{"id":1413,"date":"2024-09-12T07:22:00","date_gmt":"2024-09-11T23:22:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=1413"},"modified":"2024-09-04T21:56:28","modified_gmt":"2024-09-04T13:56:28","slug":"%e7%94%a8-python-%e4%b8%ad%e7%9a%84%e9%87%8f%e5%ad%90%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e9%a2%84%e6%b5%8b%e8%82%a1%e7%a5%a8%e4%bb%b7%e6%a0%bc","status":"publish","type":"post","link":"https:\/\/blog.laoyulaoyu.top\/index.php\/2024\/09\/12\/%e7%94%a8-python-%e4%b8%ad%e7%9a%84%e9%87%8f%e5%ad%90%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e9%a2%84%e6%b5%8b%e8%82%a1%e7%a5%a8%e4%bb%b7%e6%a0%bc\/","title":{"rendered":"\u7528 Python \u4e2d\u7684\u91cf\u5b50\u673a\u5668\u5b66\u4e60\u9884\u6d4b\u80a1\u7968\u4ef7\u683c"},"content":{"rendered":"\n

\u4f5c\u8005\uff1a\u8001\u4f59\u635e\u9c7c<\/a><\/p>\n\n\n\n

\u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005\u3002<\/mark><\/strong><\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n
\n
\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u4eca\u5929\uff0c\u6211\u4eec\u5c06\u6df1\u5165\u91cf\u5b50\u8ba1\u7b97\u4e0e\u673a\u5668\u5b66\u4e60\u7684\u4ea4\u53c9\u9886\u57df\uff0c\u63a2\u7d22\u91cf\u5b50\u673a\u5668\u5b66\u4e60\u3002\u6211\u4eec\u7684\u4e3b\u8981\u76ee\u6807\u662f\u6bd4\u8f83\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u4e0e\u7b80\u5355\u7684\u5355\u5c42 MLP \u5728\u9884\u6d4b\u80a1\u7968\u4ef7\u683c\u65f6\u95f4\u5e8f\u5217\u65b9\u9762\u7684\u6027\u80fd<\/mark>\u3002<\/pre>\n<\/blockquote>\n\n\n\n
\u4e3a\u4e86\u4fc3\u8fdb\u8fd9\u4e2a\u9879\u76ee\uff0c\u6211\u4eec\u5c06\u5229\u7528 Financial Modeling Prep (FMP) \u63d0\u4f9b\u7684 Historical API \u7aef\u70b9\u6765\u83b7\u53d6\u53ef\u9760\u3001\u51c6\u786e\u7684\u6570\u636e\uff0c\u8fd9\u4e00\u70b9\u975e\u5e38\u91cd\u8981\u3002<\/p>\n\n\n\n
\u4e00\u3001\u5bfc\u5165\u6570\u636e<\/strong><\/h2>\n\n\n\n
\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u5bfc\u5165\u5206\u6790\u6240\u9700\u7684\u5e93\u3002\u8fd9\u4e9b\u5e93\u5c06\u4e3a\u6211\u4eec\u63d0\u4f9b\u63a2\u7d22\u548c\u5b9e\u65bd\u9879\u76ee\u6240\u9700\u7684\u57fa\u672c\u5de5\u5177\u3002<\/p>\n\n\n\n
import numpy as np\nimport pandas as pd\nimport requests\nimport json\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom sklearn.metrics import mean_squared_error\nfrom qiskit import QuantumCircuit\nfrom qiskit.circuit.library import PauliFeatureMap\nfrom qiskit.algorithms.optimizers import ADAM\nfrom qiskit.circuit import Parameter\nfrom qiskit.primitives import Sampler<\/code><\/pre>\n\n\n\n
\u6211\u4eec\u5df2\u7ecf\u5b89\u88c5\u4e86\u7528\u4e8e\u91cf\u5b50\u8ba1\u7b97\u7f51\u7edc\u7684 Qiskit \u5e93\u548c\u5176\u4ed6\u91cd\u8981\u5e93\uff0c\u4ece\u800c\u5efa\u7acb\u4e86\u6211\u4eec\u7684\u73af\u5883\u3002\u4e3a\u4e86\u63d0\u53d6\u6570\u636e\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 Financial Modeling Prep<\/a> \u63d0\u4f9b\u7684\u5386\u53f2\u6570\u636e API <\/a>\u3002<\/p>\n\n\n\n
FMP \u7684\u5386\u53f2\u6570\u636e\u5e94\u7528\u7a0b\u5e8f\u63a5\u53e3\uff08API\uff09\u63d0\u4f9b\u4e86\u4e00\u4e2a\u65b9\u4fbf\u8bbf\u95ee\u7684\u7aef\u70b9\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u591a\u6837\u7684\u5386\u53f2\u80a1\u7968\u6570\u636e\uff0c\u8fd9\u5728\u6211\u4eec\u9879\u76ee\u7684\u6bcf\u4e00\u4e2a\u6b65\u9aa4\u4e2d\u90fd\u8bc1\u660e\u662f\u975e\u5e38\u5b9d\u8d35\u7684\u3002\u8fd9\u4e00\u8d44\u6e90\u4f7f\u6211\u4eec\u80fd\u591f\u8bbf\u95ee\u5e7f\u6cdb\u7684\u91d1\u878d\u4fe1\u606f\uff0c\u4ece\u800c\u63d0\u9ad8\u6211\u4eec\u5206\u6790\u7684\u6df1\u5ea6\u548c\u51c6\u786e\u6027\u3002\u5176\u53cb\u597d\u7684\u7528\u6237\u754c\u9762\u548c\u5168\u9762\u7684\u6570\u636e\u96c6\u5927\u5927\u63d0\u9ad8\u4e86\u6211\u4eec\u7814\u7a76\u548c\u5b9e\u65bd\u7684\u6210\u529f\u7387\u548c\u6548\u7387\u3002\u73b0\u5728\uff0c\u6211\u4eec\u5c06\u63d0\u53d6\u5386\u53f2\u6570\u636e\u5982\u4e0b\uff1a<\/p>\n\n\n\n
api_url = \"https:\/\/financialmodelingprep.com\/api\/v3\/historical-price-full\/AAPL?apikey=YOUR API KEY\"\n\n# Make a GET request to the API\nresponse = requests.get(api_url)\n\n# Check if the request was successful (status code 200)\nif response.status_code == 200:\n    # Parse the response JSON\n    data = response.json()\nelse:\n    print(f\"Error: Unable to fetch data. Status code: {response.status_code}\")\n\ndata<\/code><\/pre>\n\n\n\n
\u5c06\u60a8\u7684 API \u5bc6\u94a5\u66ff\u6362\u4e3a\u60a8\u7684\u79d8\u5bc6 API \u5bc6\u94a5\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa FMP \u5e10\u6237<\/a>\u83b7\u5f97\u8be5\u5bc6\u94a5\u3002\u8f93\u51fa\u662f\u4e00\u4e2a JSON \u54cd\u5e94\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n

