quantrs2_circuit/
tensor_network.rs

1//! Tensor network compression for quantum circuits
2//!
3//! This module provides tensor network representations of quantum circuits
4//! for efficient simulation and optimization.
5
6use crate::builder::Circuit;
7use crate::dag::{circuit_to_dag, CircuitDag, DagNode};
8// SciRS2 POLICY compliant - using scirs2_core::Complex64
9use quantrs2_core::{
10    error::{QuantRS2Error, QuantRS2Result},
11    gate::GateOp,
12    qubit::QubitId,
13};
14use scirs2_core::Complex64;
15use std::collections::{HashMap, HashSet};
16use std::f64::consts::PI;
17
18/// Complex number type
19type C64 = Complex64;
20
21/// Tensor representing a quantum gate or state
22#[derive(Debug, Clone)]
23pub struct Tensor {
24    /// Tensor data in row-major order
25    pub data: Vec<C64>,
26    /// Shape of the tensor (dimensions)
27    pub shape: Vec<usize>,
28    /// Labels for each index
29    pub indices: Vec<String>,
30}
31
32impl Tensor {
33    /// Create a new tensor
34    #[must_use]
35    pub fn new(data: Vec<C64>, shape: Vec<usize>, indices: Vec<String>) -> Self {
36        assert_eq!(shape.len(), indices.len());
37        let total_size: usize = shape.iter().product();
38        assert_eq!(data.len(), total_size);
39
40        Self {
41            data,
42            shape,
43            indices,
44        }
45    }
46
47    /// Create an identity tensor
48    #[must_use]
49    pub fn identity(dim: usize, in_label: String, out_label: String) -> Self {
50        let mut data = vec![C64::new(0.0, 0.0); dim * dim];
51        for i in 0..dim {
52            data[i * dim + i] = C64::new(1.0, 0.0);
53        }
54
55        Self::new(data, vec![dim, dim], vec![in_label, out_label])
56    }
57
58    /// Get the rank (number of indices)
59    #[must_use]
60    pub fn rank(&self) -> usize {
61        self.shape.len()
62    }
63
64    /// Get the total number of elements
65    #[must_use]
66    pub fn size(&self) -> usize {
67        self.data.len()
68    }
69
70    /// Contract two tensors along specified indices
71    pub fn contract(&self, other: &Self, self_idx: &str, other_idx: &str) -> QuantRS2Result<Self> {
72        // Find index positions
73        let self_pos = self
74            .indices
75            .iter()
76            .position(|s| s == self_idx)
77            .ok_or_else(|| QuantRS2Error::InvalidInput(format!("Index {self_idx} not found")))?;
78        let other_pos = other
79            .indices
80            .iter()
81            .position(|s| s == other_idx)
82            .ok_or_else(|| QuantRS2Error::InvalidInput(format!("Index {other_idx} not found")))?;
83
84        // Check dimensions match
85        if self.shape[self_pos] != other.shape[other_pos] {
86            return Err(QuantRS2Error::InvalidInput(format!(
87                "Dimension mismatch: {} vs {}",
88                self.shape[self_pos], other.shape[other_pos]
89            )));
90        }
91
92        // Compute new shape and indices
93        let mut new_shape = Vec::new();
94        let mut new_indices = Vec::new();
95
96        for (i, (dim, idx)) in self.shape.iter().zip(&self.indices).enumerate() {
97            if i != self_pos {
98                new_shape.push(*dim);
99                new_indices.push(idx.clone());
100            }
101        }
102
103        for (i, (dim, idx)) in other.shape.iter().zip(&other.indices).enumerate() {
104            if i != other_pos {
105                new_shape.push(*dim);
106                new_indices.push(idx.clone());
107            }
108        }
109
110        // Perform contraction (simplified implementation)
111        let new_size: usize = new_shape.iter().product();
112        let mut new_data = vec![C64::new(0.0, 0.0); new_size];
113
114        // This is a simplified contraction - in practice, would use optimized tensor libraries
115        let contract_dim = self.shape[self_pos];
116
117        // For now, return a placeholder
118        Ok(Self::new(new_data, new_shape, new_indices))
119    }
120
121    /// Reshape the tensor
122    pub fn reshape(&mut self, new_shape: Vec<usize>) -> QuantRS2Result<()> {
123        let new_size: usize = new_shape.iter().product();
124        if new_size != self.size() {
125            return Err(QuantRS2Error::InvalidInput(format!(
126                "Cannot reshape {} elements to shape {:?}",
127                self.size(),
128                new_shape
129            )));
130        }
131
132        self.shape = new_shape;
133        Ok(())
134    }
135}
136
137/// Tensor network representation of a quantum circuit
138#[derive(Debug)]
139pub struct TensorNetwork {
140    /// Tensors in the network
141    tensors: Vec<Tensor>,
142    /// Connections between tensors (`tensor_idx1`, idx1, `tensor_idx2`, idx2)
