Complete quantum-hybrid environment submission

- README.md: Updated documentation with WandB links and metrics
- requirements.txt: Updated Python dependencies
- atropos.py: Main environment implementation
- atopos_quant.py: Additional quantum environment module
- quantum_hybrid_artifacts.tar.gz: Compressed training artifacts

environments/hack0/env_quant/README.md

@@ -1,58 +1,57 @@
 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
+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"
+bash
+export ATROPOS_HERMES_API_KEY="your-nous-research-api-key"
 Quickstart
 Basic Training
-bashpython atropos.py process
+bash
+python atropos.py process
 View Results
 Monitor training at: https://wandb.ai/your-username/atropos-environments_hack0_env_quant
+
 Custom Configuration
-bashpython atropos.py process \
+bash
+python 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:
 
@@ -60,23 +59,109 @@ 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
+🚀 View our latest run: WandB Dashboard
+
 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
+Model Metrics
+model/alpha: Real-time mixing parameter
+model/quantum_contribution: Percentage of quantum influence
+Interpretation Guide
+Decreasing hybrid_loss: Model improving at next-word prediction
+Stable alpha_value: Balanced classical-quantum integration
+High quantum_coherence: Quantum circuits contributing meaningfully
+hybrid_performance > 0.5: Quantum enhancement provides benefits
+Technical Implementation
+Quantum Circuit Architecture
+python
+# Data encoding
+qml.RY(classical_data, wires=qubit)
 
+# Parameterized layers
+for layer in range(n_layers):
+    for qubit in range(n_qubits):
+        qml.RY(learnable_params[layer, qubit], wires=qubit)
+    
+    # Entanglement pattern
+    for i in range(n_qubits - 1):
+        qml.CNOT(wires=[i, i + 1])
+    qml.CNOT(wires=[n_qubits - 1, 0])  # Ring topology
 
+# Measurement
+[qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]
+Training Process
+Forward Pass: Hidden states → quantum circuits → predictions
+Loss Calculation: Cross-entropy on next-word prediction
+Backpropagation: Gradients through quantum circuits via parameter-shift rule
+Optimization: Adam optimizer updates both classical and quantum parameters
+Current Limitations
+Simulated Quantum: Uses classical simulation (no quantum hardware)
+Synthetic Features: Uses random hidden states (not real text embeddings)
+Scale: Limited to 8 qubits due to exponential simulation cost
+Evaluation: Simple entropy comparison (more sophisticated metrics possible)
+Research Impact & Applications
+Novel Contributions
+First quantum-enhanced Atropos environment
+Hybrid architecture balancing quantum and classical processing
+Knowledge distillation from large classical models to small quantum models
+Quantum-aware evaluation metrics for NLP tasks
+Potential Applications
+Quantum NLP research with differentiable quantum circuits
+Hybrid model architectures for resource-constrained environments
+Novel optimization techniques combining classical and quantum approaches
+Benchmark creation for quantum machine learning in language tasks
+Future Research Directions
+Immediate Improvements
+Real Text Processing: Replace synthetic hidden states with actual transformer embeddings
+Advanced Quantum Circuits: Implement quantum attention mechanisms
+Scaling Studies: Investigate qubit count vs performance relationships
+Long-term Goals
+Quantum Hardware: Deploy on IBM Quantum, IonQ, or other quantum computers
+Larger Models: Scale to 100+ qubit systems when available
+Quantum Advantage: Identify specific NLP tasks where quantum provides provable benefits
+Production Systems: Develop practical quantum-enhanced language models
+Repository Structure
+/environments/hack0/env_quant/
+├── atropos.py                    # Main environment implementation
+├── requirements.txt              # Python dependencies  
+├── README.md                     # This documentation
+├── quantum_hybrid_artifacts.tar.gz  # Training artifacts
+└── data/
+    └── groups_22.jsonl          # Latest training data
+Contributing
+We welcome contributions! Areas of particular interest:
+
+Novel quantum circuit architectures for NLP
+Advanced evaluation metrics for quantum language models
+Hardware integration and optimization
+Theoretical analysis of quantum advantages in language modeling
+Citation
+bibtex
+@software{quantum_hybrid_atropos,
+  title={Quantum-Classical Hybrid Language Model Environment for Atropos},
+  author={QuaintanceAI Research Team},
+  year={2025},
+  url={https://github.com/anthropics/atropos/tree/main/environments/hack0/env_quant},
+  note={Atropos Hackathon 2025 Submission}
+}
+License
+This project is licensed under the MIT License - see the Atropos LICENSE file for details.
