pub fn create_data_encoding_circuit(
num_qubits: usize,
encoding_type: DataEncodingType,
) -> Result<DynamicCircuit>Expand description
Create quantum data encoding circuit
Examples found in repository?
examples/tensorflow_quantum_demo.rs (lines 211-214)
26fn main() -> Result<()> {
27 println!("=== TensorFlow Quantum Compatibility Demo ===\n");
28
29 // Step 1: Create TFQ-style quantum circuits
30 println!("1. Creating TensorFlow Quantum style circuits...");
31
32 let (circuits, circuit_symbols) = create_tfq_circuits()?;
33 println!(
34 " - Created {} parameterized quantum circuits",
35 circuits.len()
36 );
37 println!(" - Circuit symbols: {circuit_symbols:?}");
38
39 // Step 2: Build TFQ-style model with PQC layers
40 println!("\n2. Building TFQ-compatible model...");
41
42 let mut model = TFQModel::new(vec![4, 1]); // input_shape: [batch_size, features]
43
44 // Add quantum circuit layer (equivalent to tfq.layers.PQC)
45 // Note: QuantumCircuitLayer does not implement TFQLayer in current API
46 // model.add_layer(Box::new(QuantumCircuitLayer::new(
47 // circuits[0].clone(),
48 // circuit_symbols.clone(),
49 // Observable::PauliZ(vec![0]),
50 // Arc::new(StatevectorBackend::new(8))
51 // )));
52 println!(" - Quantum circuit layer placeholder added");
53
54 // Add classical preprocessing layer
55 // Note: TFQDenseLayer not implemented in current API
56 // model.add_layer(Box::new(TFQDenseLayer::new(
57 // 4, 8,
58 // ActivationFunction::ReLU,
59 // ParameterInitStrategy::XavierUniform
60 // )?));
61
62 // Add PQC layer with different observable
63 // Note: PQCLayer not implemented in current API
64 // model.add_layer(Box::new(PQCLayer::new(
65 // circuits[1].clone(),
66 // Observable::PauliZ(vec![1]),
67 // RegularizationType::L2(0.01)
68 // )?));
69
70 // Add quantum convolutional layer
71 // Note: QuantumConvolutionalLayer not implemented in current API
72 // model.add_layer(Box::new(QuantumConvolutionalLayer::new(
73 // circuits[2].clone(),
74 // (2, 2), // kernel_size
75 // PaddingType::Valid,
76 // 2 // stride
77 // )?));
78
79 // Final output layer
80 // Note: TFQDenseLayer not implemented in current API
81 // model.add_layer(Box::new(TFQDenseLayer::new(
82 // 8, 2,
83 // ActivationFunction::Softmax,
84 // ParameterInitStrategy::HeNormal
85 // )?));
86
87 println!(" Model architecture:");
88 // model.summary(); // Not implemented in current API
89
90 // Step 3: Create TFQ-style quantum dataset
91 println!("\n3. Creating TensorFlow Quantum dataset...");
92
93 let quantum_dataset = create_tfq_quantum_dataset()?;
94 // println!(" - Dataset size: {}", quantum_dataset.size());
95 // println!(" - Data encoding: {:?}", quantum_dataset.encoding_type());
96 // println!(" - Batch size: {}", quantum_dataset.batch_size());
97 println!(" - Quantum dataset created successfully");
98
99 // Step 4: Configure TFQ-style training
100 println!("\n4. Configuring TFQ training setup...");
101
102 let optimizer = TFQOptimizer::Adam {
103 learning_rate: 0.001,
104 beta1: 0.9,
105 beta2: 0.999,
106 epsilon: 1e-7,
107 };
108
109 let loss_function = TFQLossFunction::CategoricalCrossentropy;
110
111 model.compile()?;
112
113 println!(" - Optimizer: Adam");
114 println!(" - Loss: Sparse Categorical Crossentropy");
115 println!(" - Metrics: Accuracy, Precision, Recall");
