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

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

In this article, based on some simple and reasonable assumptions, we derive a Gaussian noise model for quantum amplitude estimation. We provide results from quantum amplitude estimation run on various IBM superconducting quantum computers and on Quantinuum's H1 trapped-ion quantum computer to show that the proposed model is a good fit for real-world experimental data. We also show that the proposed Gaussian noise model can be easily composed with other noise models in order to capture effects that are not well described by Gaussian noise. We give a generalized procedure for how to embed this noise model into any quantum-phase-estimation-free quantum amplitude estimation algorithm, such that the amplitude estimation is “noise aware.” We then provide experimental results from running an implementation of noise-aware quantum amplitude estimation using data from an IBM superconducting quantum computer, demonstrating that the addition of “noise awareness” serves as an effective means of quantum error mitigation.

Demand-side response (DSR) is a strategy that enables consumers to actively participate in managing electricity demand. It aims to alleviate strain on the grid during high demand and promote a more balanced and efficient use of (renewable) electricity resources. We implement DSR through discount scheduling, which involves offering discrete price incentives to consumers to adjust their electricity consumption patterns to times when their local energy mix consists of more renewable energy. Since we tailor the discounts to individual customers' consumption, the discount scheduling problem (DSP) becomes a large combinatorial optimization task. Consequently, we adopt a hybrid quantum computing approach, using D-Wave's Leap Hybrid Cloud. We benchmark Leap against Gurobi, a classical mixed-integer optimizer, in terms of solution quality at fixed runtime and fairness in terms of discount allocation. Furthermore, we propose a large-scale decomposition algorithm/heuristic for the DSP, applied with either quantum or classical computers running the subroutines, which significantly reduces the problem size while maintaining solution quality. Using synthetic data generated from real-world data, we observe that the classical decomposition method obtains the best overall solution quality for problem sizes up to 3200 consumers; however, the hybrid quantum approach provides more evenly distributed discounts across consumers.

Quanvolutional neural networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of traditional convolution. Unfortunately, the qubit's reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this article, we aim at investigating the propagation of logical-shift errors (i.e., the unexpected modification of the qubit state) in QNNs. We propose a bottom–up evaluation reporting data from 13 322 547 200 logical-shift injections. We characterize the error propagation in the quantum circuit implementing a single convolution and then in various designs of the same QNN, varying the dataset and the network depth. We track the logical-shift error propagation through the qubits, channels, and subgrids, identifying the faults that are more likely to cause misclassifications. We found that up to 10% of the injections in the quanvolutional layer cause misclassification and even logical-shifts of small magnitude can be sufficient to disturb the network functionality. Our detailed analysis shows that corruptions in the qubits' state that alter their probability amplitude are more critical than the ones altering their phase, that some object classes are more likely than others to be corrupted, that the criticality of subgrids depends on the dataset, and that the control qubits, once corrupted, are more likely to modify the QNN output than the target qubits.

The increased development of quantum computing hardware in recent years has led to increased interest in its application to various areas. Finding effective ways to apply this technology to real-world use-cases is a current area of research in the remote sensing community. This article proposes an adiabatic quantum kitchen sinks (AQKS) kernel approximation algorithm with parallel quantum annealing on the D-Wave Advantage quantum annealer. The proposed implementation is applied to support vector regression and Gaussian process regression algorithms. To evaluate its performance, a regression problem related to estimating chlorophyll concentration in water is considered. The proposed algorithm was tested on two real-world datasets and its results were compared with those obtained by a classical implementation of kernel-based algorithms and a random kitchen sinks implementation. On average, the parallel AQKS achieved comparable results to the benchmark methods, indicating its potential for future applications.

In portfolio theory, the investment portfolio optimization problem is one of those problems whose complexity grows exponentially with the number of assets. By backtesting classical and quantum computing algorithms, we can get a sense of how these algorithms might perform in the real world. This work establishes a methodology for backtesting classical and quantum algorithms in equivalent conditions, and uses it to explore four quantum and three classical computing algorithms for portfolio optimization and compares the results. Running 10 000 experiments on equivalent conditions we find that quantum can match or slightly outperform classical results, showing a better escalability trend. To the best of our knowledge, this is the first work that performs a systematic backtesting comparison of classical and quantum portfolio optimization algorithms. In this work, we also analyze in more detail the variational quantum eigensolver algorithm, applied to solve the portfolio optimization problem, running on simulators and real quantum computers from IBM. The benefits and drawbacks of backtesting are discussed, as well as some of the challenges involved in using real quantum computers of more than 100 qubits. Results show quantum algorithms can be competitive with classical ones, with the advantage of being able to handle a large number of assets in a reasonable time on a future larger quantum computer.

In recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in quantum machine learning and in particular for the popular quantum support vector machine (SVM). Several versions of the quantum SVM have been proposed, and quantum annealing has been shown to be effective in them. Extensions to multiclass problems have also been made, which consist of an ensemble of multiple binary classifiers. This article proposes a novel quantum SVM formulation for direct multiclass classification based on quantum annealing, called quantum multiclass SVM (QMSVM). The multiclass classification problem is formulated as a single quadratic unconstrained binary optimization problem solved with quantum annealing. The main objective of this article is to evaluate the feasibility, accuracy, and time performance of this approach. Experiments have been performed on the D-Wave Advantage quantum annealer for a classification problem on remote sensing data. Results indicate that, despite the memory demands of the quantum annealer, QMSVM can achieve an accuracy that is comparable to standard SVM methods, such as the one-versus-one (OVO), depending on the dataset (compared to OVO: 0.8663 versus 0.8598 on Toulouse, 0.8123 versus 0.8521 on Potsdam). More importantly, it scales much more efficiently with the number of training examples, resulting in nearly constant time (compared to OVO: 85.72 versus 248.02 s on Toulouse, 58.89 versus 580.17 s on Potsdam). This article shows an approach for bringing together classical and quantum computation, solving practical problems in remote sensing with current hardware.

Noisy intermediate-scale quantum computers are widely used for quantum computing (QC) from quantum cloud providers. Among them, superconducting quantum computers, with their high scalability and mature processing technology based on traditional silicon-based chips, have become the preferred solution for most commercial companies and research institutions to develop QC. However, superconducting quantum computers suffer from fluctuation due to noisy environments. To maintain reliability for every execution, calibration of the quantum processor is significantly important. During the long procedure to calibrate physical quantum bits (qubits), quantum processors must be turned into offline mode. In this work, we propose a real-time calibration framework (RCF) to execute quantum program tasks and calibrate in-demand qubits simultaneously, without interrupting quantum processors. Across a widely used noisy intermediate-scale quantum (NISQ) evaluation benchmark suite such as QASMBench, RCF achieves up to 18% reliability improvement for applications. For reliability on different physical qubits, RCF achieves an average gain of 15.7% (up to 36.7%). For cloud quantum machines, the throughput can be improved up to 9.5 throughput per minute (6.5 on average) based on baseline calibration time. In conclusion, RCF offers a reliable solution for large-scale, long-serving quantum machines.

In the design of digital logic circuits, QCA technology is an excellent alternative to CMOS technology. Its advantages over CMOS include low power consumption, fast circuit switching, and nanoscale design. Circuits that convert data between different formats are code converters. Code converters have an essential role in high-performance computing and signal processing. In this paper, first, we proposed a novel QCA structure for the quantum reversible Fredkin gate. Second, we proposed 4-bit and 8-bit QCA binary-to-gray converter and vice versa. For the second proposal, both reversible and irreversible structures are suggested. The proposed structures are scalable up to N bits. To change the conversion type from B2G to G2B, we use a 2:1 QCA multiplexer. The proposed QCA Fredkin is applied in the reversible design of QCA code converters as multiplexers. The suggested designs are simulated using the QCADesigner tool. Then we calculated figures of merit, including cell counts, occupied areas, and clock zones. Finally, we compare the proposed structures to existing research. Our proposed approach is the first quantum-dot cellular automata design to perform B2G conversion and G2B in a single QCA circuit. The proposed designs are scalable. Specifications are reported. [ABSTRACT FROM AUTHOR]

Quantum computing (QC) holds great promise to open up a new era of computing and has been receiving significant attention recently. To overcome the performance limitations of near-term QC, utilizing the current quantum computers to complement classical techniques for solving real-world problems is of utmost importance. In this article, we develop QC-based solution strategies that exploit quantum annealing and classical optimization techniques for solving large-scale scheduling problems in manufacturing systems. The applications of the proposed algorithms are illustrated through two case studies in production scheduling. First, we present a hybrid QC-based solution approach for the job-shop scheduling problem. Second, we propose a hybrid QC-based parametric method for the multipurpose batch scheduling problem with a fractional objective. The proposed hybrid algorithms can tackle optimization problems formulated as mixed-integer linear and mixed-integer fractional programs, respectively, and provide feasibility guarantees. Performance comparison between state-of-the-art exact and heuristic solvers and the proposed QC-based hybrid solution techniques is presented for both job-shop and batch scheduling problems. Unlike conventional classical solution techniques, the proposed hybrid frameworks harness quantum annealing to supplement established deterministic optimization algorithms and demonstrate performance efficiency over standard off-the-shelf optimization solvers.

