Rotation Gate Research Articles

Quantum-based PRNG for image encryption: a comprehensive study

Quantum computing, grounded in the principles of quantum mechanics, has ushered in a new era of intriguing possibilities and fresh scientific insights. In this document, we present an innovative algorithm tailored for the encryption and decryption of images. This algorithm harnesses the quasi-probability values extracted from each state within the quantum system, represented as | ψ 〉 . These numerical values are seamlessly integrated with conventional data to facilitate image encryption and decryption processes. Our methodology undergoes comprehensive validation and evaluation through extensive simulations performed on the IBM Qiskit Aer simulator. We subject the generated numerical sequence to rigorous statistical scrutiny using the NIST SP 800-22 suite, ensuring its authenticity and reliability. Employing a quantum system comprising 3 qubits and uniform rotation gates, our algorithm produces a dataset consisting of 41,943,040 binary digits. Additionally, we conduct a thorough assessment encompassing both visual and statistical analyses to ascertain the effectiveness and robustness of our proposed algorithm. Furthermore, we emphasize the critical importance of achieving a quasi-probability error rate of 10 − 6 for real-world implementation, highlighting the practical viability and scalability of our approach. Through comparative analysis and meticulous examination of correlation patterns, our study provides invaluable insights into the distinctive characteristics and efficacy of our encryption scheme.

A research on resistance spot welding quality judgment of stainless steel sheets based on revised quantum genetic algorithm and hidden markov model

In view of the following problems existed in the data-driven resistance spot welding (RSW) quality judgment methods: relying on expert experience, resulting in a high misjudgment rate; affected by the initial value, it is easy to fall into the local extreme point; multiple parameters needed to be modulated to increase the training time of the model. Therefore, this paper proposed a RSW quality judgment method based on revised quantum genetic algorithm (RQGA) and hidden markov model (HMM). Which used quantum rotation gate to replace the evolution process of genetic algorithm (GA), reducing the number of parameters that needed to be modulated in the model, and optimizing the initial model of HMM by RQGA to avoid falling into local extreme point. At the same time, combining expert experience with HMM to make quality judgment to ensure the accuracy of judgment.Firstly, by RSW test to obtain the main influencing factors of quality, and combining with expert experience to realize the preliminary judgment of it, on this basis, HMM was established. Next, to address the susceptibility of HMM to initial model, quantum genetic algorithm (QGA) was utilized to search for the optimal initial model. Moreover, in view of the limitations of QGA (“coding length disaster” and fixed rotation angle), a corresponding improvement was also presented. At last, the established model was applied to the RSW process of stainless steel sheets railway carriage roof. Compared with the other five models, it took the least time to complete parameter training of HMM in four quality states, less than 15s; when judging the quality, its log-likelihood probability value was −47.8418, which was close to the maximum value of this state; during the classification test, the average classification accuracy of the four quality states were 90.8 %, 92.2 %, 90.8 % and 92.0 %, respectively, which was higher than the other five models. Consequently, the model established in this paper was effective.

The Quantum Cyclic Rotation Gate

A circular shift operator (or cyclic rotation gate) ROTk\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\,\\mathrm{\ exttt {ROT}}\\,}}_k$$\\end{document} applies a rightward (or leftward) shift to an input register of n qubits o by as many positions as encoded by an additional input k∈N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k \\in \\mathbb N$$\\end{document}. Specifically, the qubit at position x is moved to position (x+k)modn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(x+k) \\mod n$$\\end{document}. While it is known that there exists a quantum rotation operator that can be implemented in O(log(n))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\,\\mathrm{\\mathcal {O}}\\,}}(\\log (n))$$\\end{document}-time, through the repeated parallel application of the elementary Swap\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\,\\mathrm{\ exttt {Swap}}\\,}}$$\\end{document} operators, there is no systematic procedure that concretely constructs the quantum operator ROT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\,\\mathrm{\ exttt {ROT}}\\,}}$$\\end{document} for variable size n of the quantum register and a variable parameter k. We fill the gap, providing a systematic implementation of the cyclic rotation operator (denoted ROT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\,\\mathrm{\ exttt {ROT}}\\,}}$$\\end{document}) in a quantum circuit model of computation whose depth is O(log(n))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\,\\mathrm{\\mathcal {O}}\\,}}(\\log (n))$$\\end{document}. We show how the circular shift operator can be utilized in quantum approaches to text processing, focusing on the problem of getting all possible cyclic rotations of a string in O(log2(n))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\,\\mathrm{\\mathcal {O}}\\,}}(\\log ^2(n))$$\\end{document} depth.