143    bonds: Vec<(usize, String, usize, String)>,
144    /// Open indices (external legs)
145    open_indices: HashMap<String, (usize, usize)>, // index -> (tensor_idx, position)
146}
147
148impl Default for TensorNetwork {
149    fn default() -> Self {
150        Self::new()
151    }
152}
153
154impl TensorNetwork {
155    /// Create a new empty tensor network
156    #[must_use]
157    pub fn new() -> Self {
158        Self {
159            tensors: Vec::new(),
160            bonds: Vec::new(),
161            open_indices: HashMap::new(),
162        }
163    }
164
165    /// Add a tensor to the network
166    pub fn add_tensor(&mut self, tensor: Tensor) -> usize {
167        let idx = self.tensors.len();
168
169        // Track open indices
170        for (pos, index) in tensor.indices.iter().enumerate() {
171            self.open_indices.insert(index.clone(), (idx, pos));
172        }
173
174        self.tensors.push(tensor);
175        idx
176    }
177
178    /// Connect two tensor indices
179    pub fn add_bond(
180        &mut self,
181        t1: usize,
182        idx1: String,
183        t2: usize,
184        idx2: String,
185    ) -> QuantRS2Result<()> {
186        if t1 >= self.tensors.len() || t2 >= self.tensors.len() {
187            return Err(QuantRS2Error::InvalidInput(
188                "Tensor index out of range".to_string(),
189            ));
190        }
191
192        // Remove from open indices
193        self.open_indices.remove(&idx1);
194        self.open_indices.remove(&idx2);
195
196        self.bonds.push((t1, idx1, t2, idx2));
197        Ok(())
198    }
199
200    /// Contract the entire network to a single tensor
201    pub fn contract_all(&self) -> QuantRS2Result<Tensor> {
202        if self.tensors.is_empty() {
203            return Err(QuantRS2Error::InvalidInput(
204                "Empty tensor network".to_string(),
205            ));
206        }
207
208        // Simple contraction order: left to right
209        // In practice, would use optimal contraction ordering
210        let mut result = self.tensors[0].clone();
211
212        for bond in &self.bonds {
213            let (t1, idx1, t2, idx2) = bond;
214            if *t1 == 0 {
215                result = result.contract(&self.tensors[*t2], idx1, idx2)?;
216            }
217        }
218
219        Ok(result)
220    }
221
222    /// Apply SVD-based compression
223    pub const fn compress(&mut self, max_bond_dim: usize, tolerance: f64) -> QuantRS2Result<()> {
224        // Placeholder for SVD-based compression
225        // Would implement MPS/MPO compression techniques
226        Ok(())
227    }
228}
229
230/// Convert a quantum circuit to tensor network representation
231pub struct CircuitToTensorNetwork<const N: usize> {
232    /// Maximum bond dimension for compression
233    max_bond_dim: Option<usize>,
234    /// Truncation tolerance
235    tolerance: f64,
236}
237
238impl<const N: usize> Default for CircuitToTensorNetwork<N> {
239    fn default() -> Self {
240        Self::new()
241    }
242}
243
244impl<const N: usize> CircuitToTensorNetwork<N> {
245    /// Create a new converter
246    #[must_use]
247    pub const fn new() -> Self {
248        Self {
249            max_bond_dim: None,
250            tolerance: 1e-10,
251        }
252    }
253
254    /// Set maximum bond dimension
255    #[must_use]
256    pub const fn with_max_bond_dim(mut self, dim: usize) -> Self {
257        self.max_bond_dim = Some(dim);
258        self
259    }
260
261    /// Set truncation tolerance
262    #[must_use]
263    pub const fn with_tolerance(mut self, tol: f64) -> Self {
264        self.tolerance = tol;
265        self
266    }
267
268    /// Convert circuit to tensor network
269    pub fn convert(&self, circuit: &Circuit<N>) -> QuantRS2Result<TensorNetwork> {
270        let mut tn = TensorNetwork::new();
271        let mut qubit_wires: HashMap<usize, String> = HashMap::new();
272
273        // Initialize qubit wires
274        for i in 0..N {
275            qubit_wires.insert(i, format!("q{i}_in"));
276        }
277
278        // Convert each gate to a tensor
279        for (gate_idx, gate) in circuit.gates().iter().enumerate() {
280            let tensor = self.gate_to_tensor(gate.as_ref(), gate_idx)?;
281            let tensor_idx = tn.add_tensor(tensor);
282
283            // Connect to previous wires
284            for qubit in gate.qubits() {
285                let q = qubit.id() as usize;
286                let prev_wire = qubit_wires
287                    .get(&q)
288                    .ok_or_else(|| {
289                        QuantRS2Error::InvalidInput(format!("Qubit wire {q} not found"))
290                    })?