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\"\"\/<\/figure>\n\n\n\n
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\"\"\/<\/figure>\n\n\n\n
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\"\"\/<\/figure>\n\n\n\n
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\u5728\u6211\u4eec\u7684\u9879\u76ee\u4e2d\uff0c\u6211\u4eec\u7279\u522b\u5f3a\u8c03\u4e00\u7c7b\u88ab\u79f0\u4e3a\u53c2\u6570\u5316\u95e8\u7684\u95e8\u3002\u8fd9\u4e9b\u95e8\u7684\u884c\u4e3a\u53d6\u51b3\u4e8e\u7279\u5b9a\u7684\u8f93\u5165\u53c2\u6570\uff0c\u7528\u7b26\u53f7 \u03b8 \u8868\u793a\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5982\u4e0b\u56fe\u6240\u793a\uff0c\u6bcf\u4e2a\u77e9\u9635\u90fd\u6709\u4e00\u4e2a\u5355\u5143\u77e9\u9635\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u8ba9\u6211\u4eec\u6df1\u5165\u7814\u7a76\u4e00\u4e0b\u8fd9\u4e9b\u65cb\u8f6c\u95e8\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u8bbe\u60f3\u5b83\u662f\u4e00\u4e2a\u7c7b\u4f3c\u65cb\u8f6c\u95e8\u7684\u91cf\u5b50\u95e8\uff0c\u53ef\u4ee5\u5c06\u91cf\u5b50\u6bd4\u7279\u65cb\u8f6c\u4e00\u4e2a\u7279\u5b9a\u7684\u89d2\u5ea6 \u03b8\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u548c<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u95e8\u7684\u529f\u80fd\u7c7b\u4f3c\uff0c\u90fd\u662f\u56f4\u7ed5\u4e0d\u540c\u7684\u8f74\u8fdb\u884c\u65cb\u8f6c\u3002<\/p>\n\n\n\n
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\u4ece\u672c\u8d28\u4e0a\u8bb2\uff0c\u03b8 \u5c31\u50cf\u4e00\u4e2a\u7c7b\u4f3c\u65cb\u94ae\u7684\u8c03\u8282\u53c2\u6570\uff0c\u4f7f\u6211\u4eec\u80fd\u591f\u5728\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u6846\u67b6\u5185\u5bf9\u91cf\u5b50\u7b97\u6cd5\u7684\u884c\u4e3a\u8fdb\u884c\u5fae\u8c03\u548c\u5b9a\u5236\u3002\u5728\u9488\u5bf9\u7279\u5b9a\u4efb\u52a1\u4f18\u5316\u548c\u5b9a\u5236\u91cf\u5b50\u7b97\u6cd5\u6027\u80fd\u65f6\uff0c\u8fd9\u79cd\u7075\u6d3b\u6027\u53d8\u5f97\u81f3\u5173\u91cd\u8981\u3002<\/p>\n\n\n\n
2.2 \u91cf\u5b50\u7535\u8def<\/strong><\/h3>\n\n\n\n
\u91cf\u5b50\u7b97\u6cd5\u53ef\u4ee5\u770b\u4f5c\u662f\u5bf9\u91cf\u5b50\u6001\u6267\u884c\u7684\u4e00\u7cfb\u5217\u64cd\u4f5c\uff0c\u7528\u7c7b\u4f3c\u7684\u8868\u8fbe\u5f0f\u6765\u8868\u793a\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5982\u4e0b\u56fe\u6240\u793a\uff0c\u8fd9\u4e9b\u7b97\u6cd5\u88ab\u8f6c\u5316\u4e3a\u91cf\u5b50\u7535\u8def\u3002\u5728\u8fd9\u5e45\u56fe\u4e2d\uff0c\u7b97\u6cd5\u4ece\u521d\u59cb\u72b6\u6001 |q_0 q_1\u27e9 = |00\u27e9 \u5f00\u59cb\uff0c\u4ee5\u6d4b\u91cf\u7ed3\u679c|00\u27e9 \u6216 |11\u27e9\uff08\u6982\u7387\u76f8\u7b49\uff0c\u5747\u4e3a 0.5\uff09\u7ed3\u675f\uff0c\u5e76\u8bb0\u5f55\u4e3a\u7ecf\u5178\u6bd4\u7279\uff08c \u7ebf\uff09\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
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2.3 \u91cf\u5b50\u673a\u5668\u5b66\u4e60<\/strong><\/h3>\n\n\n\n
QML \u7684\u4e3b\u8981\u76ee\u6807\u662f\u8bbe\u8ba1\u548c\u90e8\u7f72\u80fd\u591f\u5728\u91cf\u5b50\u8ba1\u7b97\u673a\u4e0a\u8fd0\u884c\u7684\u65b9\u6cd5\uff0c\u4ee5\u89e3\u51b3\u7ecf\u5178\u673a\u5668\u5b66\u4e60\u4e2d\u9047\u5230\u7684\u4f20\u7edf\u76d1\u7763\u3001\u65e0\u76d1\u7763\u548c\u5f3a\u5316\u5b66\u4e60\u4efb\u52a1\u3002<\/p>\n\n\n\n
QML \u7684\u4e0e\u4f17\u4e0d\u540c\u4e4b\u5904\u5728\u4e8e\u5b83\u5229\u7528\u91cf\u5b50\u8ba1\u7b97\uff08QC\uff09\u56fa\u6709\u7684\u53e0\u52a0\u3001\u96a7\u9053\u3001\u7ea0\u7f20\u548c\u91cf\u5b50\u5e76\u884c\u7b49\u72ec\u7279\u529f\u80fd\uff0c\u8fdb\u884c\u91cf\u5b50\u64cd\u4f5c\u3002\u5728\u6211\u4eec\u7684\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u7279\u522b\u5173\u6ce8\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\uff08QNN\uff09\u7684\u8bbe\u8ba1\u3002\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u662f\u7ecf\u5178\u795e\u7ecf\u7f51\u7edc\u7684\u91cf\u5b50\u5bf9\u5e94\u7269\u3002<\/p>\n\n\n\n
\u7ec6\u5206\u8d77\u6765\uff0cQNN \u7684\u6bcf\u4e00\u5c42\u90fd\u662f\u4e00\u4e2a\u53d8\u91cf\u91cf\u5b50\u7535\u8def (VQC)\uff0c\u7531\u53c2\u6570\u5316\u7684\u95e8\u7ec4\u6210\u3002\u8fd9\u4e9b\u53c2\u6570\u76f8\u5f53\u4e8e\u7ecf\u5178\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u6743\u91cd\u3002\u6b64\u5916\uff0c\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u8fd8\u5305\u542b\u4e00\u79cd\u5728\u73b0\u6709\u91cf\u5b50\u4f4d\u4e4b\u95f4\u4ea4\u6362\u4fe1\u606f\u7684\u673a\u5236\uff0c\u7c7b\u4f3c\u4e8e\u7ecf\u5178\u7f51\u7edc\u4e2d\u4e0d\u540c\u5c42\u795e\u7ecf\u5143\u4e4b\u95f4\u7684\u8fde\u63a5\u3002\u901a\u5e38\uff0c\u8fd9\u79cd\u4fe1\u606f\u4ea4\u6362\u662f\u901a\u8fc7\u7ea0\u7f20\u5b9e\u73b0\u7684\uff0c\u91c7\u7528\u7684\u7b97\u5b50\u5305\u62ec CNOT \u95e8\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u521b\u5efa\u91cf\u5b50\u673a\u5668\u5b66\u4e60\uff08QML\uff09\u6a21\u578b\u901a\u5e38\u9700\u8981\u51e0\u4e2a\u6b65\u9aa4\uff0c\u5982\u4e0a\u56fe\u6240\u793a\u3002\u9996\u5148\uff0c\u6211\u4eec\u5728\u7ecf\u5178 CPU \u4e0a\u52a0\u8f7d\u5e76\u9884\u5904\u7406\u6570\u636e\u96c6\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f7f\u7528\u91cf\u5b50\u5d4c\u5165\u6280\u672f\uff0c\u5728\u91cf\u5b50\u5904\u7406\u5355\u5143\uff08QPU\uff09\u6216\u91cf\u5b50\u786c\u4ef6\u4e0a\u5c06\u7ecf\u5178\u6570\u636e\u7f16\u7801\u4e3a\u91cf\u5b50\u6001\u3002\u4e00\u65e6\u7ecf\u5178\u6570\u636e\u4ee5\u91cf\u5b50\u6001\u8868\u793a\uff0c\u5c31\u4f1a\u6267\u884c\u5728\u89e3\u6790\u4e2d\u5b9e\u73b0\u7684\u6838\u5fc3\u6a21\u578b\uff0c\u5e76\u4f7f\u7528\u7ecf\u5178\u6bd4\u7279\u6d4b\u91cf\u7ed3\u679c\u3002\u6700\u540e\uff0c\u5982\u679c\u9700\u8981\uff0c\u6211\u4eec\u4f1a\u5728 CPU \u4e0a\u5bf9\u8fd9\u4e9b\u7ed3\u679c\u8fdb\u884c\u540e\u5904\u7406\uff0c\u4ee5\u83b7\u5f97\u9884\u671f\u7684\u6a21\u578b\u8f93\u51fa\u3002\u5728\u6211\u4eec\u7684\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u6309\u7167\u8fd9\u4e2a\u6574\u4f53\u6d41\u7a0b\u6765\u7814\u7a76\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u5728\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u4e2d\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n
2.4 \u91cf\u5b50\u795e\u7ecf\u7f51\u7edc<\/strong><\/h3>\n\n\n\n
\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc (QNN) \u901a\u5e38\u7531\u4e09\u4e2a\u4e3b\u8981\u5c42\u7ec4\u6210\uff1a<\/p>\n\n\n\n
1.\u8f93\u5165\u5c42\uff1a<\/strong>\u8be5\u5c42\u5c06\u7ecf\u5178\u8f93\u5165\u6570\u636e\u8f6c\u6362\u4e3a\u91cf\u5b50\u6001\u3002\u5b83\u4f7f\u7528\u5e26\u6709\u65cb\u8f6c\u548c\u53d7\u63a7\u65cb\u8f6c\u95e8\u7684\u53c2\u6570\u5316\u53d8\u5206\u7535\u8def\uff0c\u4e3a\u7ed9\u5b9a\u8f93\u5165\u51c6\u5907\u6240\u9700\u7684\u91cf\u5b50\u6001\u3002\u8fd9\u4e00\u6b65\u88ab\u79f0\u4e3a\u91cf\u5b50\u5d4c\u5165\uff0c\u91c7\u7528\u4e86\u57fa\u7f16\u7801\u3001\u632f\u5e45\u7f16\u7801\u3001\u54c8\u5bc6\u987f\u7f16\u7801\u6216\u5f20\u91cf\u4e58\u79ef\u7f16\u7801\u7b49\u6280\u672f\u3002<\/p>\n\n\n\n
2.\u89e3\u6790\u5c42\uff1a<\/strong>\u8be5\u5c42\u662f QNN \u7684\u6838\u5fc3\uff0c\u5305\u542b\u4e00\u4e2a\u53d8\u91cf\u91cf\u5b50\u7535\u8def\uff0c\u91cd\u590d L \u6b21\u5373\u53ef\u7ecf\u5178\u5730\u6a21\u62df L \u4e2a\u7f51\u7edc\u5c42\u3002\u5b83\u8d1f\u8d23\u5904\u7406\u548c\u64cd\u4f5c\u91cf\u5b50\u4fe1\u606f\u3002<\/p>\n\n\n\n
3.\u8f93\u51fa\u5c42\uff1a<\/strong>\u8fd9\u4e00\u5c42\u5bf9\u6bd4\u7279\u8fdb\u884c\u6d4b\u91cf\uff0c\u63d0\u4f9b\u6700\u7ec8\u7684\u9884\u671f\u7ed3\u679c\u3002<\/p>\n\n\n\n
\u5bf9\u4e8e\u8f93\u5165\u5c42\uff0c\u6211\u4eec\u91c7\u7528\u5f20\u91cf\u4e58\u79ef\u7f16\u7801\u6280\u672f\u3002\u8fd9\u6d89\u53ca\u4e3a\u6bcf\u4e2a\u91cf\u5b50\u6bd4\u7279\u8bbe\u7f6e\u4e00\u4e2a\u7b80\u5355\u7684 X \u65cb\u8f6c\u95e8\uff0c\u95e8\u53c2\u6570\u901a\u8fc7\u5c06\u7ecf\u5178\u6570\u636e\u7f29\u653e\u81f3 [-\u03c0, \u03c0] \u8303\u56f4\u6765\u8bbe\u7f6e\u3002\u867d\u7136\u8fd9\u662f\u4e00\u79cd\u5feb\u901f\u3001\u76f4\u63a5\u7684\u7f16\u7801\u65b9\u6cd5\uff08O(1) \u8fd0\u7b97\uff09\uff0c\u4f46\u5b83\u4e5f\u6709\u5c40\u9650\u6027\u3002\u6240\u9700\u7684\u91cf\u5b50\u6bd4\u7279\u6570\u4e0e\u8f93\u5165\u7684\u7ecf\u5178\u6570\u636e\u6210\u7ebf\u6027\u6bd4\u4f8b\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u6211\u4eec\u5728\u8f93\u5165\u6570\u636e\u4e2d\u5f15\u5165\u4e86\u53ef\u5b66\u4e60\u7684\u7f29\u653e\u548c\u504f\u5dee\u53c2\u6570\uff0c\u589e\u5f3a\u4e86\u91cf\u5b50\u5d4c\u5165\u7684\u7075\u6d3b\u6027\u3002\u5728\u56fe 3 \u4e2d\uff0c\u60a8\u53ef\u4ee5\u770b\u5230\u4e00\u4e2a\u6709 3 \u4e2a\u91cf\u5b50\u6bd4\u7279\u7684\u7f51\u7edc\u7684\u8f93\u5165\u5c42\u793a\u4f8b\uff0c\u5176\u4e2d\u7ecf\u5178\u6570\u636e\u5177\u6709\u4ee5\u4e0b\u7279\u5f81\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u8f93\u5165\u6bd4\u4f8b\u53c2\u6570\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u548c\u504f\u7f6e\u53c2\u6570\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5c31\u4f1a\u53d1\u6325\u4f5c\u7528\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u5173\u4e8e\u516c\u5f0f\uff0c\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u4e0e\u7ecf\u5178\u795e\u7ecf\u7f51\u7edc\u4e0d\u540c\uff0c\u6587\u732e\u4e2d\u5e38\u89c1\u7684\u91cf\u5b50\u5c42\u7ed3\u6784\uff08\u5982\u5168\u8fde\u63a5\u5c42\u6216\u9012\u5f52\u5c42\uff09\u5e76\u4e0d\u662f\u56fa\u5b9a\u4e0d\u53d8\u7684\u3002\u91cf\u5b50\u6bd4\u7279\u4e4b\u95f4\u53ef\u80fd\u5b58\u5728\u7684\u91cf\u5b50\u4fe1\u606f\u4f20\u8f93\u95e8\u662f\u975e\u5e38\u5e7f\u6cdb\u7684\uff0c\u800c\u5982\u4f55\u4f18\u5316\u8fd9\u4e9b\u95e8\u7684\u7ec4\u7ec7\u4ee5\u5b9e\u73b0\u6709\u6548\u7684\u6570\u636e\u4f20\u8f93\u662f\u4e00\u4e2a\u5c1a\u672a\u88ab\u6df1\u5165\u63a2\u7d22\u7684\u9886\u57df\u3002<\/p>\n\n\n\n
