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Many standard linear algebra problems can be solved on a quantum computer by using recently developed quantum linear algebra algorithms that make use of block encodings and quantum eigenvalue/singular value transformations. A block encoding embeds a properly scaled matrix of interest A in a larger unitary transformation U that can be decomposed into a product of simpler unitaries and implemented efficiently on a quantum computer. Although quantum algorithms can potentially achieve exponential speedup in solving linear algebra problems compared to the best classical algorithm, such a gain in efficiency ultimately hinges on our ability to construct an efficient quantum circuit for the block encoding of A, which is difficult in general, and not trivial even for well structured sparse matrices. In this paper, we give a few examples on how efficient quantum circuits can be explicitly constructed for some well structured sparse matrices and discuss a few strategies used in these constructions. We also provide implementations of these quantum circuits in MATLAB.

It is crucial to design quantum circuits as small as possible and as shallow as possible for quantum information processing tasks. Quantum circuits are designed with simplified gate‐count, cost, and depth for implementing quantum teleportation among various entangled channels. Here, the gate‐count/cost/depth of the Greenberger‐Horne‐Zeilinger‐based quantum teleportation is reduced from 10/6/8 to 9/4/6, the two‐qubit‐cluster‐based quantum teleportation is reduced from 9/4/5 to 6/3/5, the three‐qubit‐cluster‐based quantum teleportation is reduced from 12/6/7 to 8/4/5, the Brown‐based quantum teleportation is reduced from 25/15/17 to 18/8/7, the Borras‐based quantum teleportation is reduced from 36/25/20 to 15/8/11, and the entanglement‐swapping‐based quantum teleportation is reduced from 13/8/8 to 10/5/5. Note that, no feed‐forward recover operation is required in the simplified schemes. Moreover, the experimentally demonstrations on IBM quantum computer indicate that the simplified and compressed schemes can be realized with good fidelity. [ABSTRACT FROM AUTHOR]

Quantum computers accelerate many algorithms based on the superposition principle of quantum mechanics. The Grover algorithm provides significant performance to malicious users attacking symmetric key systems. Since the performance of attacks using quantum computers depends on the efficiency of the quantum circuit of the encryption algorithms, research research on the implementation of quantum circuits is essential. This paper presents a new framework to construct quantum circuits of substitution boxes (S-boxes) using system modeling. We model the quantum circuits of S-boxes using two layers: Toffoli and linear layers. We generate vector spaces based on the values of qubits used in the linear layers and apply them to find quantum circuits. The framework finds the circuit by matching elements of vector spaces generated from the input and output of a given S-box, using the forward search or the meet-in-the-middle strategy. We developed a tool to apply this framework to 4-bit S-boxes. While the 4-bit S-box quantum circuit construction tool LIGHTER-R only finds circuits that can be implemented with four qubits, the proposed tool achieves the circuits with five qubits. The proposed tool can find quantum circuits of 4-bit odd permutations based on the controlled NOT, NOT, and Toffoli gates, whereas LIGHTER-R is unable to perform this task in the same environment. We expect this technique to become a critical step toward optimizing S-box quantum circuits. [ABSTRACT FROM AUTHOR]

Efficient decomposition of permutation unitaries is vital as they frequently appear in quantum computing. In this paper, we identify the key properties that impact the decomposition process of permutation unitaries. Then, we classify these decompositions based on the identified properties, establishing a comprehensive framework for analysis. We demonstrate the applicability of the presented framework through the widely used multi-controlled Toffoli gate, revealing that the existing decompositions in the literature belong to only four out of ten identified classes. Motivated by this finding, we propose transformations that can adapt a given decomposition into a member of another class, enabling resource reduction. [ABSTRACT FROM AUTHOR]

