在当今科技飞速发展的时代，量子计算作为极具潜力的前沿领域，正不断探索着突破传统计算局限的新路径。然而，标准量子 预言机在面对一些复杂且特殊的计算需求时，逐渐显现出其局限性，难以满足日益多样化的科研及应用场景。微算法科技 （NASDAQ MLGO）开始深入研究非标准量子预言机，试图通过拓展预言机的功能，进一步推动量子计算的发展。非标准量子 预言机的研究不仅有助于深入理解量子计算的内在机制，还为解决复杂问题提供了新的思路和方法。 非标准量子预言机是相对于传统量子预言机而言的，它在功能上更加灵活多变。传统量子预言机通常被定义为一个黑箱函数， 用于在量子算法中执行特定的计算任务。而非标准量子预言机则打破了这一限制，它允许在预言机内部实现更复杂的逻辑运算 和数据处理，从而提高了量子算法的效率和适用范围。非标准量子预言机的核心在于其能够根据不同的应用场景，动态调整自 身的计算逻辑，以适应各种复杂问题的求解需求。 量子门操作：微算法科技并非遵循传统的、已被广泛认知的那一套固定逻辑和顺序，而是融入了实验中发现的一些新的量子相 互作用机制。科研人员巧妙地设计了一系列复杂且新颖的量子门组合，这些量子门在操控量子态的演化上有着别样 ...

Core Viewpoint - MicroAlgo Inc. has developed a Grover-based quantum algorithm aimed at finding pure Nash equilibria in graphical games, marking a significant advancement in quantum algorithm research and game theory applications [1][6]. Group 1: Algorithm Development - The Grover search algorithm is utilized for efficient searching in unstructured databases, achieving a time complexity of the square root of the number of elements [1]. - The algorithm transforms the oracle in graphical games into a Boolean satisfiability problem, encoding game states and strategies as quantum states [2]. - A method has been designed to convert Boolean expressions into quantum gate operations, ensuring the quantum circuit reflects strategy choices and payoff feedback [3]. Group 2: Implementation and Efficiency - Adjustments were made to the Grover algorithm to address efficiency bottlenecks in multi-objective or multi-dimensional problems, employing a stepwise iterative approach to improve search efficiency [4]. - The algorithm's iterative process maximizes the amplitude of the target state based on oracle feedback, enhancing the success rate of finding pure Nash equilibria [4]. Group 3: Experimental Validation - Extensive experiments on random graphical game instances using a quantum simulator demonstrated the algorithm's effectiveness, showing significant improvements in speed and accuracy compared to traditional methods [5]. - The algorithm exhibited a higher success rate and shorter computation time across multiple iterations in complex gaming environments [5]. Group 4: Future Implications - The Grover-based quantum algorithm is expected to play a key role in practical business decision-making, market analysis, and multi-party game scenarios, equipping decision-makers with advanced tools for complex competitive environments [7]. - MicroAlgo aims to expand the application boundaries of this technology through collaboration with academia and industry, potentially driving scientific progress and business innovation [8].

Core Viewpoint - The integration of Explainable AI (XAI) technology by Micro Algorithm Technology (NASDAQ: MLGO) significantly enhances threat detection capabilities in blockchain networks, providing transparency and understanding in decision-making processes [1][5]. Group 1: Technology and Methodology - Micro Algorithm Technology has developed an intelligent threat detection system that combines deep learning models with an explainability module, allowing for automatic learning of attack patterns from large volumes of network traffic data [1][3]. - The system employs data collection and preprocessing techniques to ensure data quality and consistency, followed by feature extraction and selection using machine learning algorithms [3]. - The threat detection model is trained and evaluated using explainable machine learning algorithms, ensuring high accuracy and interpretability through iterative optimization [3]. Group 2: Applications and Impact - The application of Micro Algorithm Technology's XAI in blockchain networks has led to significant improvements in threat detection accuracy and efficiency, particularly in identifying anomalous transactions and malicious nodes [4]. - The technology effectively detects unusual transaction patterns, such as large or frequent transactions, which may indicate fraudulent activities, enabling rapid response to mitigate losses [4]. - In smart contract auditing, the technology analyzes code logic to identify potential security vulnerabilities, thereby preventing attacks due to contract flaws [4]. Group 3: Future Prospects - As technology continues to evolve, Micro Algorithm Technology's solutions are expected to find broader applications, contributing to the creation of a more secure and intelligent network environment [5].