\u5728\u6211\u4eec\u7684\u9879\u76ee\u4e2d\uff0c\u6211\u4eec\u91c7\u7528\u4e86 “\u771f\u5b9e\u632f\u5e45”\uff08Real Amplitudes ansatz\uff09\uff0c\u8fd9\u4e00\u9009\u62e9\u7684\u7075\u611f\u6765\u81ea\u4e8e\u5b83\u5728\u91cf\u5b50\u5f3a\u5316\u5b66\u4e60\u548c\u5206\u7c7b\u7684\u7b56\u7565\u4f30\u8ba1\u7b49\u591a\u4e2a\u9886\u57df\u7684\u6210\u529f\u5e94\u7528\u3002\u8be5\u65b9\u6848\u9996\u5148\u91c7\u7528\u5168\u65cb\u8f6c X\/Y\/Z \u53c2\u6570\u5316\u95e8\uff0c\u7c7b\u4f3c\u4e8e\u91cf\u5b50\u7248\u7684\u8fde\u63a5\u6743\u91cd\u3002\u7136\u540e\u662f\u4e00\u7cfb\u5217\u4ee5\u73af\u5f62\u7ed3\u6784\u6392\u5217\u7684 CNOT \u95e8\uff0c\u4ee5\u4fc3\u8fdb\u91cf\u5b50\u6bd4\u7279\u4fe1\u606f\u7684\u4f20\u8f93\u3002\u56fe 4 \u76f4\u89c2\u5730\u5c55\u793a\u4e86\u8fd9\u4e00\u6784\u60f3\u662f\u5982\u4f55\u5b9e\u73b0\u7684\uff0c\u76f8\u5f53\u4e8e 3 \u91cf\u5b50\u6bd4\u7279\u7f51\u7edc\u7684\u91cf\u5b50\u7f51\u7edc\u5c42\u3002<\/p>\n\n\n\n
\u7ec6\u5206\u800c\u8a00\uff0c\u6211\u4eec\u5de5\u4f5c\u4e2d\u7684\u91cf\u5b50\u7f51\u7edc\u5c42\u6d89\u53ca\u4e00\u7ec4\u53c2\u6570\uff0c\u53c2\u6570\u603b\u6570\u662f\u91cf\u5b50\u6bd4\u7279\u6570\u7684 3 \u500d\uff083*n\uff09\uff0c\u5176\u4e2d “n “\u4ee3\u8868\u91cf\u5b50\u7f51\u7edc\u4e2d\u7684\u91cf\u5b50\u6bd4\u7279\u6570\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u73b0\u5728\uff0c\u8ba9\u6211\u4eec\u6765\u8c08\u8c08\u8f93\u51fa\u5c42\uff0c\u5b83\u662f\u6211\u4eec\u91cf\u5b50\u6a21\u578b\u7684\u5173\u952e\u90e8\u5206\u3002\u5728\u91cf\u5b50\u8ba1\u7b97\u4e2d\uff0c\u5f53\u6211\u4eec\u60f3\u4ece\u91cf\u5b50\u6001\u4e2d\u63d0\u53d6\u4fe1\u606f\u65f6\uff0c\u901a\u5e38\u4f1a\u4f7f\u7528\u9009\u5b9a\u7684\u53ef\u89c2\u6d4b\u91cf\u8fdb\u884c\u6d4b\u91cf\u3002\u8ba1\u7b97\u57fa\u7840\u4e0a\u7684\u03c3_z\u7b97\u5b50\u5c31\u662f\u8fd9\u6837\u4e00\u79cd\u5e38\u7528\u7684\u89c2\u6d4b\u6307\u6807\u3002\u8981\u7406\u89e3\u8fd9\u4e00\u70b9\uff0c\u53ef\u4ee5\u5c06\u5176\u89c6\u4e3a\u4ece\u91cf\u5b50\u6001\u4e2d\u63d0\u53d6\u4fe1\u606f\u7684\u4e00\u79cd\u65b9\u6cd5\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u7f51\u7edc\u8f93\u51fa\u662f\u901a\u8fc7\u8ba1\u7b97\u91cf\u5b50\u6001\u7684\u671f\u671b\u503c\u6765\u786e\u5b9a\u7684\u3002\u8fd9\u8868\u793a\u4e3a\u27e8\u03c8|\u03c3_z|\u03c8\u27e9\uff0c\u5176\u4e2d\u27e8\u03c8|\u8868\u793a|\u03c8\u27e9\u7684\u590d\u5171\u8f6d\u3002\u7ed3\u679c\u5728 [-1, 1] \u8303\u56f4\u5185\u3002<\/p>\n\n\n\n
\u4e0d\u7528\u518d\u4e3a\u90a3\u4e9b\u590d\u6742\u7684\u6570\u5b66\u516c\u5f0f\u800c\u7d27\u5f20–\u6211\u4eec\u503c\u5f97\u4fe1\u8d56\u7684\u5e93 Qiskit \u5df2\u7ecf\u5e2e\u6211\u4eec\u89e3\u51b3\u4e86\u8fd9\u4e2a\u95ee\u9898\uff01Qiskit \u53ef\u4ee5\u65e0\u7f1d\u5904\u7406\u6240\u6709\u590d\u6742\u7684\u91cf\u5b50\u8ba1\u7b97\uff0c\u8ba9\u6211\u4eec\u66f4\u5bb9\u6613\u7406\u89e3\u91cf\u5b50\u8ba1\u7b97\u8fc7\u7a0b\u3002\u56e0\u6b64\uff0c\u4f60\u53ef\u4ee5\u4e13\u6ce8\u4e8e\u63a2\u7d22\u91cf\u5b50\u4e16\u754c\uff0c\u800c\u4e0d\u5fc5\u88ab\u7e41\u7410\u7684\u6570\u5b66\u95ee\u9898\u6240\u56f0\u6270<\/p>\n\n\n\n
\u73b0\u5728\uff0c\u4e3a\u4e86\u4f7f\u6211\u4eec\u7684\u7f51\u7edc\u8f93\u51fa\u5bf9\u6570\u636e\u96c6\u56fa\u6709\u7684\u504f\u5dee\u548c\u89c4\u6a21\u4e0d\u90a3\u4e48\u654f\u611f\uff0c\u6211\u4eec\u5f15\u5165\u4e86\u6700\u7ec8\u89c4\u6a21\u53c2\u6570\u548c\u9700\u8981\u5b66\u4e60\u7684\u504f\u5dee\u3002\u8fd9\u4e00\u6b65\u9aa4\u4e3a\u6211\u4eec\u7684\u6a21\u578b\u589e\u52a0\u4e86\u4e00\u5c42\u9002\u5e94\u6027\uff0c\u4f7f\u5176\u80fd\u591f\u6839\u636e\u6570\u636e\u7684\u5177\u4f53\u7279\u5f81\u5bf9\u8f93\u51fa\u8fdb\u884c\u5fae\u8c03\u548c\u8c03\u6574\u3002\u6574\u4e2a\u6a21\u578b\u67b6\u6784\u5982\u4e0b\u56fe\u6240\u793a\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u6211\u4eec\u63d0\u51fa\u7684\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\uff08QNN\uff09\u7684\u8bad\u7ec3\u662f\u5728\u666e\u901a CPU \u4e0a\u4f7f\u7528\u4e9a\u5f53\u4f18\u5316\u5668\u7b49\u7ecf\u5178\u7b97\u6cd5\u8fdb\u884c\u7684\u3002\u4e2d\u592e\u5904\u7406\u5668\u901a\u8fc7\u4f20\u7edf\u7684\u4f20\u64ad\u89c4\u5219\u5904\u7406\u68af\u5ea6\u8ba1\u7b97\uff0c\u800c\u5728\u91cf\u5b50\u5904\u7406\u5355\u5143\uff08QPU\uff09\u4e0a\uff0c\u6211\u4eec\u4f7f\u7528\u53c2\u6570\u79fb\u52a8\u89c4\u5219\u8ba1\u7b97\u68af\u5ea6\u3002\u8fd9\u6709\u70b9\u50cf\u53cc\u7cfb\u7edf\uff0c\u4e2d\u592e\u5904\u7406\u5668\u7ba1\u7406\u4e3b\u8981\u7684\u8bad\u7ec3\uff0c\u800c QPU \u5219\u5728\u7279\u5b9a\u7684\u91cf\u5b50\u8ba1\u7b97\u4e2d\u53d1\u6325\u4f5c\u7528\u3002<\/p>\n\n\n\n