Fast super robust nonadiabatic geometric quantum computation

Nonadiabatic geometric quantum computation (NGQC) provides a method for implementing fast and robust quantum gates. To enhance the robustness of NGQC against control errors, many solutions have been proposed by previous researchers. However, these solutions often lead to longer operation times for quantum gates. To simultaneously enhance quantum gate robustness against control errors and reduce operation times to mitigate decoherence effects, we introduce fast super robust NGQC (FSR-NGQC). This approach achieves faster speeds when operating small-angle rotation gates. Through numerical calculations, we have demonstrated the performance of our scheme in a decoherence environment. The results show that our scheme achieves higher fidelity, thus enabling fast and robust geometric quantum computing.

Efficient entanglement enhancement of partially entangled pairs

We present a comprehensive approach to transform a partially entangled pure state into a maximally entangled state, utilizing only local operations and a single copy of the entangled state. Our approach eliminates the need for multiple copies of entangled states, bilateral operations, and the time-consuming partial-collapse weak measurements. Integrating ancilla qubits, rotation gates, and CNOT gates, we simplify the process of enhancing entanglement, achieving significant efficiency using only a single partially entangled state. Assuming perfect local operations, we showcase the efficacy of our method through both mathematical analysis and the practical implementation of the corresponding circuit in Qiskit. Furthermore, we explore the impact of imperfect local operations, specifically focusing on the CNOT gate and measurement, for a comprehensive analysis. Additionally, we address the case of unknown parameters in the partially entangled pure state and introduce a robust method to effectively handle this uncertainty, ensuring reliable entanglement fidelity optimization in practical situations. We extend our investigation to the application of our method to mixed initial entangled states. Our results demonstrate the reliability of our method in increasing entanglement even in the presence of phase damping and amplitude damping. Additionally, we illustrate its effectiveness in transforming specific mixed initial states in the presence of depolarization into states with enhanced entanglement.

Shor’s algorithm does not factor large integers in the presence of noise

We consider Shor’s quantum factoring algorithm in the setting of noisy quantum gates. Under a generic model of random noise for (controlled) rotation gates, we prove that the algorithm does not factor integers of the form pq when the noise exceeds a vanishingly small level in terms of n—the number of bits of the integer to be factored, where p and q are from a well-defined set of primes of positive density. We further prove that with probability 1 − o(1) over random prime pairs (p, q), Shor’s factoring algorithm does not factor numbers of the form pq, with the same level of random noise present.

Optimal joint cutting of two-qubit rotation gates

Optimal joint cutting of two-qubit rotation gates

Quantum machine learning for drowsiness detection with EEG signals

Human reliability is an increasingly important area in various fields for accident prevention. Monitoring human biological parameters, such as metabolic agents, through techniques like an electroencephalogram (EEG), data analysis can help detect patterns indicating drowsiness, a major cause of fatigue that may impact tasks in various industries, including oil and gas, aviation, naval, railway, and others that involve shift work. While traditional machine learning methods, such as Multilayer Perceptron (MLP), have been explored in the literature for EEG-based drowsiness detection, advancements in computing technology have brought quantum mechanics concepts into play, offering potential advantages in computational efficiency for problem-solving. This work explores drowsiness detection via Quantum Machine Learning (QML). EEG signals are preprocessed to extract features specific to this type of data, such as Higuchi Fractal Dimension, Complexity, and Mobility, as well as statistical features such as mean, variance, root mean square, peak-to-peak, and maximum amplitude. We employ different quantum circuit architectures involving operations such as rotation gates (Ry, Rz, Ry), and entangling gates (CNOT, CZ, and iSWAP). We also combine those configurations considering different numbers of layers (1, 5, and 10). The models are trained and compared with classical MLP, considering five subjects. The main findings indicate that one subject (10) showed better results with the classic MLP model. However, for two subjects (1 and 8), iSWAP gates with 1 and 10 layers were notable, whereas for the last two subjects (5 and 6), configurations of CZ gates with 1 and 10 layers displayed the best results. This proof-of-principle study shows that QML models are suitable for analyzing EEG data related to drowsiness and can be further improved as quantum computing continues to evolve.