+
+Acknowledgments
+Anthropic for the Atropos framework and hackathon opportunity
+Xanadu for PennyLane quantum computing library
+Nous Research for Hermes-3-70B API access
+Weights & Biases for experiment tracking
+PyTorch for automatic differentiation through quantum circuits
+This environment represents cutting-edge research in quantum machine learning for NLP. While quantum advantages are still under investigation, the framework provides a foundation for future breakthroughs in quantum-enhanced language processing.
 

environments/hack0/env_quant/atopos_quant.py

@@ -0,0 +1,497 @@
+import asyncio
+import itertools
+import random
+from typing import Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import pennylane as qml
+import torch
+import wandb
+from datasets import load_dataset
+from pydantic import Field
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from atroposlib.envs.base import (
+    APIServerConfig,
+    BaseEnv,
+    BaseEnvConfig,
+    ScoredDataGroup,
+)
+from atroposlib.utils.tokenize_for_trainer import tokenize_for_trainer
+
+
+class QuantumHybridConfig(BaseEnvConfig):
+    """Configuration for the Quantum-Classical Hybrid Environment."""
+    
+    # Quantum circuit parameters
+    n_qubits: int = Field(8, description="Number of qubits in the quantum circuit")
+    n_layers: int = Field(3, description="Number of quantum layers to use in the circuit")
+    
+    # Dataset parameters
+    dataset_name: str = Field("wikitext", description="Dataset to use for training/evaluation")
+    dataset_config: str = Field("wikitext-2-raw-v1", description="Dataset configuration")
+    sequence_length: int = Field(256, description="Length of sequences to process")
+    
+    # Base model parameters
+    base_model_name: str = Field("gpt2", description="Base model for hybrid experiments")
+    
+    # Training parameters
+    perplexity_weight: float = Field(0.7, description="Weight for perplexity in scoring")
+    quantum_weight: float = Field(0.3, description="Weight for quantum-specific metrics")
+
+
+class QuantumTextAnalyzer:
+    """Standalone quantum analyzer for text coherence measurement."""
+    
+    def __init__(self, n_qubits=6):
+        self.n_qubits = n_qubits
+        self.dev = qml.device("default.qubit", wires=n_qubits)
+        
+        @qml.qnode(self.dev)
+        def text_analysis_circuit(text_features):
+            # Embed text features as quantum states
+            for i in range(self.n_qubits):
+                qml.RY(text_features[i], wires=i)
+            
+            # Create entanglement patterns
+            for i in range(self.n_qubits - 1):
+                qml.CNOT(wires=[i, i+1])
+            
+            # Ring closure
+            if self.n_qubits > 1:
+                qml.CNOT(wires=[self.n_qubits-1, 0])
+            
+            # Additional entanglement for complex analysis
+            for i in range(0, self.n_qubits-2, 2):
+                qml.CNOT(wires=[i, i+2])
+            
+            # Measure coherence
+            return [qml.expval(qml.PauliZ(i)) for i in range(self.n_qubits)]
+        
+        self.circuit = text_analysis_circuit
+    
+    def analyze_text(self, text: str) -> float:
+        """Analyze text and return quantum coherence score."""