\u56fe 6 \u5c55\u793a\u4e86\u8bad\u7ec3\u8fc7\u7a0b\u6d41\u6c34\u7ebf\uff0c\u5176\u4e2d \u03b8\u00b9 \u4ee3\u8868\u8f93\u5165\u5c42\u7684\u6807\u5ea6\/\u504f\u7f6e\u53c2\u6570\uff0c\u03b8\u00b2 \u5bf9\u5e94\u5305\u542b\u7b49\u5f0f\u7684\u5404\u5c42\u53c2\u6570\uff0c\u03b8\u00b3 \u662f\u7f51\u7edc\u8f93\u51fa\u7684\u6807\u5ea6\/\u504f\u7f6e\u53c2\u6570\u3002\u8fd9\u79cd\u534f\u8c03\u786e\u4fdd\u4e86\u5229\u7528\u7ecf\u5178\u548c\u91cf\u5b50\u8ba1\u7b97\u8d44\u6e90\u7684\u5185\u805a\u8bad\u7ec3\u65b9\u6cd5\u3002<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u7531\u4e8e\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\uff08QNN\uff09\u662f\u4e00\u79cd\u524d\u9988\u6a21\u578b\uff0c\u6211\u4eec\u9996\u5148\u8981\u5b9a\u4e49\u4e00\u4e2a\u65f6\u95f4\u8de8\u5ea6\uff0c\u7528 T \u8868\u793a\u3002\u5728\u8fd9\u91cc\uff0c\u76ee\u6807\u662f\u65f6\u95f4 t \u65f6\u7684\u65f6\u95f4\u5e8f\u5217\u503c\uff0c\u8bb0\u4e3a x(t)\uff0c\u800c\u8f93\u5165\u5219\u5305\u62ec x(t-1)\u3001x(t-2)\u3001……\u3001x(t-T)\u3002\u8fd9\u79cd\u7ed3\u6784\u8c03\u6574\u6709\u52a9\u4e8e\u6a21\u578b\u7406\u89e3\u6570\u636e\u4e2d\u7684\u65f6\u95f4\u5173\u7cfb\uff0c\u4f7f\u5176\u80fd\u591f\u6839\u636e\u8fc7\u53bb\u7684\u503c\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n\n\n\n

\u4e09\u3001\u7528 Qiskit \u521b\u5efa QNN<\/strong><\/h2>\n\n\n\n

3.1 \u63d0\u53d6\u6570\u636e\u548c\u6570\u636e\u9884\u5904\u7406<\/strong><\/h3>\n\n\n\n
\u9996\u5148\uff0c\u6211\u4eec\u4f7f\u7528 Financial Modeling Prep \u63d0\u4f9b\u7684\u5386\u53f2\u6570\u636e API \u7aef\u70b9\u83b7\u53d6\u6570\u636e\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n\n\n\n
api_url = \"https:\/\/financialmodelingprep.com\/api\/v3\/historical-price-full\/AAPL?apikey=YOUR API KEY\"\n\n# Make a GET request to the API\nresponse = requests.get(api_url)\n\n# Check if the request was successful (status code 200)\nif response.status_code == 200:\n    # Parse the response JSON\n    data = response.json()\nelse:\n    print(f\"Error: Unable to fetch data. Status code: {response.status_code}\")\n\ndf = pd.json_normalize(data, 'historical', ['symbol']) #convert into a datframe\ndf.tail()<\/code><\/pre>\n\n\n\n
\u8f93\u51fa\u7ed3\u679c\u662f\u4e00\u4e2a Pandas \u6570\u636e\u6846\u67b6\uff0c\u770b\u8d77\u6765\u50cf\u8fd9\u6837\uff08\u5728\u6b64\u4e4b\u524d\uff0c\u8bf7\u786e\u4fdd\u7528\u60a8\u7684\u79d8\u5bc6 API \u5bc6\u94a5\u66ff\u6362\u60a8\u7684 API \u5bc6\u94a5\uff09\uff1a<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u4ece\u8fd9\u4e9b\u5927\u91cf\u6570\u636e\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u5f00\u76d8\u4ef7\u4f5c\u4e3a\u65f6\u95f4\u53d8\u91cf\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 500 \u4e2a\u6570\u636e\u70b9\uff0c\u6bcf\u4e2a\u6570\u636e\u70b9\u4ee3\u8868\u6bcf\u65e5\u5f00\u76d8\u4ef7\uff0c\u6211\u4eec\u7684\u9884\u6d4b\u7a97\u53e3\u5927\u5c0f\u4e3a 2\u3002<\/p>\n\n\n\n
final_data = df[['open', 'date']][0:500] #forming filtered dataframe\ninput_sequences = []\nlabels = []\n\n#Creating input and output data for time series forecasting\nfor i in range(len(final_data['open'])):\n    if i > 1:\n        labels.append(final_data['open'][i])\n        input_sequences.append(final_data['open'][i-2:i+1].tolist())\n        \n#creating train test split\nx_train = np.array(input_sequences[0:400])\nx_test = np.array(input_sequences[400:])\ny_train = np.array(labels[0:400])\ny_test = np.array(labels[400:])<\/code><\/pre>\n\n\n\n
\u73b0\u5728\uff0c\u8ba9\u6211\u4eec\u7ed8\u5236\u83b7\u5f97\u7684\u6570\u636e\u3002<\/p>\n\n\n\n
import matplotlib.pyplot as plt\nplt.style.use('ggplot')\n\n# Convert the 'date' column to datetime format\ndf['date'] = pd.to_datetime(df['date'])\n\n# Plotting the time series data\nplt.figure(figsize=(10, 6))\nplt.plot(df['date'][0:500], df['open'][0:500], marker='o', linestyle='-')\n\n# Adding labels and title\nplt.xlabel('date')\nplt.ylabel('open')\nplt.title('Time Series Data')\n\n# Display the plot\nplt.grid(True)\nplt.show()<\/code><\/pre>\n\n\n\n
\u4ee5\u4e0b\u662f\u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n

3.2 \u91cf\u5b50\u795e\u7ecf\u7f51\u7edc<\/strong><\/h3>\n\n\n\n
Pauli Feature Map:<\/strong>
<\/p>\n\n\n\n
num_features =  2\nfeature_map = PauliFeatureMap(feature_dimension = num_features, reps = 2)\noptimizer = ADAM(maxiter = 100)<\/code><\/pre>\n\n\n\n