Fast coherent control of nitrogen-14 spins associated with nitrogen-vacancy centers in diamonds using dynamical decoupling

Abstract A nitrogen-vacancy (NV) center in a diamond enables the access to an electron spin, which is expected to present highly sensitive quantum sensors. Although exploiting a nitrogen nuclear spin improves the sensitivity, manipulating it using a resonant pulse requires a long gate time owing to its small gyromagnetic ratio. Another technique to control nuclear spins is a conditional rotation gate based on dynamical decoupling, which is faster but unavailable for nitrogen spins owing to the lack of transverse hyperfine coupling with the electron spin. In this study, we generated effective transverse coupling by applying a weak off-axis magnetic field. An effective coupling depends on the off-axis field; the conditional rotation gate on the nitrogen-14 spins of an NV center was demonstrated within 4.2 μs under an 1.8% off-axis field and a longitudinal field of approximately 280 mT. We estimated that a population transfer from the electron to nitrogen spins can be implemented with 8.7 μs. Our method is applicable to an ensemble of NV centers, in addition to a single NV center.

Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs

Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.

Quantum gate-assisted teleportation in noisy environments: robustness and fidelity improvement

Quantum teleportation as the key strategy for quantum communication requires pure maximally shared entangled states among quantum nodes. In practice, quantum decoherence drastically degrades the shared entanglement during entanglement distribution, which is a serious challenge for the development of quantum networks. However, most of the decoherence control strategies proposed thus far are either resource-intensive or time-consuming. To overcome this obstacle, we enable noise-resistant teleportation through a noisy channel with a limited number of qubits and without applying time-consuming weak measurements. We apply a quantum gate control unit consisting of a controlled NOT gate and a rotation gate after the original teleportation protocol is accomplished. Furthermore, we demonstrate that a teleportation fidelity of unity is attainable when environment-assisted measurement is added to the proposed teleportation protocol via quantum gates. Moreover, we present an entanglement distribution process by employing the designed quantum gate control unit followed by the deterministic standard teleportation protocol to improve teleportation fidelity by establishing improved shared entanglement. Our performance analysis indicates that the proposed teleportation schemes offer a competitive fidelity and success probability compared with the conventional schemes and a recent weak measurement-based teleportation protocol.

An efficient task offloading method for drip irrigation and fertilization at edge nodes based on quantum chaotic genetic algorithm

Minimizing the offloading latency of agricultural drip irrigation and fertilization tasks has long been a pressing issue in agricultural drip irrigation and fertilization wireless sensor networks (AIFWSNs). The introduction of edge computing as a robust and practical aid to cloud computing in AIFWSNs can significantly improve the execution speed of agricultural drip irrigation and fertilization tasks and effectively reduce the task offloading latency. Therefore, this paper investigates the optimization method of drip irrigation and fertilization task offloading delay in AIFWSNs based on edge computing and proposes a new edge task offloading method for AIFWSNs, namely, Quantum Chaotic Genetic Optimization Algorithm (QCGA). This paper introduces a novel quantum operator in QCGA, comprising a quantum non-gate and a quantum rotation gate, to improve the algorithm’s global search capability. The quantum operator accomplishes the updating of quantum rotating gates without querying the quantum rotation angle table, which reduces the computational complexity of introducing quantum optimization into the task offloading problem of AIFWSNs. This paper proposes a new chaotic operator to make the initial solution more uniformly distributed in the search space by chaotic mapping. This paper’s simulation experiments compared QCGA and snake optimizer (SO), genetic algorithm (GA), particle swarm optimization (PSO), sequential offloading, and random offloading methods. Simulation results showed that, compared with SO, GA, PSO, sequential offloading, and random offloading methods, the average delay of QCGA was reduced by 9.96%, 26.78%, 29.31%, 44.67%, and 61.24%.

