H1. [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.

Sparse connectivity of a superconducting quantum computer results in large experimental overheads of SWAP gates. In this study, we consider employing a virtual two-qubit gate (VTQG) as an error suppression technique. The VTQG enables a non-local operation between a pair of distant qubits using only single qubit gates and projective measurements. Here, we apply the VTQG to the digital quantum simulation of the transverse-field Ising model on an IBM quantum computer to suppress the errors due to the noisy two-qubit operations. We present an effective use of VTQG, where the reduction in multiple SWAP gates results in increasing the fidelity of output states. The obtained results indicate that the VTQG can be useful for suppressing the errors due to additional SWAP gates. In our experiments, we have observed one order of magnitude improvement in accuracy for the quantum simulation of the transverse-field Ising model with 8 qubits. Finally, we have demonstrated an efficient implementation of the VTQG by utilizing dynamic circuits. This scheme reduces experimental overheads for implementing m VTQGs from O (10 m) to O (6 m). [ABSTRACT FROM AUTHOR]

The integration of quantum processing units (QPUs) in a heterogeneous high-performance computing environment requires solutions that facilitate hybrid classical–quantum programming. Standards such as OpenCL facilitate the programming of heterogeneous environments, consisting of CPUs and hardware accelerators. This study presents an innovative method that incorporates QPU functionality into OpenCL, standardizing quantum processes within classical environments. By leveraging QPUs within OpenCL, hybrid quantum–classical computations can be sped up, impacting domains like cryptography, optimization problems, and quantum chemistry simulations. Using Portable Computing Language (Jääskeläinen et al. in Int J Parallel Program 43(5):752–785, 2014. https://doi.org/10.1007/s10766-014-0320-y) and the Qulacs library (Suzuki et al. in Quantum 5:559, 2021. https://doi.org/10.22331/q-2021-10-06-559), results demonstrate, for instance, the successful execution of Shor's algorithm (Nielsen and Chuang in Quantum computation and quantum information, 10th anniversary edn. Cambridge University Press, Cambridge, 2010), serving as a proof of concept for extending the approach to larger qubit systems and other hybrid quantum–classical algorithms. This integration approach bridges the gap between quantum and classical computing paradigms, paving the way for further optimization and application to a wide range of computational problems. [ABSTRACT FROM AUTHOR]

The current state of quantum computing is commonly described as the Noisy Intermediate-Scale Quantum era. Available computers contain a few dozens of qubits and can perform a few dozens of operations before the inevitable noise erases all information encoded in the calculation. Even if the technology advances fast within the next years, any use of quantum computers will be limited to short and simple tasks, serving as subroutines of more complex classical procedures. Even for these applications the resource efficiency, measured in the number of quantum computer runs, will be a key parameter. Here we suggest a general meta-optimization procedure for hybrid quantum-classical algorithms that allows finding the optimal approach with limited quantum resources. This method optimizes the usage of resources of an existing method by testing its capabilities and setting the optimal resource utilization. We demonstrate this procedure on a specific example of variational quantum algorithm used to find the ground state energy of a hydrogen molecule. [ABSTRACT FROM AUTHOR]

As a crucial component of superconducting quantum computing, the three‐energy‐level quantum system, known as qutrit, along with qutrit gates, has garnered significant attention over the past decades, which highlights the significance of accomplishing state transfers between levels in multiqutrit systems as a critical task. Herein, the |fg⟩−|ge⟩$\left|\right. f g \rangle - \left|\right. g e \rangle$ coupling is constructed via the all‐microwave approach. Both the coupling strength and Alternating Current stark shift are calibrated, based on which a fast (70 ns) state transfer from |gf⟩$\left|\right. g f \rangle$ to |fg⟩$\left|\right. f g \rangle$ is investigated and achieved through the stimulated Raman shortcut‐to‐adiabatic passage. [ABSTRACT FROM AUTHOR]

