Metadata-Version: 2.2
Name: quantum-metalearn
Version: 1.1.1
Summary: A project for meta-learning experiments
Author: Krishna Bajpai
Author-email: bajpaikrishna715@gmail.com
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0.0
Requires-Dist: gymnasium>=0.28.0
Requires-Dist: numpy>=1.23.0
Requires-Dist: tqdm>=4.65.0
Requires-Dist: click>=8.1.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: license
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Krishna Bajpai Meta-Learn

[![CI/CD](https://github.com/yourorg/Krishna Bajpai-metalearn/actions/workflows/ci.yml/badge.svg)](https://github.com/yourorg/Krishna Bajpai-metalearn/actions)
[![License](https://img.shields.io/badge/License-QuantumML-brightgreen)](LICENSE)
[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)

A revolutionary meta-learning framework combining quantum-inspired optimization, neuromorphic computing, and evolutionary task dynamics for unparalleled adaptive AI capabilities.

## Features

- 🌀 **Quantum-Informed Meta-Optimization**  
- 🧠 **Neuromorphic Architecture** with spiking neural dynamics  
- 🌌 **4D Hypernetwork** parameter generation  
- 🧬 **Evolutionary Task Environments** with genetic programming  
- ⚡ **Hybrid Quantum-Classical** computation support

## Quick Start

```python
from Krishna Bajpai import QuantumMetaLearner, NeuromorphicTransformer

# Initialize quantum-inspired meta-learner
model = NeuromorphicTransformer(input_dim=256)
learner = QuantumMetaLearner(model)

# Evolve tasks with genetic programming
tasks = evolve_task_population(base_tasks)

# Meta-train with hybrid optimization
learner.hybrid_train(tasks, qpu_backend='ionq_harmony')