Adjustable rotation gate based quantum evolutionary algorithm for energy optimisation in cloud computing systems

Adjustable rotation gate based quantum evolutionary algorithm for energy optimisation in cloud computing systems

A quantum image encryption method for dual chaotic systems based on quantum logistic mapping

On the basis of using quantum NEQR (novel enhanced quantum representation of digital image) to display images, a dual chaos system based on quantum logistic mapping is proposed to encrypt quantum images to ensure the security of quantum image transmission. The encryption algorithm is based on quantum logistic mapping and Chen chaos system to generate chaotic sequences, and uses quantum rotation gate operations to rotate and transform each pixel of the quantum image to achieve the effect of image encryption. Traditional quantum image encryption usually uses classical randomly generated sequences to construct the encryption angle of the quantum rotating door. This method combines the randomness of measured quantum with the chaotic system to obtain a truly random sequence. Using this random sequence can better Keep images confidential. Experimental results show that this method has high security and sensitivity to keys. In the sensitivity analysis of the results of the simulation experiment, its NPCR (Number of Pixels Change Rate) values floated around 99.60%. In the field of image encryption, the reliability of image encryption is greatly enhanced.

Quantum Tapsilou—A Quantum Game Inspired by the Traditional Greek Coin Tossing Game Tapsilou

This paper introduces a new quantum game called Quantum Tapsilou that is inspired by the classical traditional Greek coin tossing game tapsilou. The new quantum game, despite its increased complexity and scope, retains the most important characteristic of the traditional game. In the classical game, both players have 14 probability to win. The quantum version retains this characteristic feature, which is that both players have the same probability to win, but only now this probability varies considerably and depends on previous moves and choices. The two most important novelties of Quantum Tapsilou can be attributed to its implementation of entanglement via the use of rotation gates instead of Hadamard gates, which generates Bell-like states with unequal probability amplitudes, and the integral use of groups. In Quantum Tapsilou both players agree on a specific cyclic rotation group of order n, for some sufficiently large n. The game is based on the chosen group, in the sense that both players will draw their moves from its elements. More specifically, both players will pick rotations from this group to realize their actions using the corresponding Ry rotation gates. In the Quantum Tapsilou game, it is equally probable for both players to win. This fact is in accordance with a previous result in the literature showing that quantum games where both players choose their actions from the same group, exhibit perfect symmetry by providing each player with the possibility to pick the move that counteracts the other player’s action.

Multi-objective niching quantum genetic algorithm-based optimization method for pneumatic hammer structure

Pneumatic down-the-hole (DTH) hammer drilling technology has been used extensively in various drilling applications owing to its high drilling efficiency, low gas consumption, non-polluting nature, and safety. Optimizing the hammer structure using empirical approaches is challenging due to the highly complex nonlinear problem caused by the piston movement. To address it, the intelligent algorithm is introduced for the first time and a multi-objective niching quantum genetic algorithm-based (MNQGA) optimization method for pneumatic hammer structure design is proposed in this study, to simultaneously maximize impact energy and minimize air consumption or optimize these objectives individually. Additionally, a novel niching strategy, corresponding adaptive quantum rotation gate, and mutation strategy are designed for the proposed method. The radial structures of a GQ127-type pneumatic hammer are optimized to maximize impact energy, followed by the optimization of the axial structures to satisfy four working conditions. The results show that different optimization objectives and constraints lead to different axial structural parameter values. After optimization, the hammer air consumption is reduced by at least 50.80 % at a constant impact energy of 350 J. The hammer impact energy is improved by more than 15.93 % if the volumetric flow rate of the supplied air is maintained at 10 m3/min. In addition, the effects of the hammer’s axial structure on its impact energy are investigated in detail based on an analytical model to validate the results of the proposed method. Five axial structure parameters are analyzed that have different effects on the impact energy. Within the given constraints, these parameters have their own optimal values that can be easily obtained using the proposed method. That is, the optimizations using the proposed method are consistent with those of the analytical models published in the literature. Therefore, the proposed method is efficient and accurate and can be used confidently when designing pneumatic hammers.

