Complete quantum-hybrid environment submission

environments/hack0/env_quant/README.md

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+Quantum-Classical Hybrid Language Model Environment
+A novel Atropos environment that trains quantum-enhanced language models by combining classical transformers with quantum circuits using PennyLane and PyTorch.
+Overview
+This environment implements a quantum-classical hybrid architecture for next-word prediction, trained on high-quality text generated by Hermes-3-70B. The key innovation is using quantum circuits to enhance traditional neural networks for language modeling tasks.
+Research Question
+Can quantum circuits provide advantages over purely classical approaches in natural language processing tasks?
+Architecture
+Data Flow
+Input Prompts → Hermes-3-70B (text generation) → Hybrid Model Training → Quantum-Enhanced Predictions
+Hybrid Model Components
+
+Classical Pathway: Standard transformer-style neural network head
+Quantum Pathway:
+
+Dimensionality reduction: 768D → 8D (quantum space)
+Two quantum circuit layers with parameterized gates
+Quantum-to-vocabulary mapping: 8D → 50K vocab
+
+
+Learnable Mixing: Parameter α balances classical vs quantum contributions
+
+Quantum Circuit Design
+
+8 qubits with 3 parameterized layers
+RY rotation gates for classical data encoding
+CNOT gates creating entanglement patterns
+Pauli-Z measurements for classical output extraction
+
+Installation & Setup
+Prerequisites
+bash# Install dependencies
+pip install -r requirements.txt
+
+# Atropos framework (follow official guide)
+Environment Setup
+bashexport ATROPOS_HERMES_API_KEY="your-nous-research-api-key"
+Quickstart
+Basic Training
+bashpython atropos.py process
+View Results
+Monitor training at: https://wandb.ai/your-username/atropos-environments_hack0_env_quant
+Custom Configuration
+bashpython atropos.py process \
+  --env.n_qubits 16 \
+  --env.n_layers 5 \
+  --env.total_steps 100 \
+  --env.quantum_weight 0.5
+Environment Design & Motivation
+Why Quantum-Classical Hybrid?
+
+Pattern Recognition: Quantum circuits may capture linguistic patterns that classical networks miss
+Entanglement: Natural language has complex interdependencies that quantum entanglement might model better
+Optimization Landscape: Quantum interference could provide novel optimization pathways
+Knowledge Distillation: Transfer capabilities from large models (Hermes-3-70B) to smaller quantum-enhanced models
+
+Training Strategy
+The environment employs quantum-enhanced knowledge distillation:
+
+Teacher Model: Hermes-3-70B generates diverse, high-quality responses
+Student Model: Hybrid quantum-classical model learns next-word prediction
+Comparison: Direct evaluation of quantum vs classical pathways within the same model
+Optimization: Both classical and quantum parameters trained via gradient descent
+
+Results & Metrics
+Live Experiment
+🚀 View our latest run: WandB Dashboard: https://wandb.ai/quaintanceai-nous/atropos-environments_hack0_env_quant?nw=nwuserquaintanceai
+Key Metrics Explained
+Training Metrics
+
+train/hybrid_loss: Combined quantum-classical model loss
+train/classical_loss: Baseline classical-only model loss
+train/quantum_loss: Quantum-specific loss component
+train/alpha_value: Mixing parameter (0 = full quantum, 1 = full classical)
+
+Evaluation Metrics
+
+eval/hybrid_performance: Entropy-based comparison of hybrid vs classical predictions
+eval/quantum_weight: Current quantum contribution (1 - α)
+train/quantum_coherence: Measure of quantum circuit effectiveness
+
+
+