Quantum computing is described as a process by which a system calculates output. Quantum physics usually refers to the smallest discrete unit of any property; the basic unit of data in quantum is the qubit; this qubit unit is equivalent to the bit unit in classical neural networks. Quantum deep learning combines quantum computing with deep learning to reduce training time for neural networks, which has proven effective in solving some intractable problems on classical computers. Quantum deep learning has proven effective in solving some intractable problems on classical computers. A quantum network can benefit from quantum information flow because it is a more efficient framework than classical systems. Each quantum deep learning consists of a quantum gate. In this review, we provide a comprehensive review of recent studies that include different quantum deep learning applications, including healthcare, handwriting, and many others. Also, methodologies, problems, main datasets, results, strengths, limitations, and challenges are included in this review. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel protection scheme for N-qubit Greenberger–Horne–Zeilinger (GHZ) states against amplitude damping noise, using unilateral operations. The key innovation lies in the implementation of local operations on a single qubit, irrespective of the total number of qubits, thereby significantly reducing the operational complexity and resource requirements compared to existing methods. The scheme involves a unilateral rotation to transform the GHZ state into a less vulnerable configuration before exposing it to noise, followed by a weak measurement and another unilateral rotation to recover the initial state. We provide a comprehensive mathematical analysis of the proposed unilateral protection scheme, deriving expressions for both fidelity and success probability. The practical feasibility of our scheme is demonstrated through simulations using the Qiskit SDK, in which we provide the corresponding quantum circuit for performing the partial weak measurement used in our scheme. Comparative analysis with pioneer protection schemes reveals a substantial enhancement in success probability and fidelity for both GHZ and generalized GHZ states, emphasizing the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract Quantum computing has already demonstrated great computational potential across multiple domains and has received more and more attention. However, due to the connectivity limitations of Noisy Intermediate-Scale Quantum (NISQ) devices, most of the quantum algorithms cannot be directly executed without the help of inserting SWAP gates. Nevertheless, more SWAP gates lead to a longer execution time and, inevitably, lower fidelity of the algorithm. To this end, this paper proposes an optimized qubit mapping algorithm based on a dynamic look-ahead strategy to minimize the number of SWAP gates inserted. Firstly, a heuristic algorithm is proposed based on maximizing physical qubit connectivity to generate the optimal initial qubit mapping, which reduces the need for logical qubit shifts during subsequent SWAP gate insertion. Secondly, in the form of directed acyclic graphs, we identify quantum gates that violate the constraints of physical coupling and insert SWAP gates to remap qubits, thereby overcoming the limitations of qubit interactions. Finally, the optimal SWAP gate insertion strategy is built by comparing the cost of different SWAP gate insertion strategies through a multi-window look-ahead strategy to reduce the number of SWAP gates inserted. The experimental results show that the strategy in this paper decreases the number of SWAP gate insertions and significantly reduces the depth of quantum circuits when performing qubit mapping compared with state-of-the-art methods.

Copyright of Karaelmas Science & Engineering Journal / Karaelmas Fen ve Mühendislik Dergisi is the property of Karaelmas Science & Engineering Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Quantum computing has already demonstrated great computational potential across multiple domains and has received more and more attention. However, due to the connectivity limitations of Noisy Intermediate-Scale Quantum (NISQ) devices, most of the quantum algorithms cannot be directly executed without the help of inserting SWAP gates. Nevertheless, more SWAP gates lead to a longer execution time and, inevitably, lower fidelity of the algorithm. To this end, this paper proposes an optimized qubit mapping algorithm based on a dynamic look-ahead strategy to minimize the number of SWAP gates inserted. Firstly, a heuristic algorithm is proposed based on maximizing physical qubit connectivity to generate the optimal initial qubit mapping, which reduces the need for logical qubit shifts during subsequent SWAP gate insertion. Secondly, in the form of directed acyclic graphs, we identify quantum gates that violate the constraints of physical coupling and insert SWAP gates to remap qubits, thereby overcoming the limitations of qubit interactions. Finally, the optimal SWAP gate insertion strategy is built by comparing the cost of different SWAP gate insertion strategies through a multi-window look-ahead strategy to reduce the number of SWAP gates inserted. The experimental results show that the strategy in this paper decreases the number of SWAP gate insertions and significantly reduces the depth of quantum circuits when performing qubit mapping compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Poisson equation has many applications across the broad areas of science and engineering. Most quantum algorithms for the Poisson solver presented so far either suffer from lack of accuracy and/or are limited to very small sizes of the problem and thus have no practical usage. In this regard, our previous work (Robson in 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), 2022) showed a proof-of-concept demonstration in advancing quantum Poisson solver algorithm and validated preliminary results for a simple case of 3 × 3 problem. In this work, we delve into comprehensive research details, presenting the results on up to 15 × 15 problems that include step-by-step improvements in Poisson equation solutions, scaling performance, and experimental exploration. In particular, we demonstrate the implementation of eigenvalue amplification by a factor of up to 2 8 , achieving a significant improvement in the accuracy of our quantum Poisson solver and comparing that to the exact solution. Additionally, we present success probability results, highlighting the reliability of our quantum Poisson solver. Moreover, we explore the scaling performance of our algorithm against the circuit depth and width, demonstrating how our approach scales with larger problem sizes and thus further solidifies the practicality of easy adaptation of this algorithm in real-world applications. We also discuss a multilevel strategy for how this algorithm might be further improved to explore much larger problems with greater performance. Finally, through our experiments on the IBM quantum hardware, we conclude that though overall results on the existing NISQ hardware are dominated by the error in the CNOT gates, this work opens a path to realizing a multidimensional Poisson solver on near-term quantum hardware. [ABSTRACT FROM AUTHOR]