Quantum computing is currently a research hotspot in both academia and industry. The inherent parallelism of quantum computers and the resulting powerful computing power will bring new solutions to many problems that are difficult for classical computers. However, due to the limitations of technical conditions, it is difficult to achieve full direct coupling of all qubits on a quantum chip. When compiling a quantum circuit onto a physical chip, it is necessary to ensure those two-qubit gates act on pairs of directly coupled qubits by inserting SWAP gates. It will cause great additional cost when a large number of SWAP gates are inserted, leading to the execution time of quantum circuits longer. In this paper, we designed a strategy based on the business of each individual qubit to insert SWAP gates, named Busy-Qubits-Avoid Strategy. On the one hand, we try to hide the time overhead incurred by the inserted SWAP gates by exploiting the uneven distribution of quantum gates over qubits. On the other hand, we also expect the inserted SWAP gates to make as little negative impact on subsequent two-qubit gates as possible. We designed a heuristic function which takes into account both of these points. Compared with Sabre and tket, we achieved a better effect. In addition, as the number of two-qubit gates increases, better optimization results will be achieved. This implies higher execution efficiency and lower decoherence error rate. [ABSTRACT FROM AUTHOR]

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates quantum spiking neural networks (QSNN) and quantum long short-term memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain's hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conducted a comparative analysis of our model against existing quantum models, including quantum neural networks (QNNs), QLSTM, QSNN and their classical counterparts, elucidating its unique contributions. Through empirical results, we demonstrated the effectiveness of utilizing quantum models inspired by the brain, which outperform the classical approaches and other quantum models in optimizing energy use case. Specifically, in terms of average, best and worst total reward, test reward, robustness, and learning curve. [ABSTRACT FROM AUTHOR]

Herein, methods for the fabrication of monolithic diamond integrated photonic devices via Faraday cage‐angled etching of functional epitaxial diamond layers are presented. Optimal waveguide width is determined via simulation and the fabrication of nitrogen vacancy (NV) center‐doped microring resonators is demonstrated. The performance of the devices is verified via microphotoluminescence as well as cathodoluminescence scans of the in‐grown NV centers, revealing clearly visible cavity lines. The scalable fabrication method allows for the realization of large numbers of lateral waveguide structures as needed for future applications in integrated quantum sensing devices as well as novel spin‐based quantum computers. [ABSTRACT FROM AUTHOR]

Pauli channels are fundamental in the context of quantum computing as they model the simplest kind of noise in quantum devices. We propose a quantum algorithm for simulating Pauli channels and extend it to encompass Pauli dynamical maps (parametrized Pauli channels). A parametrized quantum circuit is employed to accommodate for dynamical maps. We also establish the mathematical conditions for an N-qubit transformation to be achievable using a parametrized circuit where only one single-qubit operation depends on the parameter. The implementation of the proposed circuit is demonstrated using IBM's quantum computers for the case of one qubit, and the fidelity of this implementation is reported. [ABSTRACT FROM AUTHOR]

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]

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 next-generation "Industrial Internet of Things" (IIoT) will support "Machine-to-Machine" (M2M) communications for smart Cyber-Physical-Systems and Industry 4.0, and require guaranteed cyber-security. This paper explores hardware-enforced cyber-security for critical infrastructures. It examines a quantum-safe "Software-Defined-Deterministic IIoT" (SDD-IIoT), with a new forwarding-plane (sub-layer-3a) for deterministic M2M traffic flows. A "Software-Defined Networking" (SDN) control plane controls many "Software-Defined-Deterministic Wide-Area Networks" (SDD-WANs), realized with FPGAs. The SDN control plane provides an "Admission-Control/Access-Control" system for network-bandwidth, using collaborating Artificial Intelligence (AI)-based "Zero Trust Architectures" (ZTAs). Hardware-enforced access-control eliminates all congestion, BufferBloat, and DoS/DDoS attacks, significantly reduces buffer-sizes, and supports ultra-reliable-low-latency communications in the forwarding-plane. The forwarding-plane can: (i) Encrypt/Authenticate M2M flows using quantum-safe ciphers, to withstand attacks by Quantum Computers; (ii) Implement "guaranteed intrusion detection systems" in FPGAs, to detect cyber-attacks embedded within billions of IIoT packets; (iii) Provide guaranteed immunity to external cyber-attacks, and exceptionally strong immunity to internal cyber-attacks; (iv) Save USD 100s of billions annually by exploiting FPGAs; and (v) Enable hybrid Classical-Quantum networks, by integrating a "quantum key distribution" (QKD) network with a classical forwarding plane with exceptionally strong cyber-security, determined by the computational hardness of cracking Symmetric Key Cryptography. Extensive experimental results for an SDD-WAN over the European Union are reported. [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]

