Shenzhen, ChinaPresent May 2, 2025 / Prnewswire/ – Microalgo Inc. (“Company” or “Microalgo”) (Nasdaq: Mlgo) announced Today the introduction of your latest classifier auto-optimization technology based on variation quantum algorithms (VQA). This technology significantly reduces the complexity of the parameter updates during training due to profound optimization of the core circle and significantly improves the arithmetic efficiency. Compared to other quantum classifiers, this optimized model has a lower complexity and includes advanced regularization techniques, which effectively prevents the classifier's generalization ability to transfer and improve the generalization ability of the classification. The introduction of this technology marks a considerable step forward when using quantum learning.
Conventional quantum classifiers can theoretically use the advantages of the quantum computer to accelerate tasks of machine learning, but they are still numerous challenges in practical applications. First of all, current mainstream classifiers often require deep quantum circuit to achieve efficient feature assignment, which leads to high optimization complexity for quantum parameters during training. With increasing volume of the training data, the computing load for parameter updates is growing rapidly, which leads to longer training times and influences the practicality of the model.
Microalgo's auto optimization technology has significantly reduced the compensation complexity due to the profound optimization of the core circle. This approach improves two important aspects: circuit design and optimization algorithms. With regard to the circuit design, the technology uses an optimized quantum circuit structure, which reduces the number of quantums and thus reduces the consumption of arithmetic resources. In the optimization algorithm front, this model uses the classifier's auto optimization an innovative strategy for parameter update, which makes parameter adjustments more efficient and the training speed is significantly accelerated.
In the training process of classifiers based on variation quantum algorithms (VQA), parameter optimization is one of the most critical steps. In general, VQA classifiers are based on parameterized quantum circuit (PQC), whereby the update of each parameter requires computer gradients to adapt the circuit structure and minimize the loss function. The deeper the quantum circle, the more complex the parameter space that requires optimization algorithms to carry out more iterations in order to achieve convergence. In addition, uncertainties and noise in quantum measurements can also influence the training process, which makes it difficult for the model to optimize stable.
Conventional optimization methods often use strategies such as stochastic gradient waste (SGD) or VQNG (variation quantum edges -natural gradients) to find optimal parameters. However, these methods are still challenges such as high computing complexity, slow convergence rates and the tendency to be enclosed in local optima. Therefore, reducing the calculation pollution through parameter updates and the improvement of training stability to key factors in improving the performance of VQA classifiers has become.
The auto -optimization technology of microalgo, based on variation quantum algorithms, significantly reduces the compensation for compensation of parameter updates due to the deep optimization of the core circle. It also includes innovative regularization techniques to improve the stability and generalization ability of the training process. The core breaks of this technology include the following aspects:
Deep -optimization of quantum circuits to reduce compensation complexity: In conventional VQA classifier structures, the number of layers in quantum circuit has an impact on the compensation complexity. To reduce the computing costs, Microalgo uses an ACP method (Adaptive Circuit Paning) during the optimization. This approach dynamically adapts the circuit structure and eliminates redundant parameters and at the same time get the expressive performance of the classificator. As a result, the number of parameters required during training is significantly reduced, which leads to a significant decrease in compensation complexity.
Hamilton's transformation optimization (HTO): In addition, Microalgo introduces an optimization method based on Hamilton transformations. By changing the Hamilton representation of the variation quantum circuit, this technology shortens the search path within the parameter space and thus improves the optimization efficiency. Experimental results show that this method can at least reduce the compensation complexity by an order of magnitude and at the same time maintain classification accuracy.
Novel regularization strategy to improve training stability and the generalization ability: In classic mechanical learning, regularization methods are often used to prevent the model from being adapted. In the area of the Quantum machine learning, Microalgo introduces a new quantum regulatory strategy that is described as a quantum of confidence (QER). This method dynamically adapts the strength of quantum dilution during the training, prevent the model from taking over the training data and thus improving the generalization ability of the classifier with invisible data.
In addition, an optimization strategy based on the energy landscape is installed, which adapts the shape of the loss function during training. As a result, the optimization algorithm can identify the global optimum faster and reduce the effects of local optima.
Improved intoxication for real quantum computer environments: In view of the fact that the current devices in the middle medium -sized (NISQ) devices (quantum) still have significant noise levels, the noise resilience of a model is critical. In order to improve the robustness of the classifier, Microalgo suggests a technique based on the variation quantum error correction (VQEC). This method actively learns intoxication patterns during training and adapts the circuit parameters to the reduction of noise effects. This strategy significantly improves the stability of the classifier in loud environments and makes its performance more reliable on real quantum equipment.
The auto -optimization technology of microalgo, based on variation quantum algorithms, successfully reduces the arithmetical complexity of parameter updates through the deep optimization of the core circuit and the introduction of new regularization methods. This approach significantly increases the training speed and the generalization ability. This breakthrough technology not only shows its effectiveness theoretically, but also shows a superior performance in simulation experiments, which is a decisive basis for the further development of quantum machines.
Since the Quantum computing hardware continues to progress, this technology will further expand its application domains in the future and accelerate the practical implementation of quantum-intelligent computer and quantum computing into a new level of the real utility. At a time when quantum computing and artificial intelligence converge, this innovation will undoubtedly serve as a significant milestone for the further development of the limits of technology.
About Microalgo Inc.
Microalgo Inc. (“Microalgo”), a company with Cayman Islands, is dedicated to the development and application of tailor -made central processing algorithms. MicroAlGO offers customers comprehensive solutions through the integration of central processing algorithms with software or hardware or both, which helps you to increase the number of customers, improve the satisfaction of the end users, to achieve direct cost savings, reduce electricity consumption and achieve technical goals. The area of the services of Microalgo includes algorithm optimization, the acceleration of computer performance, without hardware upgrades, easy data processing and data intelligence services required. The ability of Microalgo to efficiently deliver customers via tailor-made central processing algae to software and hardware optimization serves as a driving force for the long-term development of MicroAlgo.
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Source microalgo.inc
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