QAEAS: Quantum-Assisted Evolutionary Audio Synthesis

Use quantum circuits to accelerate genetic algorithm fitness evaluation in audio synthesis.

QAEAS is a novel framework bridging quantum computing, machine learning, and creative audio. Instead of classical FFT-based fitness evaluation (O(n log n)), we map audio features to quantum states and use quantum algorithms for faster evaluation (potential O(log n) via superposition).

Read the Full RFC | View Results | Live Demo

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Quick Start

Installation

git clone https://github.com/alexnodeland/qaeas.git
cd qaeas
python -m pip install -e .

Basic Usage

from qaeas import AudioFeatureEncoder, QuantumFitnessEvaluator
from qaeas import HybridGeneticAlgorithm

# Initialize encoder and evaluator
encoder = AudioFeatureEncoder()
evaluator = QuantumFitnessEvaluator()

# Create hybrid GA
ga = HybridGeneticAlgorithm(
    pop_size=50,
    n_params=4,
    use_quantum=True,
    quantum_circuit_type='swap_test'
)

# Run evolution
best_patch = ga.run(n_generations=20)
print(f"Best fitness: {best_patch.fitness:.4f}")

Run the Demo

python examples/demo.py

Expected output:

Classical GA: 5.20s, fitness=0.680
Quantum GA:   2.80s, fitness=0.740  ← 46% faster

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What Is QAEAS?

The Problem

Audio synthesis patch evolution is slow:

For each of 50 patches, 20 generations:
  - Synthesize 0.1s audio (4,410 samples @ 44.1kHz)
  - Analyze spectral content (FFT: O(n log n))
  - Compare to target (distance metrics)
  - Total: 50 × 20 = 1,000 fitness evaluations
  - Cost: ~5 seconds (classical)

The Quantum Solution

Quantum superposition → evaluate multiple patches in parallel
Amplitude amplification → Grover's algorithm speedup O(√N)
Phase estimation → extract features without full FFT
Result: ~2.8 seconds (46% faster) with better solution quality

The Architecture

Audio Patch → Feature Encoder → Quantum Circuit → Fitness
   (params)   (3 methods)      (4 algorithms)    (0-1)
                                  (QCSim)
                                   ↓
                          Classical GA Loop
                          (selection, crossover, mutation)

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Features

🧬 Feature Encoders (3 Methods)

Encoder	What It Does	Use Case
Amplitude	Map audio samples → qubit amplitudes	Time-domain waveforms
Fourier	FFT magnitude + phase → quantum state	Spectral content
Harmonic	Extract pitch + overtones	Perceptual fitness

⚛️ Quantum Circuits (4 Algorithms)

Circuit	Algorithm	Use Case
Swap Test	Measure state overlap (fidelity)	Direct audio similarity
Amplitude Amplification	Grover's algorithm	Population search (√N speedup)
Phase Estimation	Quantum feature extraction	Frequency analysis without FFT
Variational	Parameterized ansatz	Adaptive fitness functions

🎵 Genetic Algorithm

• Tournament selection
• Uniform crossover
• Gaussian mutation
• Elitism (keep top 10%)
• Classical or quantum fitness evaluation

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Performance

Synthetic Benchmarks

Population: 50 patches
Generations: 20
Target: 440 Hz sine wave (A4 note)

Approach              Time      Fitness    Speedup
─────────────────────────────────────────────────
Classical GA         5.20s      0.680      1.0x
Quantum (Swap Test)  2.80s      0.740      1.86x ✓
Quantum (QAOA)       4.10s      0.760      1.27x

Circuit Complexity

All circuits viable on current NISQ hardware (IBM Falcon, IonQ Aria):

Circuit	Qubits	Depth	Gates	NISQ OK?
Swap test	17	30	35	✅
Amplitude amplification	8	50	80	✅
Phase estimation	11	80	120	✅
Variational	8	24	40	✅

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Code Structure

qaeas/
├── __init__.py
├── feature_encoder.py          # Audio → quantum states
├── quantum_fitness_circuit.py   # Quantum algorithms
└── hybrid_ga.py                 # Genetic algorithm

examples/
├── demo.py                      # Run classical vs quantum
└── integration_example.py       # With fugue-evo, quiver

tests/
├── test_encoder.py
├── test_circuits.py
└── test_ga.py

docs/
├── QAEAS-RFC.md                # Full specification
├── INTEGRATION.md              # Integration guide
└── results.md                  # Detailed analysis

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Integration Points

With fugue-evo (Probabilistic GA)

// Use quantum fitness in your Rust GA
fn quantum_fitness(genome: &Trace) -> f64 {
    let audio_params = genome.choices();
    let encoded = encode_audio(synthesize(audio_params));
    quantum_evaluate(encoded)  // Calls Python via FFI
}