\u5728\u6211\u4eec\u7684\u65b9\u6cd5\u4e2d\uff0c\u6211\u4eec\u5229\u7528\u4fdd\u5229\u7279\u5f81\u56fe\u5c06\u6570\u636e\u7f16\u7801\u6210\u91cf\u5b50\u6bd4\u7279\uff0c\u7279\u522b\u662f\u5229\u7528 2 \u4e2a\u7279\u5f81\u3002\u7f16\u7801\u7535\u8def\u7684\u7ed3\u6784\u5982\u4e0b\uff1a<\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\u6b64\u5916\uff0c\u4e3a\u4e86\u4f18\u5316\u6211\u4eec\u7684\u6a21\u578b\uff0c\u6211\u4eec\u4f7f\u7528\u4e86 ADAM \u4f18\u5316\u5668\u3002\u8fd9\u4e00\u9009\u62e9\u6709\u52a9\u4e8e\u5fae\u8c03\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u7684\u53c2\u6570\uff0c\u4ece\u800c\u63d0\u9ad8\u5176\u6574\u4f53\u6027\u80fd\u3002<\/p>\n\n\n\n
\u91cf\u5b50\u7535\u8def\uff1a<\/strong><\/p>\n\n\n\n
def ans(n, depth):\n    qc = QuantumCircuit(n)\n    for j in range(depth):\n        for i in range(n):\n            param_name = f'theta_{j}_{i}'\n            theta_param = Parameter(param_name)\n            qc.rx(theta_param, i)\n            qc.ry(theta_param, i)\n            qc.rz(theta_param, i)\n    for i in range(n):\n        if i == n-1:\n            qc.cnot(i, 0)\n        else:\n            qc.cnot(i, i+1)\n    return qc<\/code><\/pre>\n\n\n\n
\u8be5\u51fd\u6570\u521d\u59cb\u5316\u91cf\u5b50\u7535\u8def\uff0c\u91cf\u5b50\u6bd4\u7279\u6570\u7b49\u4e8e\u7279\u5f81\u6570\u3002\u5728\u7b2c\u4e00\u4e2a\u5faa\u73af\u4e2d\uff0c\u5b83\u5c06\u65cb\u8f6c\u95e8\u6dfb\u52a0\u5230\u7535\u8def\u4e2d\uff0c\u6bcf\u4e2a\u65cb\u8f6c\u95e8\u90fd\u6709\u53c2\u6570\u5316\u7684\u65cb\u8f6c\u89d2\u5ea6\u3002\u7b2c\u4e8c\u4e2a\u5faa\u73af\u5c06 CNOT \u95e8\u6dfb\u52a0\u5230\u7535\u8def\u4e2d\u3002\u6bcf\u6b21\u8fed\u4ee3\u65f6\uff0c\u90fd\u4f1a\u6dfb\u52a0\u4e00\u4e2a CNOT \u95e8\uff0c\u5c06\u5f53\u524d\u7684\u91cf\u5b50\u6bd4\u7279\u4f5c\u4e3a\u63a7\u5236\u6bd4\u7279\uff08\u7b2c 0 \u4e2a\u91cf\u5b50\u6bd4\u7279\uff09\uff0c\u76ee\u6807\u91cf\u5b50\u6bd4\u7279\u7531\u5faa\u73af\u7d22\u5f15\u51b3\u5b9a\u3002\u6b64\u5916\uff0c\u8fd8\u4f1a\u6dfb\u52a0\u53e6\u4e00\u4e2a CNOT \u95e8\uff0c\u5c06\u5f53\u524d\u7684\u91cf\u5b50\u4f4d\u4f5c\u4e3a\u63a7\u5236\u4f4d\uff0c\u5c06\u4e0b\u4e00\u4e2a\u91cf\u5b50\u4f4d\u4f5c\u4e3a\u76ee\u6807\u4f4d\u3002<\/p>\n\n\n\n
\u8fd9\u4e2a\u8fc7\u7a0b\u4e3a\u6211\u4eec\u7684\u91cf\u5b50\u7535\u8def\u6784\u5efa\u4e86\u53cd\u6f14\uff0c\u672c\u8d28\u4e0a\u5c31\u662f\u521b\u5efa\u4e86\u6211\u4eec\u4e4b\u524d\u5b9a\u4e49\u7684\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u3002\u8be5\u7535\u8def\u7684\u793a\u610f\u56fe\u5df2\u5728\u524d\u9762\u4ecb\u7ecd\u8fc7\uff0c\u4ee5\u4f9b\u53c2\u8003\u3002<\/p>\n\n\n\n
\u542f\u52a8 Anstaz \u7535\u8def\uff1a<\/strong><\/p>\n\n\n\n
ansatz = ans(num_features, 5) #anstaz(num_qubits=num_features, reps=5)\n\n#creating train test split\nx_train = np.array(input_sequences[0:400])\nx_test = np.array(input_sequences[400:])\ny_train = np.array(labels[0:400])\ny_test = np.array(labels[400:])<\/code><\/pre>\n\n\n\n
\u73b0\u5728\uff0c\u6211\u4eec\u7ee7\u7eed\u521b\u5efa\u4e00\u4e2a\u53d8\u5206\u91cf\u5b50\u7535\u8def\uff08VQC\uff09\uff0c\u5b83\u53ef\u4ee5\u4f5c\u4e3a\u795e\u7ecf\u7f51\u7edc\u8fd0\u884c\u3002\u5728\u8fd9\u4e2a\u7535\u8def\u4e2d\uff0c\u6211\u4eec\u65e2\u5305\u542b\u4e86\u5b9a\u4e49\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\u7684ansatz\uff0c\u4e5f\u5305\u542b\u4e86\u6211\u4eec\u7684\u7279\u5f81\u7f16\u7801\u3002\u4e3a\u6b64\uff0c\u6211\u4eec\u4ece qiskit_machine_learning.classifiers \u4e2d\u5bfc\u5165\u4e86 VQC \u6a21\u5757\u3002VQC \u5c01\u88c5\u4e86\u6211\u4eec\u795e\u7ecf\u7f51\u7edc\u7684\u91cf\u5b50\u5904\u7406\u65b9\u9762\uff0c\u5b83\u4e0e Qiskit \u7684\u96c6\u6210\u7b80\u5316\u4e86\u91cf\u5b50\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u5b9e\u73b0\u3002<\/p>\n\n\n\n
VQC:<\/strong>
<\/p>\n\n\n\n
vqr = VQR(\n    feature_map = feature_map,\n    ansatz = ansatz,\n    optimizer = optimizer,\n)\n\nvqr.fit(x_train,y_train)\nvqr_mse = mean_squared_error(y_test, vqr.predict(x_test))\n\n# Calculate root mean squared error\nvqr_rmse = np.sqrt(vqr_mse)<\/code><\/pre>\n\n\n\n
\u5728\u6700\u540e\u4e00\u6b65\uff0c\u6211\u4eec\u5c06\u53d8\u5f02\u91cf\u5b50\u56de\u8def\uff08VQC\uff09\u4e0e\u6211\u4eec\u7684\u7279\u5f81\u8fdb\u884c\u62df\u5408\u3002\u8fd9\u5305\u62ec\u5728\u6211\u4eec\u7684\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u3002\u62df\u5408\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u901a\u8fc7\u8ba1\u7b97\u5747\u503c\u8bef\u5dee\u548c\u5747\u503c\u6839\u8bef\u5dee\u6765\u8bc4\u4f30\u5176\u6027\u80fd\u3002\u8fd9\u4e00\u8bc4\u4f30\u6b65\u9aa4\u6709\u52a9\u4e8e\u6211\u4eec\u8861\u91cf\u53d8\u5f02\u91cf\u5b50\u56de\u8def\u5728\u5904\u7406\u7ed9\u5b9a\u7279\u5f81\u548c\u9884\u6d4b\u7ed3\u679c\u65b9\u9762\u7684\u51c6\u786e\u6027\u548c\u6709\u6548\u6027\u3002<\/p>\n\n\n\n

3.3 \u7ecf\u5178\u795e\u7ecf\u7f51\u7edc<\/strong><\/h3>\n\n\n\n