291                    .clone();
292                let new_wire = format!("q{q}_g{gate_idx}");
293
294                // Add bond from previous wire to this gate
295                if gate_idx > 0 || prev_wire.contains("_g") {
296                    tn.add_bond(
297                        tensor_idx - 1,
298                        prev_wire.clone(),
299                        tensor_idx,
300                        format!("in_{q}"),
301                    )?;
302                }
303
304                // Update wire for next connection
305                qubit_wires.insert(q, new_wire);
306            }
307        }
308
309        Ok(tn)
310    }
311
312    /// Convert a gate to tensor representation
313    fn gate_to_tensor(&self, gate: &dyn GateOp, gate_idx: usize) -> QuantRS2Result<Tensor> {
314        let qubits = gate.qubits();
315        let n_qubits = qubits.len();
316
317        match n_qubits {
318            1 => {
319                // Single-qubit gate
320                let matrix = self.get_single_qubit_matrix(gate)?;
321                let q = qubits[0].id() as usize;
322
323                Ok(Tensor::new(
324                    matrix,
325                    vec![2, 2],
326                    vec![format!("in_{}", q), format!("out_{}", q)],
327                ))
328            }
329            2 => {
330                // Two-qubit gate
331                let matrix = self.get_two_qubit_matrix(gate)?;
332                let q0 = qubits[0].id() as usize;
333                let q1 = qubits[1].id() as usize;
334
335                Ok(Tensor::new(
336                    matrix,
337                    vec![2, 2, 2, 2],
338                    vec![
339                        format!("in_{}", q0),
340                        format!("in_{}", q1),
341                        format!("out_{}", q0),
342                        format!("out_{}", q1),
343                    ],
344                ))
345            }
346            _ => Err(QuantRS2Error::UnsupportedOperation(format!(
347                "{n_qubits}-qubit gates not yet supported for tensor networks"
348            ))),
349        }
350    }
351
352    /// Get matrix representation of single-qubit gate
353    fn get_single_qubit_matrix(&self, gate: &dyn GateOp) -> QuantRS2Result<Vec<C64>> {
354        // Simplified - would use actual gate matrices
355        match gate.name() {
356            "H" => Ok(vec![
357                C64::new(1.0 / 2.0_f64.sqrt(), 0.0),
358                C64::new(1.0 / 2.0_f64.sqrt(), 0.0),
359                C64::new(1.0 / 2.0_f64.sqrt(), 0.0),
360                C64::new(-1.0 / 2.0_f64.sqrt(), 0.0),
361            ]),
362            "X" => Ok(vec![
363                C64::new(0.0, 0.0),
364                C64::new(1.0, 0.0),
365                C64::new(1.0, 0.0),
366                C64::new(0.0, 0.0),
367            ]),
368            "Y" => Ok(vec![
369                C64::new(0.0, 0.0),
370                C64::new(0.0, -1.0),
371                C64::new(0.0, 1.0),
372                C64::new(0.0, 0.0),
373            ]),
374            "Z" => Ok(vec![
375                C64::new(1.0, 0.0),
376                C64::new(0.0, 0.0),
377                C64::new(0.0, 0.0),
378                C64::new(-1.0, 0.0),
379            ]),
380            _ => Ok(vec![
381                C64::new(1.0, 0.0),
382                C64::new(0.0, 0.0),
383                C64::new(0.0, 0.0),
384                C64::new(1.0, 0.0),
385            ]),
386        }
387    }
388
389    /// Get matrix representation of two-qubit gate
390    fn get_two_qubit_matrix(&self, gate: &dyn GateOp) -> QuantRS2Result<Vec<C64>> {
391        // Simplified - would use actual gate matrices
392        if gate.name() == "CNOT" {
393            let mut matrix = vec![C64::new(0.0, 0.0); 16];
394            matrix[0] = C64::new(1.0, 0.0); // |00⟩ -> |00⟩
395            matrix[5] = C64::new(1.0, 0.0); // |01⟩ -> |01⟩
396            matrix[15] = C64::new(1.0, 0.0); // |10⟩ -> |11⟩
397            matrix[10] = C64::new(1.0, 0.0); // |11⟩ -> |10⟩
398            Ok(matrix)
399        } else {
400            // Identity for unsupported gates
401            let mut matrix = vec![C64::new(0.0, 0.0); 16];
402            for i in 0..16 {
403                matrix[i * 16 + i] = C64::new(1.0, 0.0);
404            }
405            Ok(matrix)
