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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]

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]

This study introduces a conceptually novel polynomial encoding algorithm for simulating potential energy operators encoded in diagonal unitary forms in a quantum computing machine. The current trend in quantum computational chemistry is effective experimentation to achieve high-precision quantum computational advantage. However, high computational gate complexity and fidelity loss are some of the impediments to the realization of this advantage in a real quantum hardware. In this study, we address the challenges of building a diagonal Hamiltonian operator having exponential functional form, and its implementation in the context of the time evolution problem (Hamiltonian simulation and encoding). Potential energy operators when represented in the first quantization form is an example of such types of operators. Through systematic decomposition and construction, we demonstrate the efficacy of the proposed polynomial encoding method in reducing gate complexity from O (2 n) to O ∑ i = 1 r n C r (for some r ≪ n ). This offers a solution with lower complexity in comparison to the conventional Hadamard basis encoding approach. The effectiveness of the proposed algorithm was validated with its implementation in the IBM quantum simulator and IBM quantum hardware. This study demonstrates the proposed approach by taking the example of the potential energy operator of the sodium iodide molecule (NaI) in the first quantization form. The numerical results demonstrate the potential applicability of the proposed method in quantum chemistry problems, while the analytical bound for error analysis and computational gate complexity discussed, throw light on issues regarding its implementation. [ABSTRACT FROM AUTHOR]

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition. [ABSTRACT FROM AUTHOR]

We deal with no-signaling correlations that include Bell-type quantum nonlocality. We consider a logical implementation using a trusted central server with encrypted connections to clients. We show that in this way it is possible to implement two-party no-signaling correlations in an asynchronous manner. While from the point of view of physics our approach can be considered as the computer emulation of the results of measurements on entangled particles, from the software engineering point of view it introduces a primitive in communication protocols that can be capable of coordinating agents without revealing the details of their actions. We present an actual implementation in the form of a Web-based application programming interface (RESTful Web API). We demonstrate the use of the API via the simple implementation of the Clauser–Horne–Shimony–Holt game. [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]

Quantum computing (QC) is a rapidly growing technology in the field of computation that has garnered significant attention in recent years. This emerging technology has become particularly relevant due to the increasing complexity of optimization problems and their expanding search spaces. As a result, innovative solutions that can surpass the limitations of the current optimization paradigms executed on classic computers are becoming necessary. D-wave, a specialized quantum computer, presents a novel solution for addressing intricate optimization problems with remarkable speed advantages over traditional methods. However, a major hurdle in terms of utilizing the D-wave platform for topology optimization design is the conversion of an optimization problem into formulas that can be comprehended by a quantum annealing machine. This is because the D-wave platform is limited to solving quadratic unconstrained binary optimization problems or Ising model problems, making it necessary to find a way to adapt the task of interest to these specific types of optimization problems. This paper examines the current reality concerning the extremely limited availability of quantum computing resources. We focus on small-scale discrete structural topology optimization problems as a starting point and establish a mapping relationship between quantum bits and the cross-sectional area variables of truss elements. Utilizing this mapping, a quadratic unconstrained binary optimization model is developed with these variables. We propose a nested optimization process with dynamically adjusted cross-sectional areas, which enables the development of a quantum annealing approach for optimizing the topology of discrete variables. Our method is validated through numerical experiments, demonstrating its efficiency. [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]

The manuscript theoretically discusses various important aspects for donor atom based single qubit operations in silicon (Si) quantum computer architecture at room temperature using a single nitrogen (N) deep‐donor close to the Si/SiO2 interface. Quantitative investigation of room temperature single electron shuttling between a single N‐donor atom and the interface is the focus of attention under the influence of externally applied electric and magnetic field. To apprehend the realistic experimental configurations, central cell correction along with effective mass approach is adopted throughout the study. Furthermore, a detailed discussion currently explores the significant time scales implicated in the process and their suitability for experimental purposes. Theoretical estimates are also provided for all the external fields required to successfully achieve coherent single electron shuttling and their stable maintenance at the interface as required. The results presented in this work offer practical guidance for quantum electron control using N‐donor atoms in Si at room temperature. [ABSTRACT FROM AUTHOR]