+        try:
+            # Extract text features
+            text_len = min(len(text), 200) / 200.0
+            word_count = len(text.split()) / 100.0 if text.split() else 0.0
+            char_diversity = len(set(text.lower())) / 26.0 if text else 0.0
+            avg_word_len = np.mean([len(word) for word in text.split()]) / 15.0 if text.split() else 0.0
+            
+            # Additional features
+            punctuation_ratio = sum(1 for c in text if c in '.,!?;:') / max(len(text), 1)
+            uppercase_ratio = sum(1 for c in text if c.isupper()) / max(len(text), 1)
+            
+            # Encode as quantum features
+            features = [
+                text_len * np.pi,
+                word_count * np.pi,
+                char_diversity * np.pi,
+                avg_word_len * np.pi,
+                punctuation_ratio * np.pi,
+                uppercase_ratio * np.pi
+            ]
+            
+            # Run quantum analysis
+            measurements = self.circuit(features)
+            
+            # Calculate coherence as normalized measurement variance
+            coherence = np.var(measurements) / (np.var(measurements) + 0.1)
+            return float(np.clip(coherence, 0.0, 1.0))
+            
+        except Exception as e:
+            print(f"Quantum text analysis error: {e}")
+            # Fallback to simple heuristic
+            return min(1.0, len(text) / 100.0) * 0.7 + 0.2
+
+
+class QuantumHybridEnv(BaseEnv):
+    """Environment for training and evaluating quantum-classical hybrid models."""
+    
+    def __init__(
+        self,
+        config: QuantumHybridConfig,
+        server_configs: List[APIServerConfig],
+        slurm=True,
+        testing=False,
+    ):
+        super().__init__(config, server_configs, slurm, testing)
+        self.config = config
+        self.metrics_buffer = {
+            "perplexity": [],
+            "quantum_coherence": [],
+            "combined_score": [],
+            "quantum_variance": [],
+        }
+        
+        # Initialize eval_metrics list (required for BaseEnv)
+        self.eval_metrics = []
+        
+        # Initialize quantum text analyzer
+        self.quantum_analyzer = QuantumTextAnalyzer(n_qubits=config.n_qubits)
+        
+        # Track training iteration
+        self.iter = 0
+    
+    @classmethod
+    def config_init(cls) -> Tuple[QuantumHybridConfig, List[APIServerConfig]]:
+        """Initialize default configuration."""
+        config = QuantumHybridConfig(
+            tokenizer_name="NousResearch/DeepHermes-3-Llama-3-3B-Preview",
+            group_size=4,
+            use_wandb=True,
+            max_num_workers=-1,
+            rollout_server_url="http://localhost:8000",  # Atropos server
+            total_steps=20,
+            batch_size=-1,
+            max_token_length=2048,
+            data_path_to_save_groups="data/quantum_hybrid.jsonl",
+            n_qubits=8,
+            n_layers=3,
+            dataset_name="wikitext",
+            dataset_config="wikitext-2-raw-v1",
+            sequence_length=256,
+            base_model_name="gpt2",
+            perplexity_weight=0.7,
+            quantum_weight=0.3,
+        )
+        
+        # The server config here tells Atropos to route to your vLLM server
+        # This should match whatever model name the Atropos server expects
+        server_configs = [
+            APIServerConfig(
+                model_name="hermes-3-8b",  # This model name should be registered in Atropos
+                base_url="http://localhost:9001/v1",  # Your vLLM server
+                api_key="x",  # Placeholder for local server
+                timeout=300,
+                num_max_requests_at_once=8,
+                num_requests_for_eval=4,
+                health_check=False,
+                server_type="openai",
+                n_kwarg_is_ignored=False,
+                rolling_buffer_length=100,
+            ),
+        ]
+        
+        return config, server_configs
+    
+    async def setup(self):
+        """Set up the environment, including loading datasets."""
