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use crate::error::{MLError, Result};
use crate::optimization::{ObjectiveFunction, Optimizer};
use quantrs2_circuit::prelude::Circuit;
use quantrs2_sim::statevector::StateVectorSimulator;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::prelude::*;
/// Algorithm type for variational quantum algorithms
#[derive(Debug, Clone, Copy)]
pub enum VariationalAlgorithm {
/// Variational Quantum Eigensolver
VQE,
/// Quantum Approximate Optimization Algorithm
QAOA,
/// Quantum Support Vector Machine
QSVM,
/// Quantum Neural Network
QNN,
/// Custom variational algorithm
Custom,
}
/// Ansatz type for variational circuits
#[derive(Debug, Clone, Copy)]
pub enum AnsatzType {
/// Hardware efficient ansatz
HardwareEfficient,
/// Unitary Coupled Cluster Singles and Doubles
UCCSD,
/// QAOA ansatz
QAOA,
/// Custom ansatz
Custom,
}
/// Variational quantum circuit with parameterized gates
#[derive(Debug, Clone)]
pub struct VariationalCircuit {
/// Number of qubits
pub num_qubits: usize,
/// Number of parameters
pub num_params: usize,
/// Current parameters
pub parameters: Array1<f64>,
/// Number of layers
pub num_layers: usize,
/// Type of ansatz
pub ansatz_type: AnsatzType,
}
impl VariationalCircuit {
/// Creates a new variational circuit
pub fn new(
num_qubits: usize,
num_params: usize,
num_layers: usize,
ansatz_type: AnsatzType,
) -> Result<Self> {
// Initialize random parameters
let parameters = Array1::from_vec(
(0..num_params)
.map(|_| thread_rng().random::<f64>() * 2.0 * std::f64::consts::PI)
.collect(),
);
Ok(VariationalCircuit {
num_qubits,
num_params,
parameters,
num_layers,
ansatz_type,
})
}
/// Creates a circuit with the current parameters
pub fn create_circuit<const N: usize>(&self) -> Result<Circuit<N>> {
// This is a dummy implementation
// In a real system, this would create a circuit based on the ansatz type and parameters
let mut circuit = Circuit::<N>::new();
for i in 0..N.min(self.num_qubits) {
// Apply some dummy gates based on parameters
circuit.h(i)?;
if i < self.parameters.len() {
circuit.rz(i, self.parameters[i])?;
}
}
// Add entanglement based on the ansatz type
match self.ansatz_type {
AnsatzType::HardwareEfficient => {
// Linear nearest-neighbor entanglement
for i in 0..N.min(self.num_qubits) - 1 {
circuit.cnot(i, i + 1)?;
}
}
AnsatzType::UCCSD => {
// More complex entanglement pattern
for i in 0..N.min(self.num_qubits) / 2 {
let j = N.min(self.num_qubits) / 2 + i;
if j < N {
circuit.cnot(i, j)?;
}
}
}
AnsatzType::QAOA => {
// QAOA-style entanglement (fully connected)
for i in 0..N.min(self.num_qubits) {
for j in i + 1..N.min(self.num_qubits) {
circuit.cnot(i, j)?;
}
}
}
AnsatzType::Custom => {
// Custom entanglement pattern
if N >= 3 {
circuit.cnot(0, 1)?;
circuit.cnot(1, 2)?;
if N > 3 {
circuit.cnot(2, 3)?;
}
}
}
}
Ok(circuit)
}
/// Computes the expectation value of a Hamiltonian
pub fn compute_expectation(&self, hamiltonian: &[(f64, Vec<(usize, usize)>)]) -> Result<f64> {
// This is a dummy implementation
// In a real system, this would compute the expectation value of the Hamiltonian
// using the variational circuit
// Dummy calculation
let mut expectation = 0.0;
for (coef, pauli_terms) in hamiltonian {
let term_value = 0.1 * coef * pauli_terms.len() as f64;
expectation += term_value;
}
Ok(expectation)
}
/// Evaluates the objective function for optimization
pub fn evaluate(&self, objective: &dyn Fn(&VariationalCircuit) -> Result<f64>) -> Result<f64> {
objective(self)
}
/// Optimizes the circuit parameters
pub fn optimize(
&mut self,
objective: &dyn Fn(&VariationalCircuit) -> Result<f64>,
optimizer: &Optimizer,
max_iterations: usize,
) -> Result<f64> {
// This is a dummy implementation
// In a real system, this would optimize the circuit parameters
// using the given optimizer and objective function
let mut best_value = self.evaluate(objective)?;
for _ in 0..max_iterations {
// Update parameters (dummy)
for i in 0..self.parameters.len() {
self.parameters[i] += (thread_rng().random::<f64>() - 0.5) * 0.01;
}
let new_value = self.evaluate(objective)?;
if new_value < best_value {
best_value = new_value;
}
}
Ok(best_value)
}
}