31 skills found · Page 1 of 2
SonyCSLParis / Vqcpc BachVector Quantized Contrastive Predictive Coding for Template-based Music Generation
rasidi3112 / Quantum Machine LearningReproducible QML benchmark: VQC vs QSVM on binary tasks. Modular, cross-platform pipeline w/ artifact logging.
Sumitchongder / Quantum Hyperspectral ImagingQ-HSI explores the intersection of Deep Learning and Quantum Computing for medical diagnostics. This project implements and compares a Classical CNN, a Variational Quantum Classifier (VQC), and a high-accuracy Hybrid Ensemble to classify benign vs. malignant skin lesions.
supremacyfuture / VQCvariational quantum circuit simulator in Julia, under GPLv3
vuquangchien2208 / VQC# Loon全局配置 by nzw9314 # YouTube去广告请删除hostname 后的.bak # GitHub主页(https://github.com/nzw9314) # TG通知频道 (https://t.me/nzw9314News) [General] ipv6 = false skip-proxy = 10.0.0.0/8,127.0.0.0/8,169.254.0.0/16,192.0.2.0/24,192.168.0.0/16,198.51.100.0/24,224.0.0.0/4,*.local,localhostlocal bypass-tun = 10.0.0.0/8,127.0.0.0/8,169.254.0.0/16,192.0.2.0/24,192.168.0.0/16,198.51.100.0/24,224.0.0.0/4 # [DNS] => DNS 服务器 dns-server = system,1.2.4.8,119.29.29.29,223.5.5.5 allow-udp-proxy = true host = 127.0.0.1 [Remote Proxy] # 订阅节点 # 别名 = 订阅URL ✈️机场1 = https://example/server-complete.txt ✈️机场2 = https://example2/server-complete.txt [Remote Filter] # 筛选订阅节点，筛选后的结果可加入到策略组中，目前支持三种筛选方式 # NodeSelect: 使用在UI上选择的节点。 # NameKeyword: 根据提供的关键词对订阅中所有节点的名称进行筛选，使用筛选后的节点。 # NameRegex: 根据提供的正则表达式对订阅中所有节点的名称进行筛选，使用筛选后的节点。 𝐏𝐚𝐲𝐏𝐚𝐥 = NodeSelect,✈️机场1,✈️机场2 𝐍𝐞𝐭𝐟𝐥𝐢𝐱 = NameKeyword,✈️机场1,✈️机场2, FilterKey = Netflix 🇭🇰香港 = NameRegex,✈️机场1,✈️机场2, FilterKey = 香港|HK 🇯🇵日本 = NameRegex,✈️机场1,✈️机场2, FilterKey = 日本|JP 🇰🇷韩国 = NameRegex,✈️机场1,✈️机场2, FilterKey = 韩国|KR 🇺🇸美国 = NameRegex,✈️机场1,✈️机场2, FilterKey = 美国|US Other = NameRegex,✈️机场1,✈️机场2, FilterKey = ^(?!.*(HK|JP|US|KR|香港|日本|韩国|美国)).*$ [Proxy] # 本地节点 # 内置 DIRECT、REJECT 策略 # 节点名称 = 协议，服务器地址，服务器端口，加密协议，密码， 1 = Shadowsocks,1.2.3.4,443,aes-128-gcm,"”password“" 2 = Shadowsocks,1.2.3.4,443,aes-128-gcm,"”password“" 3 = ShadowsocksR,1.2.3.4,443,aes-256-cfb,"”password“",auth_aes128_md5,{},tls1.2_ticket_auth,{} 4 = ShadowsocksR,1.2.3.4,10076,aes-128-cfb,"”password“",auth_aes128_md5,{},tls1.2_ticket_auth,{} # vmess # 节点名称 = 协议，服务器地址，端口，加密方式，UUID，传输方式:(tcp/ws),path：websocket握手header中的path，host：websocket握手header中的path，over-tls:是否tls，tls-name：远端w服务器域名，skip-cert-verify：是否跳过证书校验（默认否） #5 = vmess, 1.2.3.4, 10086, aes-128-gcm,”uuid“,transport:ws,path:/,host:icloud.com,over-tls:true,tls-name:youtTlsServerName.com,skip-cert-verify:false # 解锁网易云音乐灰色歌曲 🎧 = http,106.52.127.72,19951 [Proxy Group] # 策略组 # 节点选项 🕹𝐅𝐢𝐧𝐚𝐥 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 🔰𝐏𝐫𝐨𝐱𝐲 = select,♻️𝐀𝐮𝐭𝐨,🌀𝐒𝐞𝐥𝐞𝐜𝐭,🔴𝐅𝐚𝐥𝐥𝐛𝐚𝐜𝐤 # url-test模式，给提供的url发出http header请求，根据返回结果，选择测速最快的节点，默认间隔600s，测速超时时间5s，为了避免资源浪费，建议节点数不要过多，只支持单个节点和远端节点，其他会被忽略 ♻️𝐀𝐮𝐭𝐨 = url-test,🇭🇰香港,🇯🇵日本,🇰🇷韩国,🇺🇸美国,url = http://bing.com/,interval = 600 # select模式,手动选择模式 🌀𝐒𝐞𝐥𝐞𝐜𝐭 = select,🇭🇰香港,🇯🇵日本,🇰🇷韩国,🇺🇸美国 # fallback模式，和url-test类似，不同的是会根据顺序返回第一个可用的节点，为了避免资源浪费，建议节点数不要过多，只支持单个节点和远端节点，其他会被忽略 🔴𝐅𝐚𝐥𝐥𝐛𝐚𝐜𝐤 = fallback,🇭🇰香港,🇯🇵日本,🇰🇷韩国,🇺🇸美国,REJECT,url = http://bing.com/,interval = 600 🎵𝐓𝐢𝐤𝐓𝐨𝐤 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 🖥𝐍𝐞𝐭𝐟𝐥𝐢𝐱 = select,𝐍𝐞𝐭𝐟𝐥𝐢𝐱,🎯𝐃𝐢𝐫𝐞𝐜𝐭 💳𝐏𝐚𝐲𝐏𝐚𝐥 = select,𝐏𝐚𝐲𝐏𝐚𝐥,🎯𝐃𝐢𝐫𝐞𝐜𝐭 