This article aims to investigate how circuit-based hybrid quantum convolutional neural networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of convolutional neural networks by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the land-use and land-cover classification, chosen as an Earth observation (EO) use case, and tested on the EuroSAT dataset used as the reference benchmark. The results of the multiclass classification prove the effectiveness of the presented approach by demonstrating that the QCNN performances are higher than the classical counterparts. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for future investigations.

Quantum computing is experiencing an inflection point. This technology is likely to facilitate great advances in many fields including logistics, chemistry simulation, or even risk analysis. It is attractive for large hardware and software companies in addition to start-up entrepreneurial enterprises. In this article, we summarize interesting insights into this field related to possibilities related to quantum computing and entrepreneurship. [ABSTRACT FROM AUTHOR]

High-frequency trading (HFT) offers an excellent use case and a potential killer application of the commercially available, first generation quasi-quantum computers. To this end, we offer here a simple game-theoretic model of HFT as the famous two player game, Prisoner’s Dilemma. We explore the implementation of HFT as an instance of Prisoner’s Dilemma on the (quasi) quantum cloud using the Eisert, Wilkens, and Lewenstein quantum mediated communication protocol, and how this implementation can not only increase transaction speed but also improve the lot of the players in HFT. Using cooperative game-theoretic reasoning, we also note that in the near future when the internet is properly quantum, players will be able to achieve Pareto-optimality in HFT as an instance of reinforced machine learning.

Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing (QC) applications that might save years of engineering effort. Current quantum software stacks follow a layered approach similar to the stack of classical computers, which was designed to manage the complexity. In this review, we point out that greater efficiency of QC systems can be achieved by breaking the abstractions between these layers. We review several works along this line, including two hardware-aware compilation optimizations that break the quantum instruction set architecture (ISA) abstraction and two error-correction/information-processing schemes that break the qubit abstraction. Last, we discuss several possible future directions. [ABSTRACT FROM AUTHOR]

Quantum computing (QC) provides an efficient platform to solve complex problems such as number factoring and searching. The quantum Fourier transform (QFT) is an integral part of quantum algorithms for integer number factoring, phase estimation, discrete algorithms, interchange of position and momentum states, quantum key distribution protocol, multiparty quantum telecommunication, and quantum arithmetic. The theoretical and experimental implementations of QFT on various platforms have been proposed by researchers. Spin-torque-based qubit(s) manipulation is one of the encouraging solid-state device technologies. However, to date, QFT is not realized by spin-torque-based QC architecture. In this article, the spin-torque-based architecture has been modeled with the help of optimized decomposition of quantum circuits for the QFT. Moreover, an optimal-depth Clifford + T gates set-based quantum circuit is utilized to implement the QFT. The performance analysis in terms of fidelity (>99%), magnitude, and phase difference of respective density matrices for different forms of three-qubit QFT provides a novel way of its physical realization to address the complex problems. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid algorithm called quantum multiverse optimization (QMVO) is proposed. The proposed QMVO is based on quantum computing and multiverse optimization (MVO) algorithm. The main features of quantum theory and MVO were applied in a new algorithm to find the optimal trade-off between exploration and exploitation. QMVO algorithm depends on adopting a quantum representation of the search space and the integration of the quantum interference and operators in the multiverse optimization algorithm to obtain the optimal solution of the objective function. The performance of QMVO algorithm is evaluated by using 50 unimodal and multimodal benchmark functions. The experimental results show that the proposed algorithm has comprehensive superiority in solving complex numerical optimization problems. Also, the results show that the proposed QMVO is a promising optimization algorithm compared with other well-known and popular algorithms. [ABSTRACT FROM AUTHOR]

