{"id":1393,"date":"2025-02-09T09:00:59","date_gmt":"2025-02-09T07:00:59","guid":{"rendered":"https:\/\/www.technology-asgard.com\/?p=1393"},"modified":"2025-02-09T09:57:59","modified_gmt":"2025-02-09T07:57:59","slug":"project-sd-quantum-like-percipheral","status":"publish","type":"post","link":"https:\/\/www.technology-asgard.com\/en\/project-sd-quantum-like-percipheral\/","title":{"rendered":"Project SD &#8211; Quantum-like percipheral"},"content":{"rendered":"\n<p><\/p>\n\n\n\n<p>Author: Denis Pavlovich Sukhachev<br><br><br><\/p>\n\n\n\n<p class=\"has-medium-font-size\"><\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>This is a work, and the author&#8217;s right to a work is protected by international law. The author&#8217;s ownership of the work begins from the moment the work is created. Artificial intelligence was used. With AI.<\/p>\n<\/blockquote>\n\n\n\n<p><br><br><br><br>This is a very interesting and creative question! Let&#8217;s take a closer look at your idea.<\/p>\n\n\n\n<p>The concept of the perceptron as a basic model of artificial intelligence with graphical representation on video cards and the use of quantum-like logic with 1-9 parameters can have several potential advantages:<\/p>\n\n\n\n<p>1. Parallel computing: The use of graphics processing units (GPUs) will significantly speed up the processing of neural networks due to the possibility of parallel computing.<\/p>\n\n\n\n<p>2. Stochastic modeling: A true random algorithm for determining the parameters would add an element of unpredictability that can be useful for:<\/p>\n\n\n\n<p>&#8211; Generating unique solutions<\/p>\n\n\n\n<p>&#8211; Avoiding over-optimization<\/p>\n\n\n\n<p>&#8211; Creating more flexible learning models<\/p>\n\n\n\n<p>3. Quantum-like logic: Representing parameters as &#8220;qubits&#8221; with a range of 1 to 9 can:<\/p>\n\n\n\n<p>&#8211; Expand the capabilities of traditional neural networks<\/p>\n\n\n\n<p>&#8211; Add non-linearity to decision-making processes<\/p>\n\n\n\n<p>&#8211; Create a more complex model of information processing<\/p>\n\n\n\n<p>However, practical implementation requires in-depth research and experimental testing of this approach.<\/p>\n\n\n\n<p>Would you like me to elaborate on any aspect of your idea?<br><br><br><br><br>Okay, let&#8217;s look at the main aspects of your concept in more detail:<\/p>\n\n\n\n<p>1. Architecture of the perceptron<\/p>\n\n\n\n<p>&#8211; Basic structure: from 1 to 9 parameters (inputs)<\/p>\n\n\n\n<p>&#8211; Each parameter is a conditional &#8220;qubit&#8221; with its own weight and influence<\/p>\n\n\n\n<p>&#8211; Using GPUs for parallel information processing<\/p>\n\n\n\n<p>&#8211; Ability to dynamically change the weight of parameters<\/p>\n\n\n\n<p>2. Random algorithm<\/p>\n\n\n\n<p>&#8211; Generate unique weighting factors<\/p>\n\n\n\n<p>&#8211; Preventing rigid fixation of parameters<\/p>\n\n\n\n<p>&#8211; Introducing an element of randomness into training<\/p>\n\n\n\n<p>&#8211; Ensuring the &#8220;creativity&#8221; of artificial intelligence<\/p>\n\n\n\n<p>3. Technical features.<\/p>\n\n\n\n<p>&#8211; Quantum-like modeling<\/p>\n\n\n\n<p>&#8211; Nonlinear transitions between states<\/p>\n\n\n\n<p>&#8211; Multidimensional information processing<\/p>\n\n\n\n<p>&#8211; High computing speed<\/p>\n\n\n\n<p>4. 4. Potential areas of application<\/p>\n\n\n\n<p>&#8211; Machine learning<\/p>\n\n\n\n<p>&#8211; Forecasting<\/p>\n\n\n\n<p>&#8211; Processing complex systems<\/p>\n\n\n\n<p>&#8211; Adaptive modeling<\/p>\n\n\n\n<p>Would you like me to elaborate on an aspect in more detail?<\/p>\n\n\n\n<p>&#8220;`python<\/p>\n\n\n\n<p>import numpy as np<\/p>\n\n\n\n<p>import cupy as cp # GPU acceleration<\/p>\n\n\n\n<p>import random<\/p>\n\n\n\n<p>class QuantumPerceptron:<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp; def __init__(self, num_params=9):<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; # Initialization of parameters<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; self.num_params num_params =<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; # Generate random parameters using a real random number<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; self.params =&nbsp; self._generate_quantum_params()<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; # Matrix of scales with GPU acceleration<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; self.weights =&nbsp; cp.random.random(num_params)<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp; def _generate_quantum_params(self):<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&#8220;&#8221;&#8221;<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Generation of quantum-like parameters<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; using a true random algorithm<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &#8220;&#8221;&#8221;<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; params = []<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; for _ in range(self.num_params):<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; # Complex