Quantum computing shows promise for 6G networks due to its parallel computing capabilities. In the context of the Noisy Intermediate‐Scale Quantum era, the introduction of hybrid quantum‐classical algorithms like Quantum Approximate Optimization Algorithm (QAOA) offer powerful solutions to many combinatorial optimization problems in 6G. This paper focuses on Low‐Density Parity‐Check (LDPC) channel decoding and proposes an improved QAOA algorithm assisted by the learning‐to‐learn strategy. We also investigate the parameter concentration phenomenon in QAOA‐based LDPC decoding to assess the rationality. To evaluate effectiveness, a comprehensive numerical expression for the energy expectation of single‐layer QAOA and propose indicators for transfer performance evaluation is provided. Based on simulation results, the similarity in parameter distribution across specific LDPC configurations is investigated. This similarity facilitates the transfer of training outcomes from smaller to larger‐scale problems for optimization initialization, thereby avoiding the need for retraining. This approach offers insights and potential solutions for rapid, large‐scale channel decoding in 6G networks, despite the current limitations of quantum hardware. [ABSTRACT FROM AUTHOR]

Entanglement serves as the resource to empower quantum computing. Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entanglement into quantum operations or measurements of quantum machine learning (QML) models leads to substantial reductions in training data size, surpassing a specified prediction error threshold. However, an analytical understanding of how the entanglement degree in data affects model performance remains elusive. In this study, we address this knowledge gap by establishing a quantum no-free-lunch (NFL) theorem for learning quantum dynamics using entangled data. Contrary to previous findings, we prove that the impact of entangled data on prediction error exhibits a dual effect, depending on the number of permitted measurements. With a sufficient number of measurements, increasing the entanglement of training data consistently reduces the prediction error or decreases the required size of the training data to achieve the same prediction error. Conversely, when few measurements are allowed, employing highly entangled data could lead to an increased prediction error. The achieved results provide critical guidance for designing advanced QML protocols, especially for those tailored for execution on early-stage quantum computers with limited access to quantum resources. Given that entangled states can store more information than unentangled ones, it would be natural to assume that highly-entangled data would always enhance capabilities of quantum machine learning models. Here, the authors show that this is not the case, in particular when the allowed number of measurements to incoherently learn quantum dynamics is low [ABSTRACT FROM AUTHOR]

One of the most effective ways to advance the performance of quantum computers and quantum sensors is to increase the number of qubits or quantum resources in the system. A major technical challenge that must be solved to realize this goal for trapped-ion systems is scaling the delivery of optical signals to many individual ions. In this paper we demonstrate an approach employing waveguides and multi-mode interferometer splitters to optically address multiple 171Yb+ ions in a surface trap by delivering all wavelengths required for full qubit control. Measurements of hyperfine spectra and Rabi flopping were performed on the E2 clock transition, using integrated waveguides for delivering the light needed for Doppler cooling, state preparation, coherent operations, and detection. We describe the use of splitters to address multiple ions using a single optical input per wavelength and use them to demonstrate simultaneous Rabi flopping on two different transitions occurring at distinct trap sites. This work represents an important step towards the realization of scalable integrated photonics for atomic clocks and trapped-ion quantum information systems. A promising strategy for scaling trapped-ion-based quantum technologies is to use fully integrated optical waveguides to deliver light to numerous ions at multiple sites. Here, the authors. optically address three ions using on-chip waveguides to deliver three distinct wavelengths per ion, and perform Rabi flopping on each ion simultaneously. [ABSTRACT FROM AUTHOR]

With a growing interest in quantum technology globally, there is an increasing need for accessing relevant physical systems for education and research. In this paper we introduce a commercially available on-site quantum computer utilizing superconducting technology, offering insights into its fundamental hardware and software components. We show how this system can be used in education to teach quantum concepts and deepen understanding of quantum theory and quantum computing. It offers learning opportunities for future talent and contributes to technological progress. Additionally, we demonstrate its use in research by replicating some notable recent achievements. [ABSTRACT FROM AUTHOR]

The intensive pursuit for quantum advantage in terms of computational complexity has further led to a modernized crucial question of when and how will quantum computers outperform classical computers. The next milestone is undoubtedly the realization of quantum acceleration in practical problems. Here we provide a clear evidence and arguments that the primary target is likely to be condensed matter physics. Our primary contributions are summarized as follows: 1) Proposal of systematic error/runtime analysis on state-of-the-art classical algorithm based on tensor networks; 2) Dedicated and high-resolution analysis on quantum resource performed at the level of executable logical instructions; 3) Clarification of quantum-classical crosspoint for ground-state simulation to be within runtime of hours using only a few hundreds of thousand physical qubits for 2d Heisenberg and 2d Fermi-Hubbard models, assuming that logical qubits are encoded via the surface code with the physical error rate of p = 10−3. To our knowledge, we argue that condensed matter problems offer the earliest platform for demonstration of practical quantum advantage that is order-of-magnitude more feasible than ever known candidates, in terms of both qubit counts and total runtime. [ABSTRACT FROM AUTHOR]

Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS2, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (Co S 0 ) and fabricate it with scanning tunneling microscopy (STM). The Co S 0 electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects. Point defects in 2D semiconductors have potential for quantum computing applications, but their controlled design and synthesis remains challenging. Here, the authors identify and fabricate a promising quantum defect in 2D WS2 via high-throughput computational screening and scanning tunnelling microscopy. [ABSTRACT FROM AUTHOR]

We showcase the digital quantum simulation of the action of a Hamiltonian that governs the interaction between a quantum mechanical oscillator and an optical field, generating quantum entanglement between them via gravitational effects. This is achieved by making use of a boson-qubit mapping protocol and a digital gate decomposition that allow us to run the simulations in the quantum computers available in the IBM Quantum platform. We present the obtained results for the fidelity of the experiment in two different quantum computers, after applying error mitigation and post-selection techniques. The achieved results correspond to fidelities over 90%, which indicates that we were able to perform a faithful digital quantum simulation of the interaction and therefore of the generation of quantum entanglement by gravitational means in optomechanical systems. [ABSTRACT FROM AUTHOR]

Quantum Convolutional Neural Network (QCNN) has achieved significant success in solving various complex problems, such as quantum many-body physics and image recognition. In comparison to the classical Convolutional Neural Network (CNN) model, the QCNN model requires excellent numerical performance or efficient computational resources to showcase its potential quantum advantages, particularly in classical data processing tasks. In this paper, we propose a computationally resource-efficient QCNN model referred to as RE-QCNN. Specifically, through a comprehensive analysis of the complexity associated with the forward and backward propagation processes in the quantum convolutional layer, our results demonstrate a significant reduction in computational resources required for this layer compared to the classical CNN model. Furthermore, our model is numerically benchmarked on recognizing images from the MNIST and Fashion-MNIST datasets, achieving high accuracy in these multi-class classification tasks. [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]

With the rapid development of Internet of Things (IoT) technology, the number of IoT users is growing year after year. IoT will become a part of our daily lives, so it is likely that the security of these devices will be an important issue in the future. Quantum computing is maturing, and the security threat associated with quantum computing will be faced in the transmissions of IoT devices, which mainly use wireless communication technologies. Therefore, to ensure the protection of transmitted data, a cryptographic algorithm that is efficient in defeating quantum computer attacks needs to be developed. In this paper, we propose a device authentication and secure communication system with post-quantum cryptography (PQC) for AIoT environments using the NTRU and Falcon signature mechanism, which can resist quantum computer attacks and be used in AIoT environments to effectively protect the confidentiality, integrity, and non-repudiation of transmitted data. We also used Raspberry Pi to simulate AIoT devices for implementation. [ABSTRACT FROM AUTHOR]

Quantum neural networks (QNNs) leverage the strengths of both quantum computing and neural networks, offering solutions to challenges that are often beyond the reach of traditional neural networks. QNNs are being used in areas such as computer games, function approximation, and big data processing. Moreover, quantum neural network algorithms are finding utility in social network modeling, associative memory systems, and automatic control mechanisms. Nevertheless, ensuring the reliability of quantum neural networks is crucial as it directly influences network performance and stability. To investigate the reliability of quantum neural networks, this paper proposes a methodology wherein operator measurements are performed on the final states of the output quantum states of a quantum neural network. The proximity of these measurements to the target value is compared, and the fidelity value, combined with a quantum gate operation, is utilized to assess the reliability of the quantum neural network. Through network training, the results demonstrate that, under optimal parameters, both the fidelity of the final state measurement value and the target value of the model approach are approximately equal to 1. It indicates that training mitigates the errors stemming from encoding into the initial quantum state, thereby resulting in enhanced system reliability and accuracy. [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]

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]

Grover's algorithm has been widely used for quantum key search to attack block ciphers with a quadratic speedup as compared to classical brute-force attacks and also used for evaluating the post-quantum security of block ciphers against quantum computer attack. But, this quantum key search on block ciphers has a high quantum circuit depth and AES-128 is still secure against such attack. In this paper, we introduce a method called the depth–measurement trade-off method that reduces the overall quantum circuit depth of quantum key search to attack block ciphers by increasing the number of measurements of the circuit. This method is to introduce dummy keys in the quantum circuit as part of the correct key. This will reduce both quantum circuit resource and quantum circuit depth. Based on this technique, the quantum circuit depth of AES-128 is less than 2 64 , while NIST suggested circuit depth should be greater than MAXDEPTH, which is 2 40 , 2 64 and 2 96 in order to resist the respective attacks. In addition, we also simulated the depth–measurement trade-off method on the reduced SIMON block cipher algorithm as a proof of concept. Furthermore, we also apply the depth–measurement technique on various block ciphers, for example AES, PRESENT, SIMON, GIFT, SPECK, RECTANGLE, LowMC, KNOT, PIPO, etc. [ABSTRACT FROM AUTHOR]