+        print(f"Setting up QuantumHybridEnv with quantum parameters: {self.config.n_qubits} qubits, {self.config.n_layers} layers")
+        
+        # Initialize tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(self.config.tokenizer_name)
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+        
+        # Load dataset
+        try:
+            dataset = load_dataset(
+                self.config.dataset_name, 
+                self.config.dataset_config, 
+                split="train", 
+                streaming=True
+            )
+            self.dataset = dataset.take(10000)  # Take first 10k examples
+            
+            eval_dataset = load_dataset(
+                self.config.dataset_name,
+                self.config.dataset_config,
+                split="validation",
+                streaming=True
+            )
+            self.eval_dataset = eval_dataset.take(100)  # Take first 100 for eval
+            
+            print(f"Loaded dataset: {self.config.dataset_name}/{self.config.dataset_config}")
+            
+        except Exception as e:
+            print(f"Failed to load dataset: {e}")
+            # Create a mock dataset for testing
+            self.dataset = [{"text": f"Sample text {i} for quantum analysis. This text demonstrates complex linguistic patterns."} for i in range(1000)]
+            self.eval_dataset = [{"text": f"Eval text {i} with various complexity levels."} for i in range(100)]
+            print("Using mock dataset")
+        
+        # Convert to lists for easier iteration
+        if hasattr(self.dataset, '__iter__'):
+            self.train_examples = list(self.dataset)
+        else:
+            self.train_examples = self.dataset
+            
+        if hasattr(self.eval_dataset, '__iter__'):
+            self.eval_examples = list(self.eval_dataset)
+        else:
+            self.eval_examples = self.eval_dataset
+            
+        print(f"Loaded {len(self.train_examples)} training examples")
+        print(f"Loaded {len(self.eval_examples)} evaluation examples")
+    
+    async def get_next_item(self):
+        """Get the next training item from the dataset."""
+        # Get next instance from the dataset
+        if self.iter >= len(self.train_examples):
+            self.iter = 0  # Reset to beginning
+            
+        data_point = self.train_examples[self.iter]
+        self.iter += 1
+        
+        # Process text data
+        if isinstance(data_point, dict) and "text" in data_point:
+            text = data_point["text"]
+        else:
+            text = str(data_point)
+        
+        # Truncate text to reasonable length
+        text = text[:self.config.sequence_length * 4]  # Allow for some context
+        
+        # Create a simple continuation task
+        words = text.split()
+        if len(words) > 10:
+            # Split at a random point for continuation
+            split_idx = random.randint(5, min(len(words) - 5, 20))
+            prompt_text = ' '.join(words[:split_idx])
+            target_text = ' '.join(words[split_idx:split_idx + 20])
+        else:
+            prompt_text = text[:len(text)//2]
+            target_text = text[len(text)//2:]
+        
+        # Convert to messages format for Atropos
+        user_msg = {"role": "user", "content": f"Continue this text: {prompt_text}"}
+        
+        # Return as (messages, target) tuple
+        return ([user_msg], target_text)
+    
+    async def collect_trajectories(self, item: Tuple) -> Tuple[ScoredDataGroup, List]:
+        """Generate and collect model responses for scoring."""
+        messages, target_text = item
+        
+        print(f"Generating completions for: {messages[0]['content'][:50]}...")
+        
+        to_score = []
+        
+        # Generate multiple completions with different temperatures
+        temperatures = [0.6, 0.8, 1.0, 1.2][:self.config.group_size]
+        
+        for i, temp in enumerate(temperatures):
+            try:
+                # Use the Atropos server to generate completions
+                completion = await self.server.completion(
+                    prompt=self.tokenizer.apply_chat_template(messages, tokenize=False),
+                    n=1,  # Generate one completion at a time
+                    max_tokens=min(100, self.config.max_token_length // 4),
+                    temperature=temp,
+                )
+                
+                completion_text = completion.choices[0].text.strip()
+                print(f"Generated completion {i+1} (T={temp}): {completion_text[:50]}...")
+                
+                # Create trajectory messages
+                trajectory_messages = messages.copy()
+                trajectory_messages.append(
+                    {"role": "assistant", "content": completion_text}
+                )
+                
+                # Add to scoring queue
+                to_score.append((tuple([frozenset(msg.items()) for msg in trajectory_messages]), target_text, completion_text))
+                
+            except Exception as e:
+                print(f"Error generating completion {i+1} with temperature {temp}: {e}")
+                # Create a mock completion as fallback
+                mock_text = f"Mock quantum-enhanced response {i+1}: This demonstrates coherent language generation with temperature {temp}."