116
117 // Step 5: Train with TFQ-style fit method
118 println!("\n5. Training with TensorFlow Quantum style...");
119
120 // Note: fit method not fully implemented in current API
121 // let history = model.fit(
122 // &quantum_dataset,
123 // 15, // epochs
124 // 0.2, // validation_split
125 // 1, // verbose
126 // vec![
127 // Box::new(EarlyStoppingCallback::new(3, "val_loss")), // patience, monitor
128 // Box::new(ReduceLROnPlateauCallback::new(0.5, 2)), // factor, patience
129 // ]
130 // )?;
131 println!(" Training setup configured (fit method placeholder)");
132
133 // println!(" Training completed!");
134 // println!(" - Final training accuracy: {:.3}", history.final_metric("accuracy"));
135 // println!(" - Final validation accuracy: {:.3}", history.final_metric("val_accuracy"));
136 // println!(" - Best epoch: {}", history.best_epoch());
137 println!(" Training placeholder completed");
138
139 // Step 6: Evaluate model performance
140 println!("\n6. Model evaluation...");
141
142 let test_dataset = create_tfq_test_dataset()?;
143 // let evaluation_results = model.evaluate(&test_dataset, 1)?; // verbose
144 //
145 // println!(" Test Results:");
146 // for (metric, value) in evaluation_results.iter() {
147 // println!(" - {}: {:.4}", metric, value);
148 // }
149 println!(" Test dataset created successfully");
150
151 // Step 7: Quantum circuit analysis
152 println!("\n7. Quantum circuit analysis...");
153
154 // let circuit_analysis = model.analyze_quantum_circuits()?;
155 // println!(" Circuit Properties:");
156 // println!(" - Total quantum parameters: {}", circuit_analysis.total_quantum_params);
157 // println!(" - Circuit depth: {}", circuit_analysis.max_circuit_depth);
158 // println!(" - Gate types used: {:?}", circuit_analysis.gate_types);
159 // println!(" - Entangling gates: {}", circuit_analysis.entangling_gate_count);
160 println!(" Circuit analysis placeholder completed");
161
162 // Step 8: Parameter shift gradients (TFQ-style)
163 println!("\n8. Computing parameter shift gradients...");
164
165 // let sample_input = quantum_dataset.get_batch(0)?;
166 // let gradients = model.compute_parameter_shift_gradients(&sample_input)?;
167 println!(" Parameter shift gradients placeholder");
168
169 // println!(" Gradient Analysis:");
170 // println!(" - Quantum gradients computed: {}", gradients.quantum_gradients.len());
171 // println!(" - Classical gradients computed: {}", gradients.classical_gradients.len());
172 // println!(" - Max quantum gradient: {:.6}",
173 // gradients.quantum_gradients.iter().fold(0.0f64, |a, &b| a.max(b.abs())));
174 // println!(" - Gradient variance: {:.6}",
175 // compute_gradient_variance(&gradients.quantum_gradients));
176 println!(" Gradient analysis placeholder completed");
177
178 // Step 9: Quantum expectation values
179 println!("\n9. Computing quantum expectation values...");
180
181 let observables = [Observable::PauliZ(vec![0]), Observable::PauliZ(vec![1])];
182
183 // let expectation_values = model.compute_expectation_values(&sample_input, &observables)?;
184 // println!(" Expectation Values:");
185 // for (i, (obs, val)) in observables.iter().zip(expectation_values.iter()).enumerate() {
186 // println!(" - Observable {}: {:.4}", i, val);
187 // }
188 println!(" Expectation values placeholder completed");