Core Viewpoint - The article discusses the increasing security issues in blockchain technology, particularly focusing on DDoS attacks and how quantum convolutional neural networks (QCNN) developed by Micro Algorithm Technology (NASDAQ: MLGO) can enhance detection and response capabilities against these threats [1][7]. Group 1: Quantum Convolutional Neural Network (QCNN) Development - Micro Algorithm Technology has innovatively improved QCNN for detecting DDoS attacks in blockchain networks by optimizing quantum bit initialization and control methods, enhancing stability and reliability [1][7]. - The structure of QCNN has been adjusted to better handle blockchain transaction data and network status information, making it more suitable for the specific characteristics of blockchain data [1][7]. - Specialized quantum state reading and parsing technologies have been developed to accurately extract features related to DDoS attacks from quantum computation results [1][7]. Group 2: Data Collection and Preprocessing - Data collection involves gathering various types of data from the blockchain network, including transaction data, node status information, and network traffic data, using APIs and monitoring tools [3]. - Preprocessing of collected data is crucial for the effective operation of QCNN, involving data cleaning, noise reduction, and standardization to ensure data quality [3]. - Feature extraction is performed to identify characteristics related to DDoS attacks, such as transaction frequency and network traffic changes, which serve as inputs for the QCNN [3]. Group 3: Quantum Operations - Quantum bit initialization ensures that quantum bits are in a stable initial state, balancing the number of quantum bits with computational complexity [4]. - Quantum convolution operations utilize the properties of quantum bits to extract features and recognize patterns from input data through a series of quantum gate operations [4]. - Quantum pooling operations reduce data dimensions while retaining important features, employing a measurement-based pooling method to select the most probable quantum states [5]. Group 4: Classification and Output - After quantum convolution and pooling, a quantum fully connected layer processes the low-dimensional quantum state for DDoS attack classification and detection [6]. - The output from the quantum fully connected layer is a quantum state representing classification results, which is converted into a readable format using specialized quantum state reading techniques [6]. - If the probability distribution indicates a high likelihood of a DDoS attack, alerts are generated to notify network administrators for appropriate defensive actions [6]. Group 5: Applications and Future Prospects - The QCNN developed by Micro Algorithm Technology can monitor blockchain networks in real-time, promptly detecting signs of DDoS attacks and issuing alerts for immediate defensive measures [7]. - This technology can be integrated with other security measures, such as encryption and access control, to create a more secure blockchain environment [7]. - As quantum computing technology advances, the application prospects for QCNN in detecting DDoS attacks will expand, potentially enhancing computational power and accuracy [7].

Core Insights - The development of blockchain technology is rapidly evolving, with consensus mechanisms being crucial for the normal operation of blockchain networks [1] - Micro Algorithm Technology (NASDAQ: MLGO) innovatively integrates LINK and a reputation evaluation mechanism into the DPoS mechanism to enhance blockchain processing efficiency and security [1][3] Group 1: DPoS Mechanism Overview - DPoS mechanism selects a certain number of super nodes through elections to generate and verify blocks, allowing token holders to delegate their voting rights to agents (super nodes) [1] - The registration and voting process involves token holders registering as candidate nodes and promoting their capabilities to attract votes, leading to the election of super nodes responsible for block generation and transaction verification [2] Group 2: LINK and Reputation Mechanism - LINK is introduced as a value transfer medium, with its distribution dynamically adjusted based on the performance of super nodes, incentivizing active participation in network governance [2] - The reputation evaluation mechanism supervises super nodes' behavior, ensuring compliance with network requirements, and dynamically adjusts rewards based on performance metrics such as block generation speed and transaction verification accuracy [3] Group 3: Applications and Future Prospects - The technology can be applied in various blockchain scenarios, including digital currency trading platforms, supply chain management, and the Internet of Things (IoT), enhancing security and efficiency [4] - The potential for further refinement and promotion of this technology exists as blockchain technology continues to develop, with more enterprises likely to adopt and optimize the DPoS mechanism [4]

