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Quantum Self-Supervised Learning Frameworks
This module implements cutting-edge quantum self-supervised learning methods that leverage quantum mechanical principles for enhanced representation learning without labeled data:
- Quantum Contrastive Learning with entanglement-based similarity
- Quantum Masked Learning with superposition encoding
- Quantum SimCLR/SimSiam with quantum augmentations
- Quantum BYOL with quantum momentum updates
- Quantum SwAV with quantum cluster assignments
- Advanced quantum representation learning frameworks
Structs§
- Clustering
Config - Contrastive
Config - Contrastive
Loss Computer - Decoherence
Model - Entanglement
Momentum - Entanglement
Similarity - Inverse
Operation - Learning
Rate Scheduler - Masked
Learning Config - Masked
Loss Computer - Momentum
Config - Momentum
Scheduler - Prototype
Evolution - Quantum
Assignment Computer - Quantum
Augmenter - Quantum
Batch Metrics - Quantum
Clustering Learner - Quantum
Contrastive Learner - Quantum
Decoder - Quantum
Decoder Layer - Quantum
DotProduct - QuantumEMA
Config - QuantumEMA
State - Quantum
Encoder - Quantum
Encoder Decoder - Quantum
Encoder Layer - Quantum
Gate - Quantum
KeyEncoder - Quantum
KeyEvolution - Quantum
Mask Evolution - Quantum
Mask Generator - Quantum
Masked Learner - Quantum
Masking Engine - Quantum
Momentum - Quantum
Momentum Learner - Quantum
Momentum Updater - Quantum
Negative Sampler - Quantum
Online Network - Quantum
Prediction Layer - Quantum
Predictor - Quantum
Projection Head - Quantum
Projection Layer - Quantum
Projector - Quantum
Prototype Bank - Quantum
Randomness - Quantum
Reconstruction Layer - Quantum
Reconstruction Network - QuantumSSL
Metrics - Quantum
Self Supervised Config - Configuration for Quantum Self-Supervised Learning
- Quantum
Self Supervised Learner - Main Quantum Self-Supervised Learning Framework
- Quantum
Similarity Computer - Quantum
Sinkhorn Algorithm - Quantum
State - Quantum
State Evolution - Quantum
State Preparation - Quantum
Target Network - Representation
Evaluation Results - SSLLearning
Output - SSLLearning
Output Batch - SSLOptimizer
State - SSLTraining
Config - SSLTraining
Metrics - Training and evaluation structures
- Temperature
Controller
Enums§
- Contrastive
Loss Function - Decoder
Layer Type - Encoder
Layer Type - Entanglement
Measure - Entanglement
Pattern - Evolution
Strategy - Evolution
Type - Inverse
Operation Type - KeyGeneration
Method - LRScheduler
Type - Mask
Evolution Type - Mask
Generator Type - Mask
Strategy - Measurement
Basis - Measurement
Strategy - Momentum
Scheduling Strategy - Momentum
Type - Momentum
Update Strategy - Negative
Pair Strategy - Negative
Sampling Strategy - Noise
Type - Phase
Pattern - Positive
Pair Strategy - Prediction
Layer Type - Preparation
Method - Projection
Layer Type - Prototype
Evolution Strategy - Prototype
Update Strategy - Quantum
Activation - Quantum
Assignment Method - Quantum
Attention Type - Quantum
Augmentation Strategy - Quantum
Clustering Method - Quantum
Distance Metric - Quantum
Gate Type - Quantum
Inversion Method - Quantum
Masking Strategy - Quantum
Norm Type - Quantum
Pooling Type - Quantum
Prediction Strategy - QuantumSSL
Method - Quantum
Similarity Metric - Randomness
Source - Reconstruction
Layer Type - Reconstruction
Objective - Reconstruction
Strategy - Reconstruction
Target - Reconstruction
Type - Rotation
Axis - Target
Network Update - Temperature
Scheduling - Update
Rule - Upsampling
Mode