quantrs2_ml/
variational.rs

1use crate::error::{MLError, Result};
2use crate::optimization::{ObjectiveFunction, Optimizer};
3use ndarray::{Array1, Array2};
4use quantrs2_circuit::prelude::Circuit;
5use quantrs2_sim::statevector::StateVectorSimulator;
6
7/// Algorithm type for variational quantum algorithms
8#[derive(Debug, Clone, Copy)]
9pub enum VariationalAlgorithm {
10    /// Variational Quantum Eigensolver
11    VQE,
12
13    /// Quantum Approximate Optimization Algorithm
14    QAOA,
15
16    /// Quantum Support Vector Machine
17    QSVM,
18
19    /// Quantum Neural Network
20    QNN,
21
22    /// Custom variational algorithm
23    Custom,
24}
25
26/// Ansatz type for variational circuits
27#[derive(Debug, Clone, Copy)]
28pub enum AnsatzType {
29    /// Hardware efficient ansatz
30    HardwareEfficient,
31
32    /// Unitary Coupled Cluster Singles and Doubles
33    UCCSD,
34
35    /// QAOA ansatz
36    QAOA,
37
38    /// Custom ansatz
39    Custom,
40}
41
42/// Variational quantum circuit with parameterized gates
43#[derive(Debug, Clone)]
44pub struct VariationalCircuit {
45    /// Number of qubits
46    pub num_qubits: usize,
47
48    /// Number of parameters
49    pub num_params: usize,
50
51    /// Current parameters
52    pub parameters: Array1<f64>,
53
54    /// Number of layers
55    pub num_layers: usize,
56
57    /// Type of ansatz
58    pub ansatz_type: AnsatzType,
59}
60
61impl VariationalCircuit {
62    /// Creates a new variational circuit
63    pub fn new(
64        num_qubits: usize,
65        num_params: usize,
66        num_layers: usize,
67        ansatz_type: AnsatzType,
68    ) -> Result<Self> {
69        // Initialize random parameters
70        let parameters = Array1::from_vec(
71            (0..num_params)
72                .map(|_| rand::random::<f64>() * 2.0 * std::f64::consts::PI)
73                .collect(),
74        );
75
76        Ok(VariationalCircuit {
77            num_qubits,
78            num_params,
79            parameters,
80            num_layers,
81            ansatz_type,
82        })
83    }
84
85    /// Creates a circuit with the current parameters
86    pub fn create_circuit<const N: usize>(&self) -> Result<Circuit<N>> {
87        // This is a dummy implementation
88        // In a real system, this would create a circuit based on the ansatz type and parameters
89
90        let mut circuit = Circuit::<N>::new();
91
92        for i in 0..N.min(self.num_qubits) {
93            // Apply some dummy gates based on parameters
94            circuit.h(i)?;
95
96            if i < self.parameters.len() {
97                circuit.rz(i, self.parameters[i])?;
98            }
99        }
100
101        // Add entanglement based on the ansatz type
102        match self.ansatz_type {
103            AnsatzType::HardwareEfficient => {
104                // Linear nearest-neighbor entanglement
105                for i in 0..N.min(self.num_qubits) - 1 {
106                    circuit.cnot(i, i + 1)?;
107                }
108            }
109            AnsatzType::UCCSD => {
110                // More complex entanglement pattern
111                for i in 0..N.min(self.num_qubits) / 2 {
112                    let j = N.min(self.num_qubits) / 2 + i;
113                    if j < N {
114                        circuit.cnot(i, j)?;
115                    }
116                }
117            }
118            AnsatzType::QAOA => {
119                // QAOA-style entanglement (fully connected)
120                for i in 0..N.min(self.num_qubits) {
121                    for j in i + 1..N.min(self.num_qubits) {
122                        circuit.cnot(i, j)?;
123                    }
124                }
125            }
126            AnsatzType::Custom => {
127                // Custom entanglement pattern
128                if N >= 3 {
129                    circuit.cnot(0, 1)?;
130                    circuit.cnot(1, 2)?;
131                    if N > 3 {
132                        circuit.cnot(2, 3)?;
133                    }
134                }
135            }
136        }
137
138        Ok(circuit)
139    }
140
141    /// Computes the expectation value of a Hamiltonian
142    pub fn compute_expectation(&self, hamiltonian: &[(f64, Vec<(usize, usize)>)]) -> Result<f64> {
143        // This is a dummy implementation
144        // In a real system, this would compute the expectation value of the Hamiltonian
145        // using the variational circuit
146
147        // Dummy calculation
148        let mut expectation = 0.0;
149
150        for (coef, pauli_terms) in hamiltonian {
151            let term_value = 0.1 * coef * pauli_terms.len() as f64;
152            expectation += term_value;
153        }
154
155        Ok(expectation)
156    }
157
158    /// Evaluates the objective function for optimization
159    pub fn evaluate(&self, objective: &dyn Fn(&VariationalCircuit) -> Result<f64>) -> Result<f64> {
160        objective(self)
161    }
162
163    /// Optimizes the circuit parameters
164    pub fn optimize(
165        &mut self,
166        objective: &dyn Fn(&VariationalCircuit) -> Result<f64>,
167        optimizer: &Optimizer,
168        max_iterations: usize,
169    ) -> Result<f64> {
170        // This is a dummy implementation
171        // In a real system, this would optimize the circuit parameters
172        // using the given optimizer and objective function
173
174        let mut best_value = self.evaluate(objective)?;
175
176        for _ in 0..max_iterations {
177            // Update parameters (dummy)
178            for i in 0..self.parameters.len() {
179                self.parameters[i] += (rand::random::<f64>() - 0.5) * 0.01;
180            }
181
182            let new_value = self.evaluate(objective)?;
183
184            if new_value < best_value {
185                best_value = new_value;
186            }
187        }
188
189        Ok(best_value)
190    }
191}
quantrs2_ml/variational.rs

quantrs2_ml/
variational.rs