Core Viewpoint - MicroAlgo Inc. has integrated the quantum image LSQb algorithm with quantum encryption technology to create a more secure and efficient data protection mechanism for information hiding and transmission [1][9]. Group 1: Technological Innovation - The LSQb algorithm allows for secure information hiding by embedding secret data into the least significant quantum bits of a quantum image, enhancing both security and efficiency in quantum networks [2][9]. - The integration of the LSQb algorithm with quantum encryption technologies, such as Quantum Key Distribution (QKD), ensures data security during transmission and reduces algorithm complexity by optimizing quantum gate operations [3][9]. Group 2: Process Overview - The original image undergoes preprocessing through compressed sensing and sparse representation to extract key features, which are then converted into quantum bit form [4]. - An improved LSQb algorithm is used for embedding selected key quantum bits into quantum states, enhancing system robustness through quantum error correction codes [5]. - QKD technology generates a shared key for secure data transmission, ensuring that any eavesdropping attempts are detectable [6]. - Encrypted quantum state information is transmitted through a quantum channel, maintaining security even in the presence of potential eavesdroppers [7]. - The receiver decrypts the quantum state information using the shared key and applies inverse operations to restore the original image, ensuring high-fidelity recovery through error correction mechanisms [8]. Group 3: Practical Applications - MicroAlgo's information hiding and transmission system has been applied in various fields, including medical image encryption and financial transaction systems, enhancing information security and processing efficiency [10][11]. - The system is expected to expand into emerging fields such as artificial intelligence and big data analysis, leveraging quantum computing advantages for faster processing and valuable information extraction [11]. Group 4: Company Overview - MicroAlgo Inc. is dedicated to developing bespoke central processing algorithms, providing solutions that enhance customer satisfaction, reduce costs, and improve energy efficiency [12].

Core Insights - MicroAlgo Inc. is exploring the potential of quantum technology, particularly in training Quantum Neural Networks (QNNs), which could lead to significant advancements in data processing and pattern recognition [1] Quantum Neural Network Training - Quantum Phase Estimation (QPE) is a crucial technique in quantum computing that enhances the training efficiency of neural networks by optimizing network parameters through precise phase estimation [2][10] - The construction of quantum circuits is essential for mapping the neural network's structure, ensuring accurate representation of parameters [3] - Quantum state initialization involves applying quantum gate operations to set qubits in specific states that correspond to the neural network's initial parameters [4] - Controlled unitary operations are utilized to entangle the neural network's parameters with auxiliary qubits, gradually accumulating phase information [5] - The inverse Quantum Fourier Transform is applied to convert quantum states into classical bit values for parameter optimization [6] Parameter Optimization and Error Correction - Parameter optimization involves adjusting the neural network's parameters based on estimated phase information to improve output accuracy through iterative processes [7] - Advanced quantum error correction techniques are implemented to enhance training stability and precision of phase estimation [8] Applications and Future Prospects - The application of QPE in QNN training has shown to significantly improve image processing capabilities, outperforming traditional methods in speed and accuracy [9] - In natural language processing, optimized network parameters allow for better understanding and generation of text, enhancing efficiency and fluency in various tasks [9] - The scalability of this technology supports the ongoing development of quantum computing and the increasing number of qubits, indicating a promising future for larger-scale QNN training [10][11][12] Company Overview - MicroAlgo Inc. specializes in developing bespoke central processing algorithms, providing comprehensive solutions that integrate these algorithms with software and hardware to enhance customer satisfaction and achieve technical goals [13]