\u73b0\u5728\uff0c\u8ba9\u6211\u4eec\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u7ecf\u5178\u795e\u7ecf\u7f51\u7edc\uff0c\u4f5c\u4e3a\u4e0e\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u8fdb\u884c\u6bd4\u8f83\u7684\u57fa\u51c6\u3002\u6211\u4eec\u9009\u62e9\u7684\u67b6\u6784\u5c06\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u591a\u5c42\u611f\u77e5\u5668\uff08MLP\uff09\uff0c\u5b83\u53ea\u6709\u4e00\u4e2a\u9690\u85cf\u5c42\uff0c\u914d\u5907 64 \u4e2a\u8282\u70b9\u3002\u8fd9\u4e2a\u7ecf\u5178\u795e\u7ecf\u7f51\u7edc\u5c06\u4e3a\u6211\u4eec\u63d0\u4f9b\u4e00\u4e2a\u53c2\u8003\u70b9\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5b83\u6765\u8bc4\u4f30\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u7684\u6027\u80fd\u3002<\/p>\n\n\n\n
\u53e4\u5178 ANN\uff1a<\/strong><\/p>\n\n\n\n
model = Sequential()\nmodel.add(Dense(64,activation = 'relu', input_shape = (4,)))\nmodel.add(Dense(1))\n\nmodel.compile(optimizer = 'adam', loss = 'mean_squared_error')\n\nmodel.fit(x_train, y_train, epochs = 20, batch_size = 32, validation_data = (x_test,y_test))\n\nloss = model.evaluate(x_test, y_test)\nprediction = model.predict(x_test)\n\nann_mse = mean_squared_error(y_test, prediction.flatten())\nann_rmse = np.sqrt(ann_mse)<\/code><\/pre>\n\n\n\n

\u56db\u3001\u7ed3\u679c\u4e0e\u6bd4\u8f83<\/strong><\/h2>\n\n\n\n
\u7ed3\u679c\u5982\u4e0b\uff1a<\/strong> 1)QNN\uff1a< 3.5 rmse 2)ann: > 3.6 rmse<\/p>\n\n\n\n
\u5728\u8fd9\u6b21\u8bc4\u4f30\u4e2d\uff0c\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\uff08QNN\uff09\u7684\u8868\u73b0\u660e\u663e\u4f18\u4e8e\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\uff08ANN\uff09\uff0c\u663e\u793a\u4e86\u5176\u524d\u666f\u3002\u4e0d\u8fc7\uff0c\u5fc5\u987b\u627f\u8ba4\u7684\u662f\uff0c\u83b7\u5f97\u7684\u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\u503c\u53ef\u80fd\u8fbe\u4e0d\u5230\u9884\u671f\u7684\u6ee1\u610f\u5ea6\u3002\u672c\u5b9e\u9a8c\u7684\u4e3b\u8981\u76ee\u7684\u662f\u7a81\u51fa\u91cf\u5b50\u8ba1\u7b97\u7684\u6f5c\u529b\uff0c\u5c55\u793a\u5176\u751f\u6210\u5353\u8d8a\u7ed3\u679c\u548c\u6784\u5efa\u9002\u7528\u4e8e\u5546\u4e1a\u7528\u9014\u7684\u6a21\u578b\u7684\u80fd\u529b\u3002<\/p>\n\n\n\n
\u672c\u7814\u7a76\u9884\u8ba1\uff0c\u968f\u7740\u91cf\u5b50\u8ba1\u7b97\u673a\u4e0d\u65ad\u8fdb\u6b65\uff0c\u5728\u6709\u6548\u8bad\u7ec3\u8fd9\u4e9b\u6a21\u578b\u65b9\u9762\u8fbe\u5230\u66f4\u9ad8\u7684\u9c81\u68d2\u6027\u6c34\u5e73\uff0c\u5728\u4e0d\u4e45\u7684\u5c06\u6765\uff0c\u8be5\u6280\u672f\u5728\u73b0\u5b9e\u4e16\u754c\u7684\u5e94\u7528\u5c06\u9010\u6b65\u53d8\u5f97\u66f4\u52a0\u5b9e\u7528\u3002<\/p>\n\n\n\n

\u5728\u8fd9\u4e9b\u53d1\u73b0\u7684\u57fa\u7840\u4e0a\uff0c\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\uff08QNN\uff09\u663e\u7136\u6709\u671b\u5f97\u5230\u8fdb\u4e00\u6b65\u53d1\u5c55\u548c\u5b9e\u9645\u5e94\u7528\u3002\u867d\u7136\u76ee\u524d\u7684\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u57fa\u51c6\u53ef\u80fd\u4f18\u4e8e QNN\uff0c\u4f46\u4ecd\u6709\u5f88\u5927\u7684\u6539\u8fdb\u6f5c\u529b\u3002\u89e3\u51b3\u5f53\u524d\u7684\u5c40\u9650\u6027\u662f\u53ef\u4ee5\u9884\u89c1\u7684\uff0c\u7279\u522b\u662f\u968f\u7740\u91cf\u5b50\u8ba1\u7b97\u7684\u4e0d\u65ad\u8fdb\u6b65\u3002\u6210\u529f\u5f00\u53d1\u51fa\u5f3a\u5927\u7684\u91cf\u5b50\u8ba1\u7b97\u673a\uff0c\u5c06\u4e3a\u5728\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u9886\u57df\u53d6\u5f97\u66f4\u5353\u8d8a\u7684\u6210\u679c\u6253\u5f00\u4e00\u6247\u5927\u95e8\u3002<\/p>\n\n\n\n
\u6ce8\uff1a<\/strong>\u6211\u5c55\u793a\u4e86\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc (QNN) \u548c\u7ecf\u5178\u795e\u7ecf\u7f51\u7edc (CNN) \u7684\u57fa\u672c\u6f14\u793a\uff0c\u4ee5\u8bf4\u660e\u5b83\u4eec\u7684\u6784\u9020\u5e76\u7a81\u51fa\u7ed3\u679c\u7684\u5dee\u5f02\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u53ef\u80fd\u4f1a\u51fa\u73b0\u5dee\u5f02\uff0c\u56e0\u6b64\u8bf7\u968f\u610f\u8c03\u6574\u6240\u63d0\u4f9b\u7684\u793a\u4f8b\uff0c\u4ee5\u66f4\u597d\u5730\u9002\u5e94\u60a8\u7684\u5177\u4f53\u4f7f\u7528\u60c5\u51b5\u3002\u8bf7\u5bf9\u4ee3\u7801\u548c\u53c2\u6570\u8fdb\u884c\u76f8\u5e94\u8c03\u6574\uff0c\u4ee5\u4f18\u5316\u6027\u80fd\uff0c\u5e76\u89e3\u51b3\u5728\u4e3a\u60a8\u7684\u7279\u5b9a\u5e94\u7528\u5b9e\u65bd\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u65f6\u53ef\u80fd\u51fa\u73b0\u7684\u4efb\u4f55\u96be\u9898\u3002<\/p>\n\n\n\n

\u4e94\u3001\u7ed3\u8bba<\/strong><\/h2>\n\n\n\n