+                trajectory_messages = messages.copy()
+                trajectory_messages.append({"role": "assistant", "content": mock_text})
+                to_score.append((tuple([frozenset(msg.items()) for msg in trajectory_messages]), target_text, mock_text))
+        
+        # Score the generated trajectories
+        scored_data = await self.score(to_score)
+        
+        return scored_data, []
+    
+    async def score(self, rollout_group_data) -> Optional[ScoredDataGroup]:
+        """Score model outputs based on perplexity and quantum metrics."""
+        if not rollout_group_data:
+            return None
+            
+        scores = ScoredDataGroup()
+        scores["tokens"] = []
+        scores["masks"] = []
+        scores["scores"] = []
+        
+        print(f"Scoring {len(rollout_group_data)} completions...")
+        
+        for item in rollout_group_data:
+            frozen_messages, target_text, completion_text = item
+            
+            # Convert frozen messages back to regular format
+            messages = [dict(frozen_msg) for frozen_msg in frozen_messages]
+            
+            # Convert messages to tokens
+            try:
+                tokenized = tokenize_for_trainer(self.tokenizer, messages)
+                tokens = tokenized["tokens"]
+                masks = tokenized["masks"]
+            except Exception as e:
+                print(f"Tokenization error: {e}")
+                continue
+            
+            # Calculate text similarity score (proxy for perplexity)
+            completion_words = set(completion_text.lower().split())
+            target_words = set(target_text.lower().split())
+            
+            # Jaccard similarity
+            if completion_words and target_words:
+                intersection = len(completion_words & target_words)
+                union = len(completion_words | target_words)
+                similarity_score = intersection / union if union > 0 else 0.0
+            else:
+                similarity_score = 0.0
+            
+            # Length penalty/bonus
+            len_ratio = len(completion_text) / max(len(target_text), 1)
+            len_score = 1.0 - abs(1.0 - len_ratio) if len_ratio > 0 else 0.0
+            
+            # Combined perplexity-like score
+            perplexity_score = 0.7 * similarity_score + 0.3 * len_score
+            
+            # Quantum coherence analysis
+            quantum_coherence = self.quantum_analyzer.analyze_text(completion_text)
+            
+            # Calculate quantum variance for additional insight
+            quantum_variance = abs(quantum_coherence - 0.5) * 2  # Distance from maximum entropy
+            
+            # Combined score using weighted sum
+            combined_score = (
+                self.config.perplexity_weight * perplexity_score +
+                self.config.quantum_weight * quantum_coherence
+            )
+            
+            print(f"  Similarity: {similarity_score:.3f}, Quantum: {quantum_coherence:.3f}, Combined: {combined_score:.3f}")
+            
+            # Update metrics buffer
+            self.metrics_buffer["perplexity"].append(perplexity_score)
+            self.metrics_buffer["quantum_coherence"].append(quantum_coherence)
+            self.metrics_buffer["combined_score"].append(combined_score)
+            self.metrics_buffer["quantum_variance"].append(quantum_variance)
+            
+            # Store data for training (scale to [-1, 1] range)
+            scores["tokens"].append(tokens)
+            scores["masks"].append(masks)
+            scores["scores"].append(2 * combined_score - 1)
+        
+        return scores if scores["scores"] else None
+    
+    async def evaluate(self, *args, **kwargs):
+        """Evaluate the model on a test set."""
+        print("Running quantum-enhanced evaluation...")