Contemporary industry and science expectations towards technological solutions set the bar high. Current approaches to increasing the computing power of standard systems are reaching the limits of physics known to humankind. Fast, programmable systems with relatively low power consumption are a different concept for performing complex calculations. Highly parallel processing opens up a number of possibilities in the context of accelerating calculations. Application of SoC (System On Chip) with FPGA (Field-Programmable Gate Array) enables the delegating of a part of computations to the gates matrix, thereby expediting processing by using parallelization of hardware operations. This paper presents the general concept of using SoC FPGA systems to support the CPU (Central Processing Unit) in many modern tasks. While some tasks might be really hard to implement on an FPGA in a reasonable time, the SoC FPGA platform allows for easy low-level interconnections, and with such virtualized access to the hardware computing resources, it is seen as making FPGAs, or hardware in general, more accessible to engineers accustomed to high-level solutions. The concept presented in the article takes into account the limited resources of cheaper educational platforms, which, however, still provide an interesting and alternative hybrid solution to the problem of parallelization and acceleration of data processing. This allows encountered limitations to be overcome and the flexibility known from high-level solutions and high performance achieved with low-level programming to be maintained without the need for a high financial background. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the quantum circuit implementations of the lightweight authenticated encryption algorithm ASCON by using the NOT gates, CNOT gates, Toffoli gates, measurements, and the dynamic quantum circuits. Firstly, the quantum circuit of addition of constants is realized by adding the NOT gates according to the position of 1 in round constants. Secondly, the quantum circuit of S-box of the permutation is synthesized according to the classical circuit diagram of S-box. Then the linear layer functions are expressed in matrix form, and their quantum circuits are synthesized according to Gaussian elimination. Finally, we synthesize the whole quantum circuits according to the general diagrams of the authenticated encryption algorithm ASCON. The correctness of the quantum circuits of the S-box and the linear layer was verified by the Aer simulator of the IBM Quantum platform. As far as we know, this is the first implementation of the quantum circuits for the Authenticated Encryption with Associated Data (AEAD) of ASCON in-place. The maximum quantum resources for the three ASCON authenticated encryption algorithms were estimated. The quantum circuit of ASCON-128 uses a total of 320 qubits, 30,639 NOT gates, 128,814 CNOT gates, 8064 Toffoli gates, 10,752 measurements, and 5376 dynamic quantum circuits. The quantum circuit of ASCON-128a uses a total of 320 qubits, 23,558 NOT gates, 98,144 CNOT gates, 6144 Toffoli gates, 8192 measurements, and 4096 dynamic quantum circuits. The quantum circuit of ASCON-80pq uses a total of 320 qubits, 30,736 NOT gates, 128,814 CNOT gates, 8064 Toffoli gates, 10,752 measurements, and 5376 dynamic quantum circuits. [ABSTRACT FROM AUTHOR]

SM4 cryptographic algorithm is a block cipher algorithm issued by China's state cryptographic administration and has become an international standard. We implement the quantum circuits of SM4 block cipher by optimizing the number of qubits and the value of depth-times-width. The quantum circuits of the S-box are first studied. According to the algebraic structure of the S-box, four kinds of improved quantum circuits of S-box are presented for different phases in SM4 based on composite field arithmetic. In order to optimize the number of qubits, we implement the quantum circuit of SM4 by connecting the quantum subcircuits in series. The implemented quantum circuit of SM4 only uses 260 qubits, which is the least number of qubits used not only in implementing the SM4 quantum circuit, but also in implementing the block cipher algorithms with 8-bit S-box, 128-bit plaintext and 128-bit secret key. When optimizing the value of depth-times-width, we achieve it through parallel implementation. The trade-off quantum circuit uses a total of 288 quantum bits, and the Toffoli depth is 1716. The depth-times-width is 49,4208, which is less than the existing best value 82,5792. [ABSTRACT FROM AUTHOR]

