Module quantum_classical 

Quantum-Classical Hybrid Optimization

This module provides classical simulation of quantum optimization algorithms:

• QAOA (Quantum Approximate Optimization Algorithm) for combinatorial problems
• VQE (Variational Quantum Eigensolver) for ground-state energy estimation
• Tensor network methods (MPS/DMRG) for quantum many-body systems

Overview

The module implements exact statevector simulation of quantum circuits, enabling benchmarking of quantum optimization protocols on small problem instances (up to ~20 qubits on classical hardware).

Example: MaxCut with QAOA

use scirs2_optimize::quantum_classical::qaoa::{MaxCutProblem, QaoaConfig, QaoaCircuit};

// Triangle graph: edges (0,1), (1,2), (0,2)
let problem = MaxCutProblem::new(3, vec![(0, 1, 1.0), (1, 2, 1.0), (0, 2, 1.0)]);
let config = QaoaConfig::default();
let circuit = QaoaCircuit::new(problem, config);
let result = circuit.optimize().expect("QAOA should converge");
println!("Expected cut value: {:.4}", result.optimal_value);

Re-exports

pub use qaoa::MaxCutProblem;

pub use qaoa::QaoaCircuit;

pub use qaoa::QaoaConfig;

pub use qaoa::QaoaResult;

pub use statevector::Statevector;

pub use tensor_network::ising_1d_mpo;

pub use tensor_network::MPS;

pub use vqe::HardwareEfficientAnsatz;

pub use vqe::PauliHamiltonian;

pub use vqe::PauliOp;

pub use vqe::VqeOptimizer;

pub use vqe::VqeResult;

Modules

qaoa

QAOA (Quantum Approximate Optimization Algorithm) for combinatorial optimization

statevector

N-qubit statevector simulator

tensor_network

Tensor network methods: MPS, MPO and DMRG-lite

vqe

VQE (Variational Quantum Eigensolver) for ground-state energy estimation

Structs

QcConfig

Configuration for quantum-classical optimizers

QcOptResult

Result of a quantum-classical optimization run

Traits

QuantumClassicalOptimizer

Trait for quantum-classical hybrid optimizers

Type Aliases

QcResult

Result type for quantum-classical optimization operations