189
190 // Step 10: TFQ utils demonstrations
191 println!("\n10. TensorFlow Quantum utilities...");
192
193 // Circuit conversion
194 let dynamic_circuit = DynamicCircuit::from_circuit(circuits[0].clone())?;
195 let tfq_format_circuit = tfq_utils::circuit_to_tfq_format(&dynamic_circuit)?;
196 println!(" - Converted circuit to TFQ format (placeholder)");
197
198 // Batch circuit execution
199 // let batch_circuits = vec![circuits[0].clone(), circuits[1].clone()];
200 // let batch_params = Array2::from_shape_fn((2, 4), |(i, j)| (i + j) as f64 * 0.1);
201 // let batch_results = tfq_utils::batch_execute_circuits(&batch_circuits, &batch_params, &observables, &backend)?;
202 // println!(" - Batch execution results shape: {:?}", batch_results.dim());
203 println!(" - Batch execution placeholder completed");
204
205 // Data encoding utilities
206 let classical_data = Array2::from_shape_fn((10, 4), |(i, j)| (i + j) as f64 * 0.2);
207 // let encoded_circuits = tfq_utils::encode_data_to_circuits(
208 // &classical_data,
209 // DataEncodingType::Angle
210 // )?;
211 let encoded_circuits = [tfq_utils::create_data_encoding_circuit(
212 4,
213 DataEncodingType::Angle,
214 )?];
215 println!(
216 " - Encoded {} data points to quantum circuits",
217 encoded_circuits.len()
218 );
219
220 // Step 11: Compare with TensorFlow classical model
221 println!("\n11. Comparing with TensorFlow classical equivalent...");
222
223 create_tensorflow_classical_model()?;
224 // let classical_accuracy = train_classical_tensorflow_model(classical_model, &quantum_dataset)?;
225 //
226 // let quantum_accuracy = evaluation_results.get("accuracy").unwrap_or(&0.0);
227 // println!(" - Quantum TFQ model accuracy: {:.3}", quantum_accuracy);
228 // println!(" - Classical TF model accuracy: {:.3}", classical_accuracy);
229 // println!(" - Quantum advantage: {:.3}", quantum_accuracy - classical_accuracy);
230 println!(" - Classical comparison placeholder completed");
231
232 // Step 12: Model export (TFQ format)
233 println!("\n12. Exporting model in TFQ format...");
234
235 // model.save_tfq_format("quantum_model_tfq.pb")?;
236 // println!(" - Model exported to: quantum_model_tfq.pb");
237 //
238 // // Export to TensorFlow SavedModel format
239 // model.export_savedmodel("quantum_model_savedmodel/")?;
240 // println!(" - SavedModel exported to: quantum_model_savedmodel/");
241 println!(" - Model export placeholder completed");
242
243 // Step 13: Advanced TFQ features
244 println!("\n13. Advanced TensorFlow Quantum features...");
245
246 // Quantum data augmentation
247 // let augmented_dataset = quantum_dataset.augment_with_noise(0.05)?;
248 // println!(" - Created augmented dataset with noise level 0.05");
249 //
250 // // Circuit optimization for hardware
251 // let optimized_circuits = tfq_utils::optimize_circuits_for_hardware(
252 // &circuits,
253 // HardwareType::IonQ
254 // )?;
255 // println!(" - Optimized {} circuits for IonQ hardware", optimized_circuits.len());
256 //
257 // // Barren plateau analysis
258 // let plateau_analysis = analyze_barren_plateaus(&model, &quantum_dataset)?;
259 // println!(" - Barren plateau risk: {:.3}", plateau_analysis.risk_score);
260 // println!(" - Recommended mitigation: {}", plateau_analysis.mitigation_strategy);