📱𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 🎬𝐘𝐨𝐮𝐓𝐮𝐛𝐞 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 🔞𝐏𝐨𝐫𝐧𝐇𝐮𝐛 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 # 🔓网易云音乐灰色歌曲,需要节点支持解锁 🎧𝐍𝐞𝐭𝐞𝐚𝐬𝐞𝐌𝐮𝐬𝐢𝐜 = select,🎯𝐃𝐢𝐫𝐞𝐜𝐭,🎧,🔰𝐏𝐫𝐨𝐱𝐲 # 网络测速 🚀𝐒𝐩𝐞𝐞𝐝𝐓𝐞𝐬𝐭 = select,🔰𝐏𝐫𝐨𝐱𝐲,🎯𝐃𝐢𝐫𝐞𝐜𝐭 # 苹果服务 🍎𝐀𝐩𝐩𝐥𝐞 = select,🎯𝐃𝐢𝐫𝐞𝐜𝐭,🔰𝐏𝐫𝐨𝐱𝐲 # 白名单模式 PROXY，黑名单模式 DIRECT # 广告拦截 🚫𝐀𝐝 𝐁𝐥𝐨𝐜𝐤 = select,⛔️𝐑𝐞𝐣𝐞𝐜𝐭,🎯𝐃𝐢𝐫𝐞𝐜𝐭 # 直接连接 🎯𝐃𝐢𝐫𝐞𝐜𝐭 = select,DIRECT # 拦截 ⛔️𝐑𝐞𝐣𝐞𝐜𝐭 = select,REJECT # SSID # 别名 = ssid，默认 = 策略， 蜂窝 = 策略， ssid名称 = 策略 #SSID = ssid, default = PROXY, cellular = DIRECT, ”DivineEngine“ = PROXY [Rule] # 本地规则 # Type:DOMAIN-SUFFIX,DOMAIN,DOMAIN-KEYWORD,USER-AGENT,URL-REGEX,IP-CIDR # Strategy:DIRECT,Proxy,REJECT # Options:force-remote-dns(Default:false),no-resolve # 𝐍𝐞𝐭𝐞𝐚𝐬𝐞𝐌𝐮𝐬𝐢𝐜 DOMAIN-SUFFIX,music.126.net,DIRECT # GeoIP China GEOIP,CN,🎯𝐃𝐢𝐫𝐞𝐜𝐭 FINAL, 🕹𝐅𝐢𝐧𝐚𝐥 [Remote Rule] # 订阅规则 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Liby.list,🚫𝐀𝐝 𝐁𝐥𝐨𝐜𝐤 # BlockOTA https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/BlockOTA.list,🚫𝐀𝐝 𝐁𝐥𝐨𝐜𝐤 # Antirevoke https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/Antirevoke.list,🚫𝐀𝐝 𝐁𝐥𝐨𝐜𝐤 # > TikTok https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/TikTok.list,🎵𝐓𝐢𝐤𝐓𝐨𝐤 # > Youtube https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/YouTube.list,🎬𝐘𝐨𝐮𝐓𝐮𝐛𝐞 # > Netflix https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/Netflix.list,🖥𝐍𝐞𝐭𝐟𝐥𝐢𝐱 # > PronHub https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/Pornhub.list,🔞𝐏𝐨𝐫𝐧𝐇𝐮𝐛 # Telegram https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/Telegram.list,📱𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦 # > PayPal https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/PayPal.list,💳𝐏𝐚𝐲𝐏𝐚𝐥 # > Outlook、Gmail https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/Mail.list,🔰𝐏𝐫𝐨𝐱𝐲 # > GoogleDrive https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/GoogleDrive.list,🔰𝐏𝐫𝐨𝐱𝐲 # Speedtest https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Sub/Speedtest.list,🚀𝐒𝐩𝐞𝐞𝐝𝐓𝐞𝐬𝐭 # >Unlock NeteaseMusic https://raw.githubusercontent.com/nzw9314/Surge/master/Ruleset/UnlockNeteaseMusic.list,🎧𝐍𝐞𝐭𝐞𝐚𝐬𝐞𝐌𝐮𝐬𝐢𝐜 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Apple_CDN.list,🍎𝐀𝐩𝐩𝐥𝐞 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Apple_API.list,🍎𝐀𝐩𝐩𝐥𝐞 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/AsianMedia.list,🎯𝐃𝐢𝐫𝐞𝐜𝐭 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/GlobalMedia.list,🔰𝐏𝐫𝐨𝐱𝐲 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Domestic.list,🎯𝐃𝐢𝐫𝐞𝐜𝐭 https://raw.githubusercontent.com/eHpo1/Rules/master/Surge4/Ruleset/Global.list,🔰𝐏𝐫𝐨𝐱𝐲 [URL Rewrite] # 本地重写 # > TikTok Unlock (By Choler) # 区域请修改下方国家代码，默认为日本 JP (?<=_region=)CN(?=&) JP 307 (?<=&app_version=)16..(?=.?.?&) 1 307 (?<=\?version_code=)16..(?=.?.?