Vector Quantized Contrastive Predictive Coding for Template-based Music Generation

Reproducible QML benchmark: VQC vs QSVM on binary tasks. Modular, cross-platform pipeline w/ artifact logging.

Q-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.

variational quantum circuit simulator in Julia, under GPLv3

# 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

AAAI 2024: VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook

This 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).

variational quantum circuit

VQCPC-GAN: Variable-length Adversarial Audio Synthesis using Vector-Quantized Contrastive Predictive Coding

Scalable 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.

Repository for paper "Non-intrusive speech intelligibility prediction from discrete latent representations"

An 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). ‎

Characterizing the loss landscape of variational quantum circuits

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Creating a Variational Quantum Classifier (VQC) generator with custom ansätz for different UCI datasets.

Codebook Transfer with Part-of-Speech

An implementation of pretrained TTN-VQC

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Lean 4 port of the Verified Quantum Computing. Developed as a personal learning project to deepen understanding of quantum computing concepts and formal verification.

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Implementation of a Variational Quantum Classifier (VQC) using Qiskit, exploring quantum machine learning concepts like quantum neural networks, feature mapping, and optimization techniques.

Hybrid Quantum-Classical Deep Learning framework combining Variational Quantum Circuits (PennyLane) with Classical Neural Networks (PyTorch) for binary classification. Features 8-qubit VQC, data re-uploading, dual-rate optimizer, and comprehensive visualization engine.

Experimental platform exploring the integration of Federated Learning, Quantum Machine Learning (VQC, QKA), and Post-Quantum Cryptography. Built with PennyLane, FastAPI, and Flutter. Features quantum-enhanced aggregation, zero-noise extrapolation, and Kyber/Dilithium security. Research/educational project - not production ready.

India is on track to become the world’s diabetes capital thus demanding accurate diagnosis of Diabetic retinopathy from optical coherence tomography (OCT) retinal images. Accurate and faster diagnosis is difficult as it depends on quality of image, operator handling and also the growing number of patients. In this paper we propose the use of quantum transfer learning model to accomplish diagnosis of Diabetic Retinopathy. Quantum Transfer Learning (QTL), is a hybrid combination of classical transfer learning and quantum computing. Unlike classical computers, quantum computers provide faster computation and better accuracy. The concept of QTL is mainly used where the dataset size is limited. The QTL model, diagnostically significant image features are extracted with Resnet18 Convolutional Neural NEtwork (CNN) model, which is reduced to 4-bit feature vector to be encoded as qubit and is finally classified by utilizing Variational Quantum Circuit (VQC). The proposed model gave a better accuracy than existing state of the art methods in terms of high accuracy despite with a smaller set of images in the training phase.

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Quantum‑augmented fine‑tuning of GPT‑Neo‑125M : Co‑training LoRA adapters with multiple Pennylane VQC variations.

Quantum-Enhanced Neuro-Coaching System (QENCS). Real-time ADHD focus analysis using Variational Quantum Classifiers (VQC) and 4-layer Entanglement.

Genetic Algorithm search of the spaces of Ansatz for a specific QAE problem. There is a potential for an extension to general VQCs.

In this repository you can find the codes that have been used for otaining the results shown in the article "Relaxation of QAOA energy landscape with a Neural Network" by J. Rivera-Dean et al.