EQNAS: Evolutionary Quantum Neural Architecture Search for Image Classification

Quantum neural network (QNN) is a neural network model based on the principles of quantum mechanics. The advantages of faster computing speed, higher memory capacity, smaller network size and elimination of catastrophic amnesia make it a new idea to solve the problem of training massive data that is difficult for classical neural networks. However, the quantum circuit of QNN are artificially designed with high circuit complexity and low precision in classification tasks. In this paper, a neural architecture search method EQNAS is proposed to improve QNN. First, initializing the quantum population after image quantum encoding. The next step is observing the quantum population and evaluating the fitness. The last is updating the quantum population. Quantum rotation gate update, quantum circuit construction and entirety interference crossover are specific operations. The last two steps need to be carried out iteratively until a satisfactory fitness is achieved. After a lot of experiments on the searched quantum neural networks, the feasibility and effectiveness of the algorithm proposed in this paper are proved, and the searched QNN is obviously better than the original algorithm. The classification accuracy on the mnist dataset and the warship dataset not only increased by 5.31% and 4.52%, respectively, but also reduced the parameters by 21.88% and 31.25% respectively. Code will be available at https://gitee.com/Pcyslist/models/tree/master/research/cv/EQNAS, and https://github.com/Pcyslist/EQNAS.

A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique.

Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm's mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS' 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results.

Memoryless Quantum Repeaters Based on Cavity‐QED and Coherent States

AbstractA quantum repeater scheme based on cavity‐quantum electrodynamics (QED) and quantum error correction of channel loss via rotation‐symmetric bosonic codes (RSBCs) is proposed to distribute atomic entangled states over long distances without memories and at high clock rates. In this scheme, controlled rotation gates, i.e., phase shifts of the propagating light modes conditioned upon the state of an atom placed in a cavity, provide a mechanism both for the entangled‐state preparations and for the error syndrome identifications. In order to assess the performance of this repeater protocol, an explicit instance of RSBCs—multicomponent cat codes—are studied quantitatively. It is found that the total fidelity and the success probability for quantum communication over a long distance (such as 1000 km) both can almost approach unity provided a small enough elementary distance between stations (smaller than 0.1 or 0.01 km) and rather low local losses (up to 0.1%) are considered. In a quantum key distribution application, secret key rates can become correspondingly high, both per channel use, beating the repeaterless bound, and per second thanks to the relatively high clock rates of the memoryless scheme. Based upon the cavity‐QED setting, this scheme can be realized at room temperature and at optical frequencies.

Solving independent set problems with photonic quantum circuits

An independent set (IS) is a set of vertices in a graph such that no edge connects any two vertices. In adiabatic quantum computation [E. Farhi, et al., Science 292, 472-475 (2001); A. Das, B. K. Chakrabarti, Rev. Mod. Phys. 80, 1061-1081 (2008)], a given graph G(V, E) can be naturally mapped onto a many-body Hamiltonian [Formula: see text], with edges [Formula: see text] being the two-body interactions between adjacent vertices [Formula: see text]. Thus, solving the IS problem is equivalent to finding all the computational basis ground states of [Formula: see text]. Very recently, non-Abelian adiabatic mixing (NAAM) has been proposed to address this task, exploiting an emergent non-Abelian gauge symmetry of [Formula: see text] [B. Wu, H. Yu, F. Wilczek, Phys. Rev. A 101, 012318 (2020)]. Here, we solve a representative IS problem [Formula: see text] by simulating the NAAM digitally using a linear optical quantum network, consisting of three C-Phase gates, four deterministic two-qubit gate arrays (DGA), and ten single rotation gates. The maximum IS has been successfully identified with sufficient Trotterization steps and a carefully chosen evolution path. Remarkably, we find IS with a total probability of 0.875(16), among which the nontrivial ones have a considerable weight of about 31.4%. Our experiment demonstrates the potential advantage of NAAM for solving IS-equivalent problems.