\u603b\u4e4b\uff0c\u672c\u9879\u76ee\u63a2\u7d22\u4e86\u91cf\u5b50\u8ba1\u7b97\u548c\u795e\u7ecf\u7f51\u7edc\u7684\u521b\u5efa\uff0c\u7a81\u51fa\u4e86\u5176\u672a\u6765\u7684\u53d1\u5c55\u6f5c\u529b\u3002\u968f\u7740\u6280\u672f\u7684\u8fdb\u6b65\uff0c\u6211\u4eec\u6709\u673a\u4f1a\u5229\u7528\u91cf\u5b50\u8ba1\u7b97\u673a\u5f00\u53d1\u66f4\u590d\u6742\u7684\u91cf\u5b50\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u3002\u8fd9\u4e9b\u7cfb\u7edf\u53ef\u4ee5\u5927\u5927\u7f29\u77ed\u8bad\u7ec3\u65f6\u95f4\uff0c\u53d7\u76ca\u4e8e\u91cf\u5b50\u6bd4\u7279\u7684\u6548\u7387\uff0c\u5e76\u80fd\u5728\u7535\u8def\u5f62\u6210\u4e2d\u4f7f\u7528\u66f4\u591a\u7684\u91cf\u5b50\u6bd4\u7279\u3002\u8fd9\u4e00\u8fdb\u5c55\u4e3a\u589e\u5f3a\u8ba1\u7b97\u80fd\u529b\u548c\u89e3\u51b3\u95ee\u9898\u7684\u80fd\u529b\u6253\u5f00\u4e86\u5927\u95e8\uff0c\u4e3a\u91cf\u5b50\u8ba1\u7b97\u5728\u673a\u5668\u5b66\u4e60\u5e94\u7528\u9886\u57df\u7684\u672a\u6765\u6307\u660e\u4e86\u4e00\u6761\u5145\u6ee1\u5e0c\u671b\u7684\u9053\u8def\u3002<\/p>\n\n\n\n
    \n
  1. \u91cf\u5b50\u8ba1\u7b97\u673a\u5229\u7528\u91cf\u5b50\u4f4d\u7684\u53e0\u52a0\u548c\u7ea0\u7f20\u7279\u6027\uff0c\u80fd\u591f\u5728\u5904\u7406\u548c\u8ba1\u7b97\u65b9\u9762\u63d0\u4f9b\u6bd4\u7ecf\u5178\u8ba1\u7b97\u673a\u66f4\u9ad8\u7684\u6548\u7387\u3002<\/li>\n\n\n\n
  2. \u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u901a\u8fc7\u53c2\u6570\u5316\u95e8\u548c\u91cf\u5b50\u7535\u8def\u5b9e\u73b0\uff0c\u80fd\u591f\u5728\u91cf\u5b50\u8ba1\u7b97\u673a\u4e0a\u6267\u884c\uff0c\u6709\u6f5c\u529b\u5728\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u4e2d\u63d0\u4f9b\u4f18\u4e8e\u7ecf\u5178\u795e\u7ecf\u7f51\u7edc\u7684\u6027\u80fd\u3002<\/li>\n\n\n\n
  3. \u5728\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u65f6\uff0c\u91cf\u5b50\u795e\u7ecf\u7f51\u7edc\u80fd\u591f\u6355\u6349\u6570\u636e\u4e2d\u7684\u590d\u6742\u6a21\u5f0f\uff0c\u5e76\u5728\u9884\u6d4b\u80a1\u7968\u4ef7\u683c\u7b49\u4efb\u52a1\u4e2d\u8868\u73b0\u51fa\u4f18\u5f02\u7684\u6f5c\u529b\u3002<\/li>\n\n\n\n
  4. \u5c3d\u7ba1\u91cf\u5b50\u673a\u5668\u5b66\u4e60\u76ee\u524d\u4ecd\u7136\u5904\u4e8e\u53d1\u5c55\u521d\u671f\uff0c\u4f46\u968f\u7740\u91cf\u5b50\u8ba1\u7b97\u6280\u672f\u7684\u8fdb\u6b65\uff0c\u5b83\u6709\u671b\u5728\u672a\u6765\u6210\u4e3a\u89e3\u51b3\u590d\u6742\u95ee\u9898\u7684\u5f3a\u6709\u529b\u5de5\u5177\u3002<\/li>\n\n\n\n
  5. \u672c\u6587\u7684\u7814\u7a76\u8868\u660e\uff0c\u91cf\u5b50\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5728\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u95ee\u9898\u65f6\uff0c\u4e0e\u7ecf\u5178\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u76f8\u6bd4\uff0c\u5177\u6709\u76f8\u4f3c\u6216\u66f4\u597d\u7684\u6027\u80fd\u3002<\/li>\n\n\n\n
  6. \u968f\u7740\u91cf\u5b50\u8ba1\u7b97\u673a\u7684\u53d1\u5c55\u548c\u91cf\u5b50\u7b97\u6cd5\u7684\u6539\u8fdb\uff0c\u91cf\u5b50\u673a\u5668\u5b66\u4e60\u5c06\u5728\u672a\u6765\u63d0\u4f9b\u66f4\u591a\u7684\u5e94\u7528\u53ef\u80fd\u6027\uff0c\u5c24\u5176\u662f\u5728\u91d1\u878d\u5e02\u573a\u5206\u6790\u7b49\u9886\u57df\u3002<\/li>\n<\/ol>\n\n\n\n
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    \u672c\u6587\u5185\u5bb9\u4ec5\u4ec5\u662f\u6280\u672f\u63a2\u8ba8\u548c\u5b66\u4e60\uff0c\u5e76\u4e0d\u6784\u6210\u4efb\u4f55\u6295\u8d44\u5efa\u8bae\u3002<\/mark><\/strong><\/p>\n\n\n\n
    \u8f6c\u53d1\u8bf7\u6ce8\u660e\u539f\u4f5c\u8005\u548c\u51fa\u5904\u3002<\/mark><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"
    \u4f5c\u8005\uff1a\u8001\u4f59\u635e\u9c7c \u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005\u3002…<\/p>\n
    Continue reading\u7528 Python \u4e2d\u7684\u91cf\u5b50\u673a\u5668\u5b66\u4e60\u9884\u6d4b\u80a1\u7968\u4ef7\u683c<\/span><\/a><\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[6],"class_list":["post-1413","post","type-post","status-publish","format-standard","hentry","category-aiinvest","tag-6","entry"],"_links":{"self":[{"href":"https:\/\/blog.laoyulaoyu.top\/index.php\/wp-json\/wp\/v2\/posts\/1413","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.laoyulaoyu.top\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.laoyulaoyu.top\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.laoyulaoyu.top\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.laoyulaoyu.top\/index.php\/wp-json\/wp\/v2\/comments?post=1413"}],"version-history":[{"count":2,"href":"https:\/\/blog.laoyulaoyu.top\/index.php\/wp-json\/wp\/v2\/posts\/1413\/revisions"}],"predecessor-version":[{"id":1415,"href":"https:\/\/blog.laoyulaoyu.top\/index.php\/wp-json\/wp\/v2\/posts\/1413\/revisions\/1415"}],"wp:attachment":[{"href":"https:\/\/blog.laoyulaoyu.top\/index.php\/wp-json\/wp\/v2\/media?parent=1413"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.laoyulaoyu.top\/index.php\/wp-json\/wp\/v2\/categories?post=1413"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.laoyulaoyu.top\/index.php\/wp-json\/wp\/v2\/tags?post=1413"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}