With the rapid development in the field of quantum computing, the security of traditional encryption algorithms is facing serious challenges. And quantum key distribution (QKD) provides a new way to solve the key dist...
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Grover's search algorithm (GSA) is known to experience a loss of its quadratic speedup when exposed to quantum noise. In this study, we partially agree with this result and present our findings. First, we examine ...
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Grover's search algorithm (GSA) is known to experience a loss of its quadratic speedup when exposed to quantum noise. In this study, we partially agree with this result and present our findings. First, we examine different typical diagonalizable noises acting on the oracles in GSA and find that the success probability decreases and oscillates around 1/2 as the number of iterations increases. Secondly, our results show that the performance of GSA can be improved by certain types of noise, such as bit flip and bit-phase flip noise. Finally, we determine the noise threshold for bit-phase flip noise to achieve a desired success probability and demonstrate that GSA with bit-phase flip noise still outperforms its classical counterpart. These results suggest new avenues for research in noisy intermediate-scale quantum computing, such as evaluating the feasibility of quantumalgorithms with noise and exploring their applications in machine learning.
Twin-field quantum key distribution (TF-QKD) has the advantage of beating the rate-loss limit (PLOB bound) for a repeaterless quantum key distribution (QKD) system. In practice, parameter optimization is of great sign...
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Twin-field quantum key distribution (TF-QKD) has the advantage of beating the rate-loss limit (PLOB bound) for a repeaterless quantum key distribution (QKD) system. In practice, parameter optimization is of great significance in maximizing the secret key rate. Nevertheless, traditional local search algorithms (LSA) are often time-consuming and limited by the computing capabilities of devices. In this paper, we use the machine learning method instead of LSA to directly predict the optimal parameters for TF-QKD system. Specifically, three neural networks, namely back propagation neural network, radial basis function neural network, and generalized regression neural network, are trained and evaluated. The performance of neural networks and LSA in optimizing parameters is discussed and analyzed in this study. It is proved that the performance of machine learning-based prediction method is comparable to LSA, but the calculation time is shortened by 6 orders of magnitude. Furthermore, a comprehensive comparison of three networks in terms of prediction accuracy and time consumption is conducted, serving as a guide for selecting the most suitable network to optimize parameters in a practical TF-QKD system with different optimization requirements.
The quantum image segmentation algorithm is to divide a quantum image into several parts, but most of the existing algorithms use more quantum resource(qubit) or cannot process the complex image. In this paper, an imp...
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The quantum image segmentation algorithm is to divide a quantum image into several parts, but most of the existing algorithms use more quantum resource(qubit) or cannot process the complex image. In this paper, an improved two-threshold quantum segmentation algorithm for NEQR image is proposed, which can segment the complex gray-scale image into a clear ternary image by using fewer qubits and can be scaled to use n thresholds for n + 1 segmentations. In addition, a feasible quantum comparator is designed to distinguish the gray-scale values with two thresholds, and then a scalable quantum circuit is designed to segment the NEQR image. For a 2(n)x2(n) image with q gray-scale levels, the quantum cost of our algorithm can be reduced to 60q-6, which is lower than other existing quantumalgorithms and does not increase with the image's size increases. The experiment on IBM Q demonstrates that our algorithm can effectively segment the image.
quantumalgorithms are demonstrated to outperform classical algorithms for certain problems and thus are promising candidates for efficient informationprocessing. Herein we aim to provide a brief and popular introduc...
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quantumalgorithms are demonstrated to outperform classical algorithms for certain problems and thus are promising candidates for efficient informationprocessing. Herein we aim to provide a brief and popular introduction to quantumalgorithms for both the academic community and the general public with interest. We start from elucidating quantum parallelism, the basic framework of quantumalgorithms and the difficulty of quantum algorithm design. Then we mainly focus on a historical overview of progress in quantum algorithm research over the past three to four decades. Finally, we clarify two common questions about the study of quantumalgorithms, hoping to stimulate readers for further exploration.
We provide an adaptive learning algorithm for tomography of general quantum states. Our proposal is based on the simultaneous perturbation stochastic approximation algorithm and applies to mixed qudit states. The sali...
