quantrs2_ml/
variational.rs

1use crate::error::{MLError, Result};
2use crate::optimization::{ObjectiveFunction, Optimizer};
3use quantrs2_circuit::prelude::Circuit;
4use quantrs2_sim::statevector::StateVectorSimulator;
5use scirs2_core::ndarray::{Array1, Array2};
6use scirs2_core::random::prelude::*;
7
8/// Algorithm type for variational quantum algorithms
9#[derive(Debug, Clone, Copy)]
10pub enum VariationalAlgorithm {
11    /// Variational Quantum Eigensolver
12    VQE,
13
14    /// Quantum Approximate Optimization Algorithm
15    QAOA,
16
17    /// Quantum Support Vector Machine
18    QSVM,
19
20    /// Quantum Neural Network
21    QNN,
22
23    /// Custom variational algorithm
24    Custom,
25}
26
27/// Ansatz type for variational circuits
28#[derive(Debug, Clone, Copy)]
29pub enum AnsatzType {
30    /// Hardware efficient ansatz
31    HardwareEfficient,
32
33    /// Unitary Coupled Cluster Singles and Doubles
34    UCCSD,
35
36    /// QAOA ansatz
37    QAOA,
38
39    /// Custom ansatz
40    Custom,
41}
42
43/// Variational quantum circuit with parameterized gates
44#[derive(Debug, Clone)]
45pub struct VariationalCircuit {
46    /// Number of qubits
47    pub num_qubits: usize,
48
49    /// Number of parameters
50    pub num_params: usize,
51
52    /// Current parameters
53    pub parameters: Array1<f64>,
54
55    /// Number of layers
56    pub num_layers: usize,
57
58    /// Type of ansatz
59    pub ansatz_type: AnsatzType,
60}
61
62impl VariationalCircuit {
63    /// Creates a new variational circuit
64    pub fn new(
65        num_qubits: usize,
66        num_params: usize,
67        num_layers: usize,
68        ansatz_type: AnsatzType,
69    ) -> Result<Self> {
70        // Initialize random parameters
71        let parameters = Array1::from_vec(
72            (0..num_params)
73                .map(|_| thread_rng().gen::<f64>() * 2.0 * std::f64::consts::PI)
74                .collect(),
75        );
76
77        Ok(VariationalCircuit {
78            num_qubits,
79            num_params,
80            parameters,
81            num_layers,
82            ansatz_type,
83        })
84    }
85
86    /// Creates a circuit with the current parameters
87    pub fn create_circuit<const N: usize>(&self) -> Result<Circuit<N>> {
88        // This is a dummy implementation
89        // In a real system, this would create a circuit based on the ansatz type and parameters
90
91        let mut circuit = Circuit::<N>::new();
92
93        for i in 0..N.min(self.num_qubits) {
94            // Apply some dummy gates based on parameters
95            circuit.h(i)?;
96
97            if i < self.parameters.len() {
98                circuit.rz(i, self.parameters[i])?;
99            }
100        }
101
102        // Add entanglement based on the ansatz type
103        match self.ansatz_type {
104            AnsatzType::HardwareEfficient => {
105                // Linear nearest-neighbor entanglement
106                for i in 0..N.min(self.num_qubits) - 1 {
107                    circuit.cnot(i, i + 1)?;
108                }
109            }
110            AnsatzType::UCCSD => {
111                // More complex entanglement pattern
112                for i in 0..N.min(self.num_qubits) / 2 {
113                    let j = N.min(self.num_qubits) / 2 + i;
114                    if j < N {
115                        circuit.cnot(i, j)?;
116                    }
117                }
118            }
119            AnsatzType::QAOA => {
120                // QAOA-style entanglement (fully connected)
121                for i in 0..N.min(self.num_qubits) {
122                    for j in i + 1..N.min(self.num_qubits) {
123                        circuit.cnot(i, j)?;
124                    }
125                }
126            }
127            AnsatzType::Custom => {
128                // Custom entanglement pattern
129                if N >= 3 {
130                    circuit.cnot(0, 1)?;
131                    circuit.cnot(1, 2)?;
132                    if N > 3 {
133                        circuit.cnot(2, 3)?;
134                    }
135                }
136            }
137        }
138
139        Ok(circuit)
140    }
141
142    /// Computes the expectation value of a Hamiltonian
143    pub fn compute_expectation(&self, hamiltonian: &[(f64, Vec<(usize, usize)>)]) -> Result<f64> {
144        // This is a dummy implementation
145        // In a real system, this would compute the expectation value of the Hamiltonian
146        // using the variational circuit
147
148        // Dummy calculation
149        let mut expectation = 0.0;
150
151        for (coef, pauli_terms) in hamiltonian {
152            let term_value = 0.1 * coef * pauli_terms.len() as f64;
153            expectation += term_value;
154        }
155
156        Ok(expectation)
157    }
158
159    /// Evaluates the objective function for optimization
160    pub fn evaluate(&self, objective: &dyn Fn(&VariationalCircuit) -> Result<f64>) -> Result<f64> {
161        objective(self)
162    }
163
164    /// Optimizes the circuit parameters
165    pub fn optimize(
166        &mut self,
167        objective: &dyn Fn(&VariationalCircuit) -> Result<f64>,
168        optimizer: &Optimizer,
169        max_iterations: usize,
170    ) -> Result<f64> {
171        // This is a dummy implementation
172        // In a real system, this would optimize the circuit parameters
173        // using the given optimizer and objective function
174
175        let mut best_value = self.evaluate(objective)?;
176
177        for _ in 0..max_iterations {
178            // Update parameters (dummy)
179            for i in 0..self.parameters.len() {
180                self.parameters[i] += (thread_rng().gen::<f64>() - 0.5) * 0.01;
181            }
182
183            let new_value = self.evaluate(objective)?;
184
185            if new_value < best_value {
186                best_value = new_value;
187            }
188        }
189
190        Ok(best_value)
191    }
192}
quantrs2_ml/variational.rs

quantrs2_ml/
variational.rs