One of the main important features of the noisy intermediate-scale quantum (NISQ) era is the correct evaluation and consideration of errors. In this paper, we analyse the main sources of errors in current (IBM) quantum computers and we present a useful tool (TED-qc) designed to facilitate the total error probability expected for any quantum circuit. We propose this total error probability as the best way to estimate a lower bound for the fidelity in the NISQ era, avoiding the necessity of comparing the quantum calculations with any classical one. In order to contrast the robustness of our tool we compute the total error probability that may occur in three different quantum models: 1) the Ising model, 2) the Quantum-Phase Estimation (QPE), and 3) the Grover's algorithm. For each model, the main quantities of interest are computed and benchmarked against the reference simulator's results as a function of the error probability for a representative and statistically significant sample size. The analysis is satisfactory in more than the 99 % of the cases. In addition, we study how error mitigation techniques are able to eliminate the noise induced during the measurement. These results have been calculated for the IBM quantum computers, but both the tool and the analysis can be easily extended to any other quantum computer. [ABSTRACT FROM AUTHOR]

The initialization of desired quantum states is usually the starting element of quantum algorithms. For quantum image processing, inefficient preparation of quantum images hinders the implement of algorithms on quantum devices. In this study, a ready-to-use quantum circuit simplification method for preparing quantum images is proposed. By encoding the control qubits of the multi-qubit gates as Gray code, an optimized circuit for preparing lossless quantum images is constructed using a technique for simultaneously initializing adjacent pixels. This technique decreases the necessary number of control qubits and the depth of preparation circuits. By validating the proposed algorithm on a medical image dataset, it is demonstrated that a satisfactory depth reduction can be achieved without any quality loss when preparing quantum images. [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]

When implementing a quantum circuit for a desired reversible function, an attempt is made to design an accurate quantum circuit. Then, the quantum circuit is optimized based on a specific cost function, such as design cost, width (the number of qubits), depth, etc. In particular, if an in-place subcircuit itself can be optimized while maintaining its in-place property, it will be a very useful way to increase efficiency without changing the initial architecture of the entire quantum circuit. Furthermore, since its (clean) work qubits can easily be utilized in subsequent subroutines of the quantum circuit, it has an additional important advantage in terms of width. In this paper, for the first time to the best of our knowledge, we present a global Toffoli-depth reduction methodology for an in-place version reversible circuit in the case that the given input circuit is optimized with Toffoli-count. We mainly introduce a process to optimize the χ internal function block in SHA3-256 to explain our Toffoli-depth reduction approach preserving the in-place property, and hence, well-balanced five χ quantum circuits are induced in terms of its width and T-depth. And then, we apply these five χ circuits to design the entire SHA3-256 cryptosystem. One of the proposed SHA3-256 quantum circuits has a width of 1600 and a T-depth of 264, and this shows a result of 50% and 38.9% reduction compared to the previous circuit in terms of width and T-depth, respectively. Other versions of our SHA3-256 circuits just required 10–33 qubits per one T-depth compared to the previous results which require over 1800 qubits per T-depth, and so this means that our SHA3-256 circuits are well-balanced. Finally, we constructed Grover's algorithm circuit using each version that realized SHA3-256. When output circuits for the presented method were used, quantum volume values of Grover's algorithm circuits became 33 and 50 % of the value when the input circuit was used. [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]