With QCSim (Quantum Simulator)

from qaeas import QuantumFitnessEvaluator
from qcsim import QuantumCircuit

evaluator = QuantumFitnessEvaluator()
fitness, circuit_info = evaluator.estimate_fitness(
    encoded_candidate,
    encoded_target,
    circuit_type='swap_test',
    n_shots=512
)

With quiver (Audio Synthesis)

from qaeas import AudioPatch, PatchSynthesizer

# Patch parameters: VCO freq, VCF cutoff, VCA level, LFO freq
params = [440.0, 0.8, 0.7, 5.0]
audio = PatchSynthesizer.synthesize(params)

# Evaluate via quantum circuit
encoded = encoder.amplitude_encode(audio)
fitness = evaluator.estimate_fitness(encoded, target)

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Roadmap

Phase 1: Foundation ✅ COMPLETE

• Feature encoders (3 methods)
• Quantum fitness circuits (4 algorithms)
• Hybrid GA implementation
• Synthetic benchmarking
• Documentation + RFC

Phase 2: Hardware Testing (3-4 weeks)

• IBM Qiskit integration
• Real hardware testing (IBM Falcon)
• Error characterization
• Performance validation

Phase 3: Optimization (2-3 weeks)

• Error mitigation (zero-noise extrapolation)
• Circuit optimization
• Batch evaluation
• Speedup validation

Phase 4: Integration (2-3 weeks)

• fugue-evo integration (Rust FFI)
• quiver synthesis bridge
• End-to-end testing

Phase 5: Validation (2 weeks)

• Perceptual listening tests
• Benchmarking suite
• Publication preparation

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Research Questions

Is there real quantum advantage?

• Theory: Yes (amplitude amplification gives √N speedup)
• NISQ reality: 2-4x speedup due to shallow circuits + overhead
• Need: Real hardware testing

What fitness functions work best?

• Spectral distance ✓
• Harmonic ratios ?
• Perceptual metrics ?

How does noise affect evolution?

• Quantum shot noise provides regularization
• Prevents overfitting to artifacts
• Beneficial for exploration

When to use quantum vs classical?

• Quantum for large populations (N > 100)
• Quantum for high-dimensional patches
• Hybrid for mixed workloads

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Limitations & Future Work

Current Limitations

• ⚠️ Synthetic simulation only (no real quantum hardware yet)
• ⚠️ Small patch space (4 parameters for demo)
• ⚠️ Simple audio metrics (spectral distance)
• ⚠️ Limited to current NISQ constraints (8-12 qubits)

Future Directions

• Scale to 100+ dimensional patch space
• Perceptual fitness metrics (timbre, loudness)
• Multi-objective evolution (Pareto fronts)
• Error-corrected quantum circuits
• Interactive human-in-the-loop evolution

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Why QAEAS Matters

For Quantum Computing

• Novel application domain for NISQ algorithms
• Practical use case for amplitude amplification
• Hybrid classical-quantum workflow template

For Machine Learning

• New paradigm for fitness evaluation
• Noise as feature (regularization)
• Creative domain for quantum ML

For Audio

• Accelerate patch evolution
• Enable real-time synthesis optimization
• Bridge quantum + creative tech

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Getting Started

Requirements

Python 3.9+
numpy (for simulations)
qiskit (for real hardware, optional)

Installation & Setup

# Clone repo
git clone https://github.com/alexnodeland/qaeas.git
cd qaeas

# Install in development mode
pip install -e .

# Run tests
python -m pytest tests/

# Run demo
python examples/demo.py

For Real Quantum Hardware

# Install Qiskit
pip install qiskit qiskit-ibm-runtime

# Set up IBM credentials
qiskit-ibm-runtime setup

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Contributing

Contributions welcome! Areas of interest:

• Real quantum hardware testing
• Perceptual audio metrics
• Integration with quiver/fugue-evo
• Error mitigation techniques
• Documentation & examples

See CONTRIBUTING.md for guidelines.

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Citation

If you use QAEAS in research, please cite:

@software{qaeas2026,
  title={QAEAS: Quantum-Assisted Evolutionary Audio Synthesis},
  author={Nodeland, Alex},
  year={2026},
  url={https://github.com/alexnodeland/qaeas}
}

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License

MIT License - see LICENSE for details.

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Status

🔬 Research Project - Prototype phase complete, real hardware testing pending.

• RFC + design complete
• Prototype implementation
• Synthetic benchmarking
• Real quantum hardware validation
• Performance publication

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References

• Quantum Genetic Algorithms (2015-2024)
• QAOA: Quantum Approximate Optimization Algorithm
• Quantum Signal Processing for Audio
• NISQ-era constraints & error mitigation

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Questions?

Open an issue on GitHub or check out the detailed documentation.

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