Landauer's bound is applicable to irreversible quantum operations. In this study, we showcased that the Doppler temperature manifests the existence of Landauer's bound, which does not block a spin from (irreversibly) flipping with a tiny amount of energy via quantum tunneling. Verified by a spin–spin magnetic interaction experiment, this (energy) amount was determined to be only 1.25 times the theoretical value of Landauer's bound. Based on Heisenberg's principle, we defined information from a measuring perspective: one bit of information corresponds to the smallest error when quantifying the product of the measured energy uncertainty ( Δ E ) and the measured time duration ( Δ t ). We then illustrate an optically manipulated, spin-encoded, near-Landauer-bound, near-Heisenberg-limit quantum computer that encompasses this new definition of information. This study may represent the last piece of the puzzle in understanding both quantum Landauer erasure and Heisenberg's quantum limit since a single spin is the smallest information carrier. [ABSTRACT FROM AUTHOR]

In this paper, we present some new key-recovery attacks on Misty L-KF, Misty R-KF, and generalized Feistel schemes. Firstly, we propose a new 5-round distinguisher on Misty L-KF structure. Based on our new distinguisher attack, we propose a new 6-round Demiric–Selçuk meet-in-the-middle attack (DS-MITM attack) against Misty L-KF structure. Secondly, we extend our classical DS-MITM attack to a new quantum DS-MITM attack on Misty L-KF structure by using the quantum claw finding algorithm. In addition, we apply the above method to attack Misty R-KF and generalized Feistel schemes. To sum up, we construct our classical key-recovery attacks on the 6-round Misty L-KF structure and Misty R-KF structure with O (2 3 n / 4) time and O (2 n / 2) memory cost. By using a quantum computer, our new quantum key-recovery attacks on the 6-round Misty L-KF structures and Misty R-KF structures can be constructed with O ~ (2 n / 2) time and O (2 n / 2) memory cost. Furthermore, we can construct our new quantum (5 d - 4) -round key-recovery attacks on the d-branch contracting Feistels with O ~ (2 (d - 1) n / d) time and O (2 (d - 1) n / d) memory cost. In the end, we can construct our new quantum (4 d - 3) -round and (5 d - 4) -round key-recovery attacks on the two types of d-branch expanding Feistels with O ~ (2 (d - 1) n / d) time and O (2 (d - 1) n / d) memory cost. [ABSTRACT FROM AUTHOR]

This article explores the critical aspect of error mitigation within quantum systems, particularly its significance in enabling secure qutrit distribution via Cleve's protocol. Initially, it investigates quantum secret sharing utilizing a (k, n) threshold scheme, demonstrating its cryptographic robustness in securely disseminating secrets among multiple parties. Subsequently, a comprehensive analysis is conducted to elucidate the nuanced impact of bit-flip, phase-flip, and depolarization errors on quantum data integrity, highlighting the necessity for tailored error mitigation strategies. Within the context of a Quantum Neural Q Network (QNQN), error correction techniques are applied to classification outcomes, showcasing their efficacy in rectifying states uniformly despite initial errors. This underscores the critical importance of error mitigation in preserving data accuracy and integrity in quantum computations and secure qutrit distribution methodologies, particularly within the framework of Cleve's protocol. [ABSTRACT FROM AUTHOR]

With the rapid development of computer and internet technology, quantum signature plays an extremely important role in modern secure communication. Quantum homomorphic aggregate signature, as an important guarantee of quantum signature, plays a significant role in reducing storage, communication, and computing costs. This article draws on the idea of quantum multi-party summation and proposes a quantum homomorphic aggregate signature scheme based on quantum Fourier transform. Our scheme uses n-particle entangled states as quantum channels, with different particles of each entangled state sent separately. This ensures secure transmission of signatures and messages with fewer entangled particles during transmission, further improving the efficiency of quantum signatures. Meanwhile, our scheme generates private keys for each participating party by randomly constructing key generation matrixes. Different signers perform quantum Fourier transforms and basis exchange operations on entangled particles based on different messages and private keys to generate signatures. In addition, the aggregator does not need to measure and verify the signature particles after receiving signatures from different signers, and the group addition operation process has additive homomorphism. Security analysis shows that our scheme has unforgeability, non-repudiation, and can resist various attacks such as entanglement measurement attacks, intercept-resend attacks, private key sequence attacks, and internal attacks by aggregator. [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]