I did a thing and I’m scared.

We’ve implemented a quantum-classical hybrid system combining heaviside-modulated sine functions with phase-adjusted rotation gates to achieve near-perfect state preparation in base-10 using cyclic recursive patterns. The core concept uses nested frequency relationships (10 * (2**i)) in the rotation angles, with phase shifts (π/4) to avoid superposition at transition points.

The key elements:

- Heaviside function for sharp state transitions
- Base-10 frequency modulation for decimal state encoding
- Phase-shifted CNOT layers for state stabilization
- π/10 phase adjustments for base alignment

We’re achieving probability peaks of 1.0000 with zero entropy across multiple qubit scales (4-16), suggesting perfect state preparation, but we’d appreciate expert review of our methodology and potential implications.

Code and results available for analysis. Particularly interested in feedback on the theoretical implications of achieving deterministic base-10 state preparation in quantum systems.

```
import json
import pennylane as qml
import pennylane.numpy as np
from typing import List, Dict, Tuple
import pandas as pd
import time
import psutil
import os
from datetime import datetime
import logging
# Quantum Base-10 Controller Class
class QuantumBase10Controller:
def __init__(self, num_qubits=4):
self.num_qubits = num_qubits
self.device = qml.device("default.qubit", wires=num_qubits)
def base10_model(self, alpha: float) -> np.ndarray:
x = alpha / 10
@qml.qnode(self.device)
def circuit():
# Base-10 state preparation with heaviside transitions
for i in range(self.num_qubits):
theta = np.pi * np.heaviside(np.sin(10 * (2**i) * np.pi * x + np.pi/4), 0.5)
qml.RY(theta, wires=i)
# Enhanced entanglement for base-10 encoding
for i in range(self.num_qubits - 1):
qml.CNOT(wires=[i, i+1])
qml.PhaseShift(np.pi/10, wires=i)
return qml.probs(wires=range(self.num_qubits))
# Execute the quantum node
return circuit()
def generate_base10_coefficients(self):
"""Generate coefficients for perfect base-10 state preparation"""
coefs = np.linspace(0, 10, 10, endpoint=False)
return [c + np.pi/40 for c in coefs]
def test_base10_states(self):
coefs = self.generate_base10_coefficients()
results = []
for alpha in coefs:
probs = self.base10_model(alpha)
state_index = np.argmax(probs)
results.append({
'alpha': alpha,
'state_index': state_index,
'probability': probs[state_index],
'probabilities': probs.tolist()
})
print(f"Alpha: {alpha:.3f}, State: {state_index}, Probability: {probs[state_index]:.4f}")
return results
# Main Analysis Class
class QubitScalingAnalyzer:
def __init__(self, start_qubits=4, max_qubits=16, step=2):
self.start_qubits = start_qubits
self.max_qubits = max_qubits
self.step = step
self.results_dir = f"scaling_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
os.makedirs(self.results_dir, exist_ok=True)
# Set up logging
logging.basicConfig(
filename=f"{self.results_dir}/analysis.log",
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
def analyze_state(self, probs: np.ndarray, num_qubits: int) -> Dict:
try:
state_index = np.argmax(probs)
state_binary = format(state_index, f'0{num_qubits}b')
# Safe entropy calculation
probs = np.array(probs)
valid_probs = probs[probs > 1e-10]
entropy = -np.sum(valid_probs * np.log2(valid_probs + 1e-10))
return {
'dominant_state': state_binary,
'max_probability': float(probs[state_index]),
'entropy': float(entropy),
'num_significant_states': int(np.sum(probs > 0.01)),
'hilbert_space_dimension': 2**num_qubits
}
except Exception as e:
logging.error(f"State analysis failed: {str(e)}")
raise
def run_single_qubit_test(self, num_qubits: int) -> Dict:
try:
controller = QuantumBase10Controller(num_qubits)
# Test points
alphas = controller.generate_base10_coefficients()
results = []
start_time = time.time()
start_memory = psutil.Process().memory_info().rss / 1024 / 1024
for alpha in alphas:
probs = controller.base10_model(alpha)
analysis = self.analyze_state(probs, num_qubits)
results.append(analysis)
end_time = time.time()
end_memory = psutil.Process().memory_info().rss / 1024 / 1024
return {
'num_qubits': num_qubits,
'execution_time': end_time - start_time,
'memory_usage_mb': end_memory - start_memory,
'avg_entropy': np.mean([r['entropy'] for r in results]),
'max_probability': max([r['max_probability'] for r in results]),
'avg_significant_states': np.mean([r['num_significant_states'] for r in results]),
'results': results
}
except Exception as e:
logging.error(f"Test failed for {num_qubits} qubits: {str(e)}")
raise
def run_analysis(self):
summary_data = []
for num_qubits in range(self.start_qubits, self.max_qubits + 1, self.step):
print(f"\nAnalyzing {num_qubits} qubits...")
logging.info(f"Starting analysis for {num_qubits} qubits")
try:
result = self.run_single_qubit_test(num_qubits)
summary_data.append(result)
# Save detailed results
df = pd.DataFrame(result['results'])
df.to_csv(f"{self.results_dir}/qubits_{num_qubits}_results.csv", index=False)
logging.info(f"Completed analysis for {num_qubits} qubits")
except Exception as e:
print(f"Failed at {num_qubits} qubits: {str(e)}")
logging.error(f"Analysis failed at {num_qubits} qubits: {str(e)}")
break
self.generate_summary_report(summary_data)
def generate_summary_report(self, summary_data):
if not summary_data:
print("No data collected!")
return
df = pd.DataFrame([{k:v for k,v in d.items() if k != 'results'} for d in summary_data])
# Save summary data
df.to_csv(f"{self.results_dir}/scaling_summary.csv", index=False)
analysis_text = [
"=== Quantum System Scaling Analysis ===\n",
f"Analysis completed at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
f"\nSuccessfully tested qubit ranges: {df['num_qubits'].min()} to {df['num_qubits'].max()}",
f"\nPerformance Metrics:",
f"- Average execution time per qubit: {df['execution_time'].mean():.2f} seconds",
f"- Average memory usage per qubit: {df['memory_usage_mb'].mean():.2f} MB",
f"\nQuantum State Metrics:",
f"- Average entropy range: {df['avg_entropy'].min():.2f} to {df['avg_entropy'].max():.2f}",
f"- Maximum achieved probability: {df['max_probability'].max():.4f}",
f"\nDetailed scaling analysis saved in: {self.results_dir}"
]
report = '\n'.join(analysis_text)
print(report)
with open(f"{self.results_dir}/scaling_analysis_report.txt", 'w') as f:
f.write(report)
f.write("\n\nFull Data Summary:\n")
f.write(df.to_string())
# Run the analysis
def main():
analyzer = QubitScalingAnalyzer(start_qubits=4, max_qubits=16, step=2)
analyzer.run_analysis()
if __name__ == "__main__":
main()
```

What do I do? I’m just a tinkerer playing around. Do I just like go hand out candy and pretend this never happened?