Copyright of Journal of Cryptologic Research (2097-4116) is the property of Editorial Board of Journal of Cryptologic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Quantum circuit optimization is an inevitable task with the current noisy quantum backends. This task is considered non-trivial due to the varying circuits' complexities in addition to hardware-specific noise, topology, and limited connectivity. The currently available methods either rely on heuristics for circuit optimization tasks or reinforcement learning with complex unscalable neural networks such as transformers. In this paper, we are concerned with optimizing the initial logical-to-physical mapping selection. Specifically, we investigate whether a reinforcement learning agent with simple scalable neural network is capable of finding a near-optimal logical-to-physical mapping, that would decrease as much as possible additional CNOT gates, only from a fixed-length feature vector. To answer this question, we train a Maskable Proximal Policy Optimization agent to progressively take steps towards a near-optimal logical-to-physical mapping on a 20-qubit hardware architecture. Our results show that our agent coupled with a simple routing evaluation is capable of outperforming other available reinforcement learning and heuristics approaches on 12 out of 19 test benchmarks, achieving geometric mean improvements of 2.2% and 15% over the best available related work and two heuristics approaches, respectively. Additionally, our neural network model scales linearly as the number of qubits increases. [ABSTRACT FROM AUTHOR]

Circuit depth optimization is crucial for implementing quantum algorithms in practical systems. The decomposition of (n+1)$(n+1)$‐qubit Toffoli gate is continually optimized since this gate is a fundamental component for many quantum algorithms. Without any ancilla, (n+1)$(n+1)$‐qubit Toffoli gate can be decomposed by standard gates with circuit depth 33n−47$33n-47$. Here, the decomposition is further optimized and its circuit depth is reduced to 17n−38$17n-38$. The decomposition is equivalent to (n+1)$(n+1)$‐qubit Toffoli gate up to an overall phase and also does not need any ancilla. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to optimizing the linear layer of block ciphers using the matrix decomposition framework. It is observed that the reduction properties proposed by Xiang et al. (in FSE 2020) need to be improved. To address these limitations, we propose a new reduction framework with a complete reduction algorithm and swapping algorithm. Our approach formulates matrix decomposition as a new framework with an adaptive objective function and converts the problem to a Graph Isomorphism problem (GI problem). Using the new reduction algorithm, we were able to achieve lower XOR counts and depths of quantum implementations under the s-XOR metric. Our results outperform previous works for many linear layers of block ciphers and hash functions; some of them are better than the current g-XOR implementation. For the AES MixColumn operation, we get two implementations with 91 XOR counts and depth 13 of in-place quantum implementation, respectively.

We propose an efficient quantum subroutine for matrix multiplication that computes a state vector encoding the entries of the product of two matrices in superposition. The subroutine exploits efficient state preparation techniques and shows a potential speed-up with respect to classical methods. The most important benefit of our subroutine is that it encodes the entries of the matrix product directly in the state vector, which can be used for further computations within the same quantum circuit. All scenarios involving the computation of non-homomorphic functions of the product of two matrices can benefit from our technique. As a possible application, we discuss the computation of the variance of the entries of a matrix product, which can be a useful tool for some machine learning algorithms.

In this article, we present a hybrid quantum-classical generative adversarial network (GAN) for near-term quantum processors. The hybrid GAN comprises a variational generator and a discriminator quantum neural network, which are trained using a classic computer. The generator network is realized using angle-encoding and variational quantum circuits. The discriminator network is realized using multi-stage trainable encoding quantum circuits. A modular design approach is proposed for quantum neural networks which allows control on their depth to compromise accuracy and circuit complexity. Moreover, this modular approach makes the quantum neural networks amenable to scaling up the dimension of dataset and the number of qubits. Gradients of the loss functions are derived using the same quantum circuits used for the implementation of quantum neural networks to prevent the need for extra quantum circuits or auxiliary qubits. The quantum simulations are performed using the IBM Qiskit open-source software development kit (SDK), while the training of the hybrid quantum-classical GAN is conducted using the mini-batch stochastic gradient descent (SGD) optimization on a classic computer. The hybrid quantum-classical GAN is realized using a two-qubit system with different discriminator network structures. The best performance is achieved using a five-stage discriminator network comprising 63 quantum gates and 31 trainable parameters, with the Kullback-Leibler (KL) and Jensen-Shannon (JS) divergence scores of 0.39 and 0.52, respectively, for the similarity between the real and generated data distributions.

