Design and implement quantum algorithm frameworks for specific problem domains. Structure quantum circuits, define hybrid classical-quantum pipelines, and create reusable quantum algorithm templates. Use when: (1) Designing new quantum algorithms for optimization or simulation, (2) Creating framework scaffolding for QAOA/VQE/QMC applications, (3) Structuring hybrid quantum-classical pipelines, (4) Implementing quantum subroutines for specific problem types.
Design and implement quantum algorithm frameworks for specific problem domains.
exec: Run Qiskit/PennyLane quantum circuit scriptsread: Load problem specifications and quantum library docswrite: Generate quantum algorithm framework code and documentationdef qaoa_framework(problem_hamiltonian, n_layers=3):
"""
QAOA framework structure:
1. Problem encoding: H_problem = QUBO/Ising
2. Mixer Hamiltonian: H_mixer = Σ X_i
3. Circuit: alternating problem/mixer unitaries
4. Optimization: minimize <ψ|H_problem|ψ>
"""
circuit = QuantumCircuit(n_qubits)
# Initial state: equal superposition
circuit.h(range(n_qubits))
# QAOA layers
for _ in range(n_layers):
apply_problem_unitary(circuit, gamma)
apply_mixer_unitary(circuit, beta)
return circuit
def vqe_framework(hamiltonian, ansatz_type="hardware_efficient"):
"""
VQE framework structure:
1. Ansatz: parameterized quantum circuit
2. Measurement: expectation value of H
3. Classical optimization: minimize energy
"""
ansatz = create_ansatz(ansatz_type, n_qubits, n_layers)
optimizer = SPSA(maxiter=300)
vqe = VQE(ansatz, optimizer, quantum_instance)
return vqe.compute_minimum_eigenvalue(hamiltonian)
Input Data → Classical Preprocessing → Quantum Encoding
→ Quantum Circuit Execution → Measurement → Classical Postprocessing → Output
Classify the target problem: combinatorial optimization (→ QAOA), eigenvalue problem (→ VQE), sampling (→ QMC), or simulation.
Choose appropriate framework and determine: qubit requirements, circuit depth, classical optimizer, error mitigation needs.
Define: encoding layer, variational/problem layers, measurement operators, and entanglement structure.
Generate Python code using Qiskit/PennyLane; include parameter initialization, circuit construction, and optimization loop.
Test on small instances with known solutions; benchmark vs classical baselines; report resource requirements.
User: "Design a QAOA framework for graph 3-coloring"
Agent:
1. Encode 3-coloring as QUBO: penalty for same-color adjacent nodes
2. Define QAOA circuit: problem unitary from QUBO, mixer from X gates
3. Implement 3-layer QAOA with COBYLA optimizer
4. Test on 5-node graph; report approximation ratio
User: "Create a VQE framework for H2 molecule ground state energy"
Agent:
1. Map H2 Hamiltonian to Pauli operators (Jordan-Wigner)
2. Design hardware-efficient ansatz (4 qubits, 2 layers)
3. Set up SPSA optimizer with 300 iterations
4. Run VQE and report ground state energy vs FCI reference
references/: Quantum algorithm implementation guidesquantum-portfolio-optimizer, quantum-neural-hybrid