The semiconductor industry is facing a twofold challenge at sub-nanometer level; first, the high power dissipation in irreversible computing architectures due to information loss and second, inability to handle large data due to sequential information processing. These issues create obstacles in producing the presumed low-power complex computing outcomes. The heat dissipation can be reduced by utilizing the complementary metal–oxide–semiconductor-based reversible computing architectures. However, these architectures fail to enhance the performance owing to inability to process large data. Quantum computing (QC) can circumvent these problems due to its fundamental ineffaceable characteristics of quantum mechanics-based reversible computing and parallelism. Spin-torque-based physical realization is the most suitable platform for reversible computing due to the electron spin analogous to the qubit. However, optimal quantum circuits are required to physically realize the complex Boolean logic due to spin-qubit decoherence and reduce the number of transistor switching activities for the spin generation and injection required for the spin-qubit rotation. Therefore, in this paper, optimal quantum circuit decompositions are presented with the help of developed elementary quantum library { $R_{y}^{(\theta)}$ , $R_{z}^{(\theta)}$ , $\sqrt {\text {SWAP}}$ } for the spin-torque-based QC architecture. The reversible Boolean logic performance is analyzed and compared for the conventional, reduced, and optimal decompositions on the first- and second-order transmission coefficient matrix-based spin-torque QC architecture. The results encourage to set a path toward QC-based reconfigurable complex computing systems in near future. [ABSTRACT FROM AUTHOR]

Spin-torque-based quantum computing (QC) architecture is emerging as one of the novel technologies to meet scalability challenge due to its intra architecture spin qubit state manipulation. The existing model for spin-torque-based QC does not include the ratio of reflection barrier height to exchange interaction to the transmission coefficients. Therefore, in this paper, a modified matrix is proposed to analyze the effect of ratio of reflection barrier height to exchange interaction on electron–qubit interaction, deviation of axis of rotation for single-qubit rotation, and average error probability for two-qubit rotation in a spin-torque-based ${n}$ -qubit reconfigurable architecture. In addition, the conventional and reduced quantum gates are compared for existing and modified matrices. The quantum gates performance is analyzed in terms of number of electrons required per gate for the electron–qubit interaction, gate fidelity, number of elementary quantum operations per gate, and gate execution time. Reconfigurability is accomplished through barrier height modulation to reduce the architecture hardware. Moreover, a novel nanomagnet-based spin reservoir is incorporated in the architecture for the nonlocal spin injection to facilitate only spin-based operations. High fidelity (~99%) of quantum gates is attained for the fault-tolerant QC. [ABSTRACT FROM AUTHOR]

Owing to the advancements in low-power consumption processors and high-power storage in a small-sized battery, the cost of handheld mobile devices, eg, mobiles, tabs, or personal digital assistants, have reduced to a great extent. This has enabled people to have at least 1 smartphone in general with this number increasing exponentially. However, the increasing use of these mobile devices results in an equal increase in the underused processing capacity of these devices too. This encourages the research aiming to use this processing power by forming a mobile computational grid. Because of the inherent limitations of bandwidth, battery, and computational power, job scheduling on these devices demands an efficient scheduling approach to harness the true potential of the grid. The problem becomes even more challenging considering the dynamic nature of these mobile devices. Job scheduling being nondeterministic polynomial time-complete allows the use of evolutionary approaches by exploring and exploiting the search space efficiently. The exploration gets boosted even more with the use of quantum-computing concepts. This work proposes a quantum-inspired Newtonian approach of attraction based on gravitational search algorithm for scheduling the jobs on mobile computational grid. Simulation study has been performed to evaluate the performance of the model over various dimensions. A comparative study has been performed with quantum-genetic algorithm. Simulation result establishes the effectiveness of model under various test conditions. [ABSTRACT FROM AUTHOR]

High-frequency trading (HFT) offers an excellent use case and a potential killer application of the commercially available, first generation quasi-quantum computers. To this end, we offer here a simple game-theoretic model of HFT as the famous two player game, Prisoner's Dilemma. We explore the implementation of HFT as an instance of Prisoner's Dilemma on the (quasi) quantum cloud using the Eisert, Wilkens, and Lewenstein quantum mediated communication protocol, and how this implementation can not only increase transaction speed but also improve the lot of the players in HFT. Using cooperative game-theoretic reasoning, we also note that in the near future when the internet is properly quantum, players will be able to achieve Pareto-optimality in HFT as an instance of reinforced machine learning., Competing Interests: FSK is employed by Dark Star Quantum Lab Inc. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Khan and Bao.)