quantum-framework-agnostic-design

name: quantum-framework-agnostic-design description: Design framework-agnostic quantum machine learning (QML) systems that eliminate vendor lock-in. Use when building QML solutions that need to work across multiple quantum computing platforms (IBM Quantum, Amazon Braket, Azure Quantum, IonQ, Rigetti), or when designing quantum neural networks for cross-framework compatibility. Covers unified computational graphs, hardware abstraction layers, and multi-framework export strategies. Activation: framework-agnostic QML, quantum vendor lock-in, QML interoperability, cross-platform quantum ML, quantum neural network portability, framework-independent quantum computing.

Quantum Framework-Agnostic Design

Design QML systems that work seamlessly across quantum computing frameworks and hardware backends.

Core Architecture Components

From arXiv:2604.04414 - Framework-agnostic quantum neural network architecture:

1. Unified Computational Graph

Abstract quantum operations into vendor-independent graph:

# Unified quantum operation representation
class QuantumOp:
    name: str          # Operation type (CNOT, H, RX, etc.)
    qubits: List[int]  # Target qubits
    params: Dict       # Gate parameters
    # Framework-specific implementations injected at runtime

Key pattern: Define quantum circuit once, execute everywhere.

2. Hardware Abstraction Layer (HAL)

Single API for multiple backends:

HAL interface:

abstract class QuantumHAL:
    def execute(circuit: QuantumCircuit) -> Results
    def get_backend_info() -> BackendInfo
    def estimate_cost(circuit) -> CostEstimate

3. Multi-framework Export Pipeline

Lossless circuit translation via ONNX metadata:

• Export from: Qiskit, Cirq, PennyLane, Braket
• ONNX format with quantum metadata extensions
• Import to: Any supported framework

4. Pluggable Encoding Strategies

Three core encoding methods compatible with all backends:

Quick Start

Design a Framework-Agnostic QNN

1. Define circuit in unified graph
2. Select encoding strategy
3. Choose target backends via HAL
4. Export to desired framework

# Example: Iris classification QNN
from framework_agnostic import UnifiedQNN, HAL, Encoding

# Define circuit (framework-independent)
qnn = UnifiedQNN(
    num_qubits=4,
    encoding=Encoding.ANGLE,
    layers=3
)

# Select backends
hal = HAL(['ibm_quantum', 'amazon_braket', 'ionq'])

# Export to specific framework
qiskit_circuit = qnn.export('qiskit')
pennylane_circuit = qnn.export('pennylane')

Workflow: Portability-First QML Design

Step 1: Define Unified Architecture

Before picking a framework:

• Specify quantum operations abstractly
• Choose encoding compatible with all targets
• Design classical co-processor interface

Step 2: Configure HAL

Select target backends based on:

• Available hardware (gate-based vs annealing)
• Cost constraints
• Performance requirements

Step 3: Export & Deploy

Generate framework-specific code:

• Qiskit for IBM Quantum
• PennyLane for gradient-based training
• Cirq for Google ecosystem
• Braket for AWS deployment

Step 4: Train & Execute

Run in native framework, compare results:

• Verify identical classification accuracy
• Check training time parity (target: <10% overhead)
• Validate cross-framework reproducibility

Key Design Patterns

Pattern 1: Vendor-Neutral Circuit Definition

Define gates without framework-specific syntax:

# Instead of qiskit-specific:
# circuit.h(0); circuit.cx(0, 1)

# Use unified representation:
ops = [
    QuantumOp('H', [0]),
    QuantumOp('CNOT', [0, 1]),
    QuantumOp('RX', [0], {'theta': 0.5})
]

Pattern 2: Backend-Aware Cost Estimation

Before execution, estimate cost across backends:

for backend in hal.available_backends():
    cost = hal.estimate_cost(circuit, backend)
    time = hal.estimate_runtime(circuit, backend)
    # Choose optimal backend

Pattern 3: Classical Co-Processor Integration

QML works with classical frameworks (TensorFlow, PyTorch, JAX):

# Hybrid quantum-classical model
model = HybridModel(
    classical_nn=nn.Sequential(...),  # PyTorch
    quantum_layer=UnifiedQNN(...),    # Framework-agnostic
    framework='pytorch'               # Target classical framework
)

Benchmarks (arXiv:2604.04414)

Performance parity across frameworks:

Key result: Identical classification accuracy across frameworks.

Best Practices

1. Design first, framework second: Define circuit before picking implementation
2. Use ONNX for translation: Standard metadata format prevents information loss
3. Test multiple backends: Verify results match across frameworks
4. Monitor overhead: Target <10% overhead from abstraction layer
5. Choose encoding wisely: Encoding affects all backends equally

Common Pitfalls

• Vendor-specific syntax: Locks you into one framework
• Non-portable encoding: Some encoding methods work only in specific frameworks
• Ignoring cost: Different backends have vastly different pricing
• Backend capabilities mismatch: Gate-based circuit on annealing hardware fails

Resources

references/

See references/encoding_strategies.md for detailed encoding implementations and references/hal_interface.md for HAL API specification.

scripts/

See scripts/export_circuit.py for circuit translation utility.

Related Skills: quantum-machine-learning, quantum-circuit-spectral-analysis, variational-quantum-algorithms