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We provide an adaptive learning algorithm for tomography of general quantum states. Our proposal is based on the simultaneous perturbation stochastic approximation algorithm and applies to mixed qudit states. The salient features of our algorithm are efficient post-processing in dimension d of the state, robustness against measurement and channel noise, and improved infidelity performance as compared to the contemporary adaptive state learning algorithms. A higher resilience against measurement noise makes our algorithm suitable for noisy intermediate-scale quantum applications.
Integrated photonic circuits play a crucial role in implementing quantuminformationprocessing in the noisy intermediate-scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical o...
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Integrated photonic circuits play a crucial role in implementing quantuminformationprocessing in the noisy intermediate-scale quantum (NISQ) era. Variational learning is a promising avenue that leverages classical optimization techniques to enhance quantum advantages on NISQ devices. However, most variational algorithms are circuit-model-based and encounter challenges when implemented on integrated photonic circuits, because they involve explicit decomposition of large quantum circuits into sequences of basic entangled gates, leading to an exponential decay of success probability due to the non-deterministic nature of photonic entangling gates. Here, a variational learning approach is presented for designing quantum photonic circuits, which directly incorporates post-selection and elementary photonic components into the training process. The complicated circuit is treated as a single nonlinear logical operator and a unified design is discovered for it through variational learning. Engineering an integrated photonic chip with automated control achieved by genetic algorithm, the internal parameters of the chip are adjusted and optimized in real-time for task-specific cost functions. A simple case of designing photonic circuits for a single ancilla CNOT gate with improved success rate is utilized to illustrate how the proposed approach works, and then the approach is applied to the first demonstration of quantum stochastic simulation using integrated photonics.
quantum network is an emerging type of network structure that leverages the principles of quantum mechanics to transmit and process information. Compared with classical data reconstruction algorithms, quantum networks...
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ISBN:
(纸本)9798350364613;9798350364606
quantum network is an emerging type of network structure that leverages the principles of quantum mechanics to transmit and process information. Compared with classical data reconstruction algorithms, quantum networks make image reconstruction more efficient and accurate. They can also process more complex image information using fewer bits and faster parallel computing capabilities. Therefore, this paper will discuss image reconstruction methods based on our quantum network and explore their potential applications in image processing. We will introduce the basic structure of the quantum network, the process of image compression and reconstruction, and the specific parameter training method. Through this study, we can achieve a classical image reconstruction accuracy of 97.57%. Our quantum network design will introduce novel ideas and methods for image reconstruction in the future.
This paper considers the problem of finding the (δ,Ε)-Goldstein stationary point of the Lipschitz continuous objective, which is a rich function class to cover a large number of important applications. We construct ...
作者：
Li, HuaZhang, MingyueChinese Acad Sci
Shanghai Inst Microsyst & Informat Technol Natl Key Lab Mat Integrated Circuits Shanghai 200050 Peoples R China
Superconducting quantum interference device (SQUID) magnetogastrogram (MGG) is a medical functional imaging method with great clinical potential for noninvasive diagnosis of gastric diseases. MGG signal frequency is a...
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Superconducting quantum interference device (SQUID) magnetogastrogram (MGG) is a medical functional imaging method with great clinical potential for noninvasive diagnosis of gastric diseases. MGG signal frequency is about 0.05 Hz, and the low-frequency environmental noise interference is serious, can be several times stronger in magnitude than the signals of interest, and may severely impede the extraction of relevant information. Wiener filter is one classic denoising solution for biomagnetic applications. In this article, a new high-pass Wiener filter and signal processing framework for MGG measurement is proposed, which can filter low-frequency noises not only specific artifacts. The filter was successfully applied to MGG signal denoising. Using this general Wiener filter frame, the filter signal-to-noise ratio (SNR) is 11.3 dB better than the classical Wiener filter and it also has 16.7 dB of SNR better than without signal noise separation step. Based on our methods, 36-point array MGG signals were detected successfully and the results were consistent with the main gastric slow wave activity.
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