&) 1 307 # > 抖音 去广告&水印 # 需配合脚本使用 ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/feed\/ https://aweme.snssdk.com/aweme/v1/feed/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/aweme\/post\/ https://aweme.snssdk.com/aweme/v1/aweme/post/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/follow\/feed\/ https://aweme.snssdk.com/aweme/v1/follow/feed/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/nearby\/feed\/ https://aweme.snssdk.com/aweme/v1/nearby/feed/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/search\/item\/ https://aweme.snssdk.com/aweme/v1/search/item/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/general\/search\/single\/ https://aweme.snssdk.com/aweme/v1/general/search/single/ header ^https?:\/\/[\w-]+\.amemv\.com\/aweme\/v\d\/hot/search\/video\/list\/ https://aweme.snssdk.com/aweme/v1/hot/search/video/list/ header enable = true [Remote Rewrite] #订阅重写 去广告 by eHpo # 格式：订阅url，别名(可选) https://raw.githubusercontent.com/eHpo1/Rules/master/Loon/Rewrite.conf,eHpo https://raw.githubusercontent.com/nzw9314/Loon/master/Q-Search_All_in_one.conf,Q-Search_All_in_one [Script] # 本地脚本 enable = true [Remote Script] # 远程脚本 https://raw.githubusercontent.com/nzw9314/Loon/master/Task.conf,签到 https://raw.githubusercontent.com/nzw9314/Loon/master/Script.conf,脚本 https://raw.githubusercontent.com/nzw9314/Loon/master/Cookie.conf,Cookie [MITM] enable = true hostname = *.googlevideo.com.bak skip-server-cert-verify = true ca-p12 = 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 ca-passphrase = eHpoj
AlexZou14 / VQCNIRAAAI 2024: VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook
maximer-v / Quantum Machine LearningThis is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).
guochu / VQC.jlvariational quantum circuit
SonyCSLParis / Vqcpc GanVQCPC-GAN: Variable-length Adversarial Audio Synthesis using Vector-Quantized Contrastive Predictive Coding
Jindi0 / SQNNScalable Quantum Neural Network builds and trains a large-scale QNN in a modular fashion. SQNN is evaluated with a binary classification task on the MNIST dataset.
vvvm23 / Stoi VqcpcRepository for paper "Non-intrusive speech intelligibility prediction from discrete latent representations"
GhadaAbdulsalam / Explainable Heart Disease Prediction Using Ensemble Quantum MLAn ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease. The proposed model is a bagging ensemble learning model where Quantum Support Vector Classifier is used as the base classifier. Furthermore, in order to make the model's outcomes more explainable, the importance of every single feature in the prediction is computed and visualized using SHapley Additive exPlanations (SHAP) framework. In the experimental study, other stand-alone quantum classifiers, namely, Quantum Support Vector Classifier (QSVC), Quantum Neural Network (QNN), and Variational Quantum Classifier (VQC) were applied and compared with classical machine learning classifiers such as Support Vector Classifier (SVC), and Artificial Neural Network (ANN).
PatrickHuembeli / Vqc Loss LandscapesCharacterizing the loss landscape of variational quantum circuits
jqi41 / TTN VQCNo description available
MohammadYehya / Custom Variational Quantum ClassifierCreating a Variational Quantum Classifier (VQC) generator with custom ansätz for different UCI datasets.
zhangbq-research / VQCTCodebook Transfer with Part-of-Speech
jqi41 / Pretrained TTN VQCAn implementation of pretrained TTN-VQC
kevinkayyy / VQC LiteNo description available
Fatemoisted / VQCrystalNo description available
Maokami / Vqc In LeanLean 4 port of the Verified Quantum Computing. Developed as a personal learning project to deepen understanding of quantum computing concepts and formal verification.