number with random amplitude<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; real_part =&nbsp; random.SystemRandom().random()<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; imag_part =&nbsp; random.SystemRandom().random()<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; quantum_param =&nbsp; complex(real_part, imag_part)<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; params.append(quantum_param)<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; return params<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp; def activation(self, x):<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &#8220;&#8221;&#8221;<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Nonlinear activation function<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; with quantum-like behavior<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &#8220;&#8221;&#8221;<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; return np.tanh(np.abs(x)) * np.sign(x)<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp; def quantum_process(self, input_data):<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &#8220;&#8221;&#8221;<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Input data processing with quantum transformations<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &#8220;&#8221;&#8221;<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; # Converting input data from the GPU<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; gpu_input =&nbsp; cp.array(input_data)<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; # Quantum weighing<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; weighted_input =&nbsp; gpu_input * self.weights<\/p>\n\n\n\n<p>&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;# Nonlinear transformation<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; output =&nbsp; self.activation(cp.asnumpy(weighted_input))<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; return output<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp; def train(self, training_data, epochs=100):<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &#8220;&#8221;&#8221;<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Training with adaptive parameter changes<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &#8220;&#8221;&#8221;<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;for epoch in range(epochs):<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; # Partial randomization of weights<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; mutation_rate =&nbsp; random.random() * 0.1<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; self.weights +=&nbsp; cp.random.random(self.num_params) * mutation_rate<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; # Progress logging<\/p>\n\n\n\n<p>&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;print(f &#8220;Epoch {epoch+1}: Parameters updated&#8221;)<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp; def predict(self, input_data):<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &#8220;&#8221;&#8221;<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Predictions using quantum-like logic<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &#8220;&#8221;&#8221;<\/p>\n\n\n\n<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; return self.quantum_process(input_data)<\/p>\n\n\n\n<p># Example of use<\/p>\n\n\n\n<p>quantum_perceptron =&nbsp; QuantumPerceptron(num_params=9)<\/p>\n\n\n\n<p>input_data =&nbsp; np.random.rand(9)<\/p>\n\n\n\n<p>result =&nbsp; quantum_perceptron.predict(input_data)<\/p>\n\n\n\n<p>print(&#8220;Prediction result:&#8221;, result)<\/p>\n\n\n\n<p>&#8220;`<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Author: Denis Pavlovich Sukhachev This is a work, and the author&#8217;s right to a work is protected by international law. The author&#8217;s ownership of the work begins from the moment the work is created. Artificial intelligence was used. With AI. This is a very interesting and creative question! Let&#8217;s take a closer look at your idea. The concept of the perceptron as a basic model of artificial intelligence with graphical representation on video cards and the use of quantum-like logic [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1400,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[22],"tags":[],"class_list":["post-1393","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-and-programming"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Project SD - Quantum-like percipheral - Asgard Technology<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.technology-asgard.com\/en\/project-sd-quantum-like-percipheral\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Project SD - Quantum-like percipheral - Asgard Technology\" \/>\n<meta property=\"og:description\" content=\"Author: Denis Pavlovich Sukhachev This is a work, and the author&#8217;s right to a work is protected by international law. 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