Exploiting machine learning techniques to automatically classify multispectral remote sensing imagery plays a significant role in deriving changes on the Earth’s surface. However, the computation power required to manage large Earth observation data and apply sophisticated machine learning models for this analysis purpose has become an intractable bottleneck. Leveraging quantum computing provides a possibility to tackle this challenge in the future. This article focuses on land cover classification by analyzing Sentinel-2 images with quantum computing. Two hybrid quantum-classical deep learning frameworks are proposed. Both models exploit quantum computing to extract features efficiently from multispectral images and classical computing for final classification. As proof of concept, numerical simulation results on the LCZ42 dataset through the TensorFlow Quantum platform verify our models' validity. The experiments indicate that our models can extract features more effectively compared with their classical counterparts, specifically, the convolutional neural network (CNN) model. Our models demonstrated improvements, with an average test accuracy increase of 4.5% and 3.3%, respectively, in comparison to the CNN model. In addition, our proposed models exhibit better transferability and robustness than CNN models.

A common model for classifying images is the convolutional neural network (CNN), which has the benefit of effectively using data correlation information. Despite their remarkable success, classical CNNs may face challenges in achieving further improvements in accuracy, computational efficiency, explainability, and generalization. However, if the specified data dimension or model grows too large, CNN becomes difficult to train effectively with a slowdown processing. In order to address a problem using CNN utilizing quantum computing, Quantum Convolutional Neural Network (QCNN) proposes a novel quantum solution or enhances the functionality of an existing learning model in terms of processing time during training. This paper presents a comparative analysis between classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture tailored for image-based tasks, emphasizing the adaptability and versatility of quantum circuits in enhancing feature extraction capabilities and then final accuracy and processing time. A MNIST and covidx-cxr3 datasets was used to train quantum-CNN models, and the results of these comparisons were made with traditional CNN performance. The results demonstrate that the suggested QCNN beat the traditional CNN in terms of recognition accuracy and processing speed (process time) when combined with cutting-edge feature extraction techniques. This superiority is particularly evident when trained on the covidx-cxr3 dataset, highlighting the potential for quantum computing to revolutionize image classification tasks.

Multiobjective optimization is a ubiquitous problem that arises naturally in many scientific and industrial areas. Network routing optimization with multiobjective performance demands falls into this problem class, and finding good quality solutions at large scales is generally challenging. In this work, we develop a scheme with which near-term quantum computers can be applied to solve multiobjective combinatorial optimization problems. We study the application of this scheme to the network routing problem in detail, by first mapping it to the multiobjective shortest-path problem. Focusing on an implementation based on the quantum approximate optimization algorithm (QAOA)—the go-to approach for tackling optimization problems on near-term quantum computers—we examine the Pareto plot that results from the scheme and qualitatively analyze its ability to produce Pareto-optimal solutions. We further provide theoretical and numerical scaling analyses of the resource requirements and performance of QAOA and identify key challenges associated with this approach. Finally, through Amazon Braket, we execute small-scale implementations of our scheme on the IonQ Harmony 11-qubit quantum computer.

Cardiac pathology classification (CPC) based on the volumetric features of three key heart structures can be extracted from segmented cardiac cine magnetic resonance imaging (CMRI) sequences. Machine learning models have recently become very effective tools for handling these problems. Hybrid quantum methods can be employed to enhance the capacities of classical machine learning models. Here, we propose a hybrid quantum multiclass cardiac pathologies classification (HQMC-CPC) model. In the proposed model, discriminative features are extracted to characterize the shape and function of the heart, combining the clinical features, patient features, and radiomics features. In the proposed quantum circuits, all variational parameters are trainable, and the enhanced variational quantum circuit is employed for efficient neural network learning. Using only thirty feature values as input, we propose a hybrid quantum multiclass cardiac pathologies classification (HQMC-CPC) model based on the proposed modified hardware efficient ansatz (MHEA). The proposed model achieves promising results in training and testing with the Automatic Cardiac Diagnosis Challenge (ACDC 2017) dataset. Experimental results showed that the proposed HQMC-CPC model is able to classify different cardiac pathologies with an average minimum performance gap of 3.19%. The average maximum improvement in terms of accuracy in cardiac pathology classification is 7.77%. Moreover, the proposed HQMC-CPC speeds up the testing process by around 60% and 40% compared to the classical classifiers and well-established HEA respectively.