Core Viewpoint - MicroAlgo Inc. is focused on enhancing the accuracy and reliability of quantum algorithms by optimizing quantum error correction algorithms, which are essential for detecting and correcting errors in qubits during quantum computation [1][2]. Group 1: Quantum Error Correction Algorithms - The company introduces redundant qubits and specific measurement operations to detect and correct errors in qubits, ensuring the accuracy of quantum computation [2][7]. - The first step in quantum error correction involves encoding quantum information using redundant qubits to form a quantum codeword, which helps retain data despite noise [3]. - Error detection is performed through specific measurement operations on auxiliary qubits, allowing for the identification of errors in the quantum codeword [4]. Group 2: Error Correction Process - Once errors are detected, a series of complex quantum operations are executed to restore erroneous qubits to their correct states, emphasizing the need for efficient error correction algorithms [5]. - The algorithm undergoes iterative optimization, continuously repeating encoding, detection, and correction processes to reduce error rates and improve algorithm accuracy [6]. Group 3: Applications and Prospects - MicroAlgo's quantum error correction algorithm has broad application prospects in quantum communication, enhancing security and anti-interference capabilities, particularly for quantum key distribution [8]. - In quantum computing, the algorithm reduces qubit error rates, improving the reliability of quantum algorithms and supporting their practical applications [8]. - The algorithm can also be applied in quantum simulation and optimization, providing new tools for research and development in these areas [8]. Group 4: Company Overview - MicroAlgo Inc. is dedicated to developing bespoke central processing algorithms, offering solutions that integrate these algorithms with software or hardware to enhance customer satisfaction and achieve technical goals [9][10].

Core Viewpoint - MicroAlgo Inc. is integrating quantum algorithms with machine learning to explore practical applications for quantum acceleration [1] Group 1: Quantum Machine Learning Technology - Quantum machine learning algorithms utilize quantum computing principles to enhance machine learning, offering advantages in feature extraction, model training, and predictive inference [2] - These algorithms excel in processing high-dimensional data, optimizing combinatorial problems, and solving large-scale linear equations, resulting in faster model training and improved prediction accuracy [2][6] - MicroAlgo employs a closed-loop process for developing quantum machine learning technology, which includes problem modeling, quantum circuit design, experimental validation, and optimization iteration [3] Group 2: Technical Aspects - Quantum feature mapping techniques enhance data distinguishability, while quantum circuit optimization employs adaptive variational algorithms to balance computational resources and model expressiveness [4] - The hybrid quantum-classical architecture combines the strengths of both computing paradigms for efficient collaborative training [5] - Noise suppression techniques are introduced to address current quantum hardware limitations, improving computational accuracy [5] Group 3: Applications and Prospects - Quantum machine learning algorithms have broad application prospects in various sectors, including finance for analyzing time-series data, healthcare for personalized treatment plans, and logistics for supply chain optimization [7] - These algorithms can also be applied in cybersecurity, smart manufacturing, and energy management, providing efficient data analysis and optimization solutions [7] - As quantum computing technology advances, it is expected to address challenges that classical computers cannot, leading to disruptive innovations across industries [8] Group 4: Company Overview - MicroAlgo Inc. is focused on developing bespoke central processing algorithms and offers comprehensive solutions by integrating these algorithms with software and hardware [9][10] - The company aims to enhance customer satisfaction, achieve cost savings, and reduce power consumption through its services, which include algorithm optimization and data intelligence [10]

Core Viewpoint - MicroAlgo Inc. has developed a novel quantum entanglement-based training algorithm for supervised quantum classifiers, which significantly enhances training efficiency and classification accuracy compared to traditional algorithms [1][12]. Group 1: Algorithm Development - The new algorithm, named the Entanglement-Assisted Training Algorithm, utilizes quantum entanglement to process multiple training samples simultaneously, improving training efficiency [2][9]. - It represents training samples as qubit vectors and encodes label information into quantum states, allowing for parallel processing of data [3][5]. Group 2: Cost Function and Optimization - A cost function based on Bell inequalities is introduced, enabling the simultaneous encoding of errors from multiple samples, which enhances classification accuracy and overcomes local optimization issues common in traditional methods [4][10]. - This cost function allows the optimization process to focus on the collective performance of multiple samples rather than individual sample errors [8][10]. Group 3: Implementation and Components - The algorithm relies on core components of quantum computing technology, including qubits, quantum gate operations, and quantum measurement, to efficiently process input data [5][6]. - Quantum entanglement is utilized to arrange training samples into an entangled state, improving data processing efficiency and accelerating convergence during training [7][9]. Group 4: Future Implications - The advancements in quantum machine learning, particularly through MicroAlgo's algorithm, are expected to revolutionize the field, potentially allowing quantum classifiers to excel in complex classification tasks beyond traditional binary classifications [12]. - Continuous progress in quantum computing technology is anticipated to address existing challenges, paving the way for broader applications of quantum classifiers [11][12].