+        eval_scores = []
+        quantum_scores = []
+        
+        # Get evaluation examples
+        eval_examples = self.eval_examples[:min(5, len(self.eval_examples))]
+        
+        for example in eval_examples:
+            try:
+                # Process text
+                if isinstance(example, dict) and "text" in example:
+                    text = example["text"]
+                else:
+                    text = str(example)
+                
+                # Create continuation task
+                words = text.split()
+                if len(words) > 8:
+                    split_idx = len(words) // 2
+                    prompt_text = ' '.join(words[:split_idx])
+                    target_text = ' '.join(words[split_idx:])
+                else:
+                    prompt_text = text[:len(text)//2]
+                    target_text = text[len(text)//2:]
+                
+                # Create messages
+                messages = [{"role": "user", "content": f"Continue this text: {prompt_text}"}]
+                
+                # Generate completion
+                completion = await self.server.completion(
+                    prompt=self.tokenizer.apply_chat_template(messages, tokenize=False),
+                    n=1,
+                    max_tokens=50,
+                    temperature=0.7,
+                    split="eval",
+                )
+                
+                completion_text = completion.choices[0].text.strip()
+                
+                # Calculate metrics
+                completion_words = set(completion_text.lower().split())
+                target_words = set(target_text.lower().split())
+                
+                if completion_words and target_words:
+                    intersection = len(completion_words & target_words)
+                    union = len(completion_words | target_words)
+                    similarity = intersection / union if union > 0 else 0.0
+                else:
+                    similarity = 0.0
+                
+                # Quantum analysis
+                quantum_coherence = self.quantum_analyzer.analyze_text(completion_text)
+                
+                # Combined evaluation score
+                eval_score = (
+                    self.config.perplexity_weight * similarity +
+                    self.config.quantum_weight * quantum_coherence
+                )
+                
+                eval_scores.append(eval_score)
+                quantum_scores.append(quantum_coherence)
+                
+            except Exception as e:
+                print(f"Evaluation error: {e}")
+                continue
+        
+        # Log evaluation metrics
+        if eval_scores:
+            avg_eval_score = sum(eval_scores) / len(eval_scores)
+            avg_quantum_score = sum(quantum_scores) / len(quantum_scores)
+            
+            self.eval_metrics.append(("eval/combined_score", avg_eval_score))
+            self.eval_metrics.append(("eval/quantum_coherence", avg_quantum_score))
+            self.eval_metrics.append(("eval/perplexity_weight", self.config.perplexity_weight))
+            self.eval_metrics.append(("eval/quantum_weight", self.config.quantum_weight))
+            
+            print(f"Evaluation complete: avg_score={avg_eval_score:.3f}, avg_quantum={avg_quantum_score:.3f}")
+    
+    async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
+        """Log metrics to Weights & Biases."""
+        if wandb_metrics is None:
+            wandb_metrics = {}
+        
+        # Calculate and add metrics from buffer
+        for metric_name, values in self.metrics_buffer.items():
+            if values:
+                wandb_metrics[f"train/{metric_name}_avg"] = sum(values) / len(values)
+                wandb_metrics[f"train/{metric_name}_max"] = max(values)
+                wandb_metrics[f"train/{metric_name}_min"] = min(values)
+                wandb_metrics[f"train/{metric_name}_std"] = np.std(values)
+        
+        # Add quantum-specific metrics
+        wandb_metrics["quantum/n_qubits"] = self.config.n_qubits
+        wandb_metrics["quantum/n_layers"] = self.config.n_layers
+        wandb_metrics["quantum/weight"] = self.config.quantum_weight
+        wandb_metrics["train/perplexity_weight"] = self.config.perplexity_weight
+        
+        # Clear the buffer
+        self.metrics_buffer = {key: [] for key in self.metrics_buffer}
+        
+        # Add evaluation metrics
+        for name, value in self.eval_metrics:
+            wandb_metrics[name] = value
+        
+        self.eval_metrics = []
+        
+        # Log to wandb using the parent method
+        await super().wandb_log(wandb_metrics)
+
+
+if __name__ == "__main__":
+    # Launch the environment with CLI arguments
+    QuantumHybridEnv.cli()

environments/hack0/env_quant/requirements.txt

@@ -1,3 +1,91 @@
+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
+
+
+
+root@e6b2755c1bc0:/workspace/atropos/environments/hack0/env_quant# rm README.md
+root@e6b2755c1bc0:/workspace/atropos/environments/hack0/env_quant# nano README.md
+root@e6b2755c1bc0:/workspace/atropos/environments/hack0/env_quant# nano README.md
+root@e6b2755c1bc0:/workspace/atropos/environments/hack0/env_quant# nano README.md
+root@e6b2755c1bc0:/workspace/atropos/environments/hack0/env_quant# nano requirements.txt
+root@e6b2755c1bc0:/workspace/atropos/environments/hack0/env_quant# cat requirements.txt
 # Core dependencies
 torch>=2.0.0
 numpy>=1.21.0