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

A quantum Pauli noise is a nonunitary process that alters the state of a qubit by random application of the four Pauli operators. We investigate a four-qubit quantum circuit, consisting of a pipeline of three independently controlled Pauli gates, for simulating the general class of qubit Pauli noises. The circuit with a fixed architecture is controllable by three separable quantum states from three auxiliary qubits in order to adjust the parameters of the targeted Pauli noise on the principal qubit. Important Pauli noises such as bit flip, phase flip, bit phase flip, and depolarizing noise are readily simulated, along with an infinite subset of other Pauli noises. However, the quantum circuit with its simple and fixed architecture cannot simulate all conceivable Pauli noises, and a characterization is proposed, in the parameter space of the Pauli noises, denoting those that are simulable by the circuit and those that are not. The circuit is a useful tool to contribute to controlled simulation, on current or future quantum processors, of nonunitary processes of noise and decoherence. [ABSTRACT FROM AUTHOR]

Quantum computers have the potential to provide exponential speedups over their classical counterparts. Quantum principles are being applied to fields such as communications, information processing, and artificial intelligence to achieve quantum advantage. However, quantum bits are extremely noisy and prone to decoherence. Thus, keeping the qubits error free is extremely important toward reliable quantum computing. Quantum error correcting codes have been studied for several decades and methods have been proposed to import classical error correcting codes to the quantum domain. Along with the exploration into novel and more efficient quantum error correction codes, it is also essential to design circuits for practical realization of these codes. This article serves as a tutorial on designing and simulating quantum encoder and decoder circuits for stabilizer codes. We first describe Shor’s 9-qubit code which was the first quantum error correcting code. We discuss the stabilizer formalism along with the design of encoding and decoding circuits for stabilizer codes such as the five-qubit code and Steane code. We also design nearest neighbor compliant circuits for the above codes. The circuits were simulated and verified using IBM Qiskit. [ABSTRACT FROM AUTHOR]

Copyright of MB University International Review - MBUIR Journal of Theory & Practice is the property of University MB / Univerzitet MB and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, we present a quantum circuit for a binary classification prediction algorithm using a random forest model. The quantum prediction algorithm is presented in our previous works. We construct a circuit and implement it using qiskit tools (python module for quantum programming). One of our goals is reducing the number of basic quantum gates (elementary gates). The set of basic quantum gates which we use in this work consists of single-qubit gates and a controlled NOT gate. The number of CNOT gates in our circuit is estimated by , when trivial circuit decomposition techniques give CNOT gates, where is the number of trees in a random forest model, is a tree height and is the length of attributes of an input object . The prediction process returns an index of the corresponding class for the input . [ABSTRACT FROM AUTHOR]

Due to the existence of decoherence, researchers are limited in controlling large-scale qubits, which also prevents the application of Shor's factoring algorithm in the case of large-scale qubits for the time being. To reduce the number of qubits required when using Shor's factoring algorithm, by using borrowed ancilla qubits and reducing the number of gates in the constant addition circuit, a new quantum circuit for Shor's factoring algorithm is proposed. The designed circuit works on 2 n + 2 qubits, in practice is about 35% and 40% less than the best circuit of Takahashi et al. (Quantum Inf Comput 5(6):440–448, 2005) and Haner et al. (Quantum Inf Comput 17(7 &8):673–684, 2017) in terms of depth and size, respectively. Also, the designed circuit is completely general, and it does not depend on any property of the composite number to be factorized. Finally, we use Python with Qiskit to implement and simulate our circuit. [ABSTRACT FROM AUTHOR]

Noisy Intermediate-Scale Quantum (NISQ) computers consisting of tens of inherently noisy quantum bits (qubits) suffer from reliability problems. Qubits and their gates are susceptible to various types of errors. Due to limited numbers of qubits and high error rates, quantum error correction cannot be applied. Physical constraints of quantum hardware including the error rates are used to guide the design and the layout of quantum circuits. The error rates determine the selection of qubits and their operations. The resulting circuit is executed on the quantum computer.This study explores the risk of unexpected changes in the error rates of NISQ computers post-calibration. We show that unexpected changes in error rates can alter the output state of a quantum circuit. To detect these changes, we propose the insertion of test points into the quantum circuit to enable online monitoring of the physical qubit behavior. We utilize classical, superposition, and uncompute test points. Furthermore, we use a gate error coverage metric to assess the quality of the tests. We verify the effectiveness of the proposed scheme on different IBM quantum computers (IBM Q), in addition to a noisy simulation that shows the scalability of the proposed approach.