261 println!(" - Advanced features placeholder completed");
262
263 println!("\n=== TensorFlow Quantum Demo Complete ===");
264
265 Ok(())
266}
267
268fn create_tfq_circuits() -> Result<(Vec<Circuit<8>>, Vec<String>)> {
269 let mut circuits = Vec::new();
270 let mut symbols = Vec::new();
271
272 // Circuit 1: Basic parameterized circuit
273 let mut circuit1 = CircuitBuilder::new();
274 circuit1.ry(0, 0.0)?;
275 circuit1.ry(1, 0.0)?;
276 circuit1.cnot(0, 1)?;
277 circuit1.ry(2, 0.0)?;
278 circuit1.cnot(1, 2)?;
279 circuits.push(circuit1.build());
280 symbols.extend(vec![
281 "theta_0".to_string(),
282 "theta_1".to_string(),
283 "theta_2".to_string(),
284 ]);
285
286 // Circuit 2: Entangling circuit
287 let mut circuit2 = CircuitBuilder::new();
288 circuit2.h(0)?;
289 circuit2.cnot(0, 1)?;
290 circuit2.cnot(1, 2)?;
291 circuit2.cnot(2, 3)?;
292 circuit2.ry(0, 0.0)?;
293 circuit2.ry(1, 0.0)?;
294 circuits.push(circuit2.build());
295 symbols.extend(vec!["phi_0".to_string(), "phi_1".to_string()]);
296
297 // Circuit 3: Convolutional-style circuit
298 let mut circuit3 = CircuitBuilder::new();
299 circuit3.ry(0, 0.0)?;
300 circuit3.ry(1, 0.0)?;
301 circuit3.cnot(0, 1)?;
302 circuits.push(circuit3.build());
303 symbols.extend(vec!["alpha_0".to_string(), "alpha_1".to_string()]);
304
305 Ok((circuits, symbols))
306}
307
308fn create_tfq_quantum_dataset() -> Result<QuantumDataset> {
309 let num_samples = 1000;
310 let num_features = 4;
311
312 // Create classical data
313 let classical_data = Array2::from_shape_fn((num_samples, num_features), |(i, j)| {
314 let noise = fastrand::f64() * 0.1;
315 (i as f64).mul_add(0.01, j as f64 * 0.1).sin() + noise
316 });
317
318 // Create labels (binary classification)
319 let labels = Array1::from_shape_fn(num_samples, |i| {
320 let sum = (0..num_features)
321 .map(|j| classical_data[[i, j]])
322 .sum::<f64>();
323 if sum > 0.0 {
324 1.0
325 } else {
326 0.0
327 }
328 });
329
330 // Create quantum circuits for the dataset
331 let circuits =
332 vec![tfq_utils::create_data_encoding_circuit(4, DataEncodingType::Angle)?; num_samples]
333 .into_iter()
334 .map(|dc| match dc {
335 DynamicCircuit::Circuit8(c) => c,
336 _ => panic!("Expected Circuit8"),
337 })
338 .collect();
339
340 // Create quantum dataset with angle encoding
341 QuantumDataset::new(
342 circuits,
343 classical_data,
344 labels,
345 32, // batch_size
346 )
347}
348
349fn create_tfq_test_dataset() -> Result<QuantumDataset> {
350 let num_samples = 200;
351 let num_features = 4;
352
353 let test_data = Array2::from_shape_fn((num_samples, num_features), |(i, j)| {
354 let noise = fastrand::f64() * 0.1;
355 (i as f64).mul_add(0.015, j as f64 * 0.12).sin() + noise
356 });
357
358 let test_labels = Array1::from_shape_fn(num_samples, |i| {
359 let sum = (0..num_features).map(|j| test_data[[i, j]]).sum::<f64>();
360 if sum > 0.0 {
361 1.0
362 } else {
363 0.0
364 }
365 });
366
367 // Create quantum circuits for the test dataset
368 let test_circuits =
369 vec![tfq_utils::create_data_encoding_circuit(4, DataEncodingType::Angle)?; num_samples]
370 .into_iter()
371 .map(|dc| match dc {
372 DynamicCircuit::Circuit8(c) => c,
373 _ => panic!("Expected Circuit8"),
374 })
375 .collect();
376
377 QuantumDataset::new(test_circuits, test_data, test_labels, 32)
378}