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]

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]

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]

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 rapid development of quantum computing has led to increasing interest in quantum algorithms for a variety of different applications. Quantum walks have also experienced a surge in interest due to their potential use in quantum algorithms. Using the qiskit software package, we test how accurately the current generation of quantum computers provided by IBM can simulate a cycle discrete-time quantum walk. Implementing an 8-node, 8-step walk and a simpler 4-node, 4-step discrete-time quantum walk on an IBM quantum device known as ibmq_quito, the results for each step of the respective walks are presented. A custom noise model is developed in order to estimate that noise levels in the ibmq_santiago quantum device would need to be reduced by at least 94% in order to execute a 16-node, 16-step cycle discrete-time quantum walk to a reasonable level of fidelity. [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]

Portfolio optimization plays a central role in finance to obtain optimal portfolio allocations that aim to achieve certain investment goals. Portfolio optimization also provides a rich area to study the application of quantum computers to obtain advantages over classical computers. In a multi-period setting, we give a sampling version of an existing classical online portfolio optimization algorithm by Helmbold et al., for which we in turn develop a quantum version. The quantum advantage is achieved by using techniques such as quantum state preparation, inner product estimation and multi-sampling. Our quantum algorithm provides a quadratic speedup in the time complexity, in terms of n, where n is the number of assets in the portfolio. The transaction cost of both of our classical and quantum algorithms is independent of n which is especially useful for practical applications with a large number of assets. [ABSTRACT FROM AUTHOR]

The fusion of quantum computing and artificial intelligence (AI) heralds a transformative era for Industry 4.0, offering unprecedented capabilities and challenges. This paper delves into the intricacies of quantum AI, its potential impact on Industry 4.0, and the necessary change management and innovation strategies for seamless integration. Drawing from theoretical insights and real-world case studies, we explore the current landscape of quantum AI, its foreseeable influence, and the implications for organizational strategy. We further expound on traditional change management tactics, emphasizing the importance of continuous learning, ecosystem collaborations, and proactive approaches. By examining successful and failed quantum AI implementations, lessons are derived to guide future endeavors. Conclusively, the paper underscores the imperative of being proactive in embracing quantum AI innovations, advocating for strategic foresight, interdisciplinary collaboration, and robust risk management. Through a comprehensive exploration, this paper aims to equip stakeholders with the knowledge and strategies to navigate the complexities of quantum AI in Industry 4.0, emphasizing its transformative potential and the necessity for preparedness and adaptability. [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]

To simulate quantum systems on classical or quantum computers, the continuous observables (e.g., coordinate and momentum or energy and time) must be reduced to discrete ones. In this paper, we consider the continuous observables represented in the positional systems as power series in the radix multiplied over the summands ("digits"), which turn out to be Hermitian operators with discrete spectrum. We investigate the obtained quantum mechanical operators of digits, the commutation relations between them, and the effects of the choice of a numeral system on lattices and representations. Renormalizations of diverging sums naturally occur in constructing the digital representation. [ABSTRACT FROM AUTHOR]

The Integrated Side Lobe Ratio (ISLR) problem the authors consider here consists in finding optimal sequences of phase shifts in order to minimise the mean squared cross‐correlation side lobes of a transmitted radar signal and a mismatched replica. Currently, ISLR does not seem to be easier than the general polynomial unconstrained binary problem, which is NP‐hard. In their work, the authors aim to take advantage of the scalability of quantum computing to find new optima, by solving the ISLR problem on a quantum annealer. This quantum device is designed to solve quadratic optimisation problems with binary variables (QUBO). After proposing suitable formulation for different instances of the ISLR, the authors discuss the performances and the scalability of their approach on the D‐Wave quantum computer. More broadly, their work enlightens the limits and potential of the adiabatic quantum computation for the solving of large instances of combinatorial optimisation problems. [ABSTRACT FROM AUTHOR]