406        }
407    }
408}
409
410/// Matrix Product State representation of a circuit
411#[derive(Debug)]
412pub struct MatrixProductState {
413    /// Site tensors
414    tensors: Vec<Tensor>,
415    /// Bond dimensions
416    bond_dims: Vec<usize>,
417    /// Number of qubits
418    n_qubits: usize,
419}
420
421impl MatrixProductState {
422    /// Create MPS from circuit
423    pub fn from_circuit<const N: usize>(circuit: &Circuit<N>) -> QuantRS2Result<Self> {
424        let converter = CircuitToTensorNetwork::<N>::new();
425        let tn = converter.convert(circuit)?;
426
427        // Convert tensor network to MPS form
428        // This is a placeholder - would implement actual MPS conversion
429        Ok(Self {
430            tensors: Vec::new(),
431            bond_dims: Vec::new(),
432            n_qubits: N,
433        })
434    }
435
436    /// Compress the MPS
437    pub const fn compress(&mut self, max_bond_dim: usize, tolerance: f64) -> QuantRS2Result<()> {
438        // Implement SVD-based compression
439        // Sweep through the MPS and truncate bonds
440        Ok(())
441    }
442
443    /// Calculate overlap with another MPS
444    pub fn overlap(&self, other: &Self) -> QuantRS2Result<C64> {
445        if self.n_qubits != other.n_qubits {
446            return Err(QuantRS2Error::InvalidInput(
447                "MPS have different number of qubits".to_string(),
448            ));
449        }
450
451        // Calculate ⟨ψ|φ⟩
452        Ok(C64::new(1.0, 0.0)) // Placeholder
453    }
454
455    /// Calculate expectation value of observable
456    pub const fn expectation_value(&self, observable: &TensorNetwork) -> QuantRS2Result<f64> {
457        // Calculate ⟨ψ|O|ψ⟩
458        Ok(0.0) // Placeholder
459    }
460}
461
462/// Circuit compression using tensor networks
463pub struct TensorNetworkCompressor {
464    /// Maximum bond dimension
465    max_bond_dim: usize,
466    /// Truncation tolerance
467    tolerance: f64,
468    /// Compression method
469    method: CompressionMethod,
470}
471
472#[derive(Debug, Clone)]
473pub enum CompressionMethod {
474    /// Singular Value Decomposition
475    SVD,
476    /// Density Matrix Renormalization Group
477    DMRG,
478    /// Time-Evolving Block Decimation
479    TEBD,
480}
481
482impl TensorNetworkCompressor {
483    /// Create a new compressor
484    #[must_use]
485    pub const fn new(max_bond_dim: usize) -> Self {
486        Self {
487            max_bond_dim,
488            tolerance: 1e-10,
489            method: CompressionMethod::SVD,
490        }
491    }
492
493    /// Set compression method
494    #[must_use]
495    pub const fn with_method(mut self, method: CompressionMethod) -> Self {
496        self.method = method;
497        self
498    }
499
500    /// Compress a circuit
501    pub fn compress<const N: usize>(
502        &self,
503        circuit: &Circuit<N>,
504    ) -> QuantRS2Result<CompressedCircuit<N>> {
505        let mps = MatrixProductState::from_circuit(circuit)?;
506
507        Ok(CompressedCircuit {
508            mps,
509            original_gates: circuit.num_gates(),
510            compression_ratio: 1.0, // Placeholder
511        })
512    }
513}
514
515/// Compressed circuit representation
516#[derive(Debug)]
517pub struct CompressedCircuit<const N: usize> {
518    /// MPS representation
519    mps: MatrixProductState,
520    /// Original number of gates
521    original_gates: usize,
522    /// Compression ratio
523    compression_ratio: f64,
524}
525
526impl<const N: usize> CompressedCircuit<N> {
527    /// Get compression ratio
528    #[must_use]
529    pub const fn compression_ratio(&self) -> f64 {
530        self.compression_ratio
531    }
532
533    /// Decompress back to circuit
534    pub fn decompress(&self) -> QuantRS2Result<Circuit<N>> {
535        // Convert MPS back to circuit representation
536        // This is non-trivial and would require gate synthesis
537        Ok(Circuit::<N>::new())
538    }
539
540    /// Get fidelity with original circuit
541    pub const fn fidelity(&self, original: &Circuit<N>) -> QuantRS2Result<f64> {
542        // Calculate |⟨ψ_compressed|ψ_original⟩|²
543        Ok(0.99) // Placeholder
544    }
545}
546
547#[cfg(test)]
548mod tests {
549    use super::*;
550    use quantrs2_core::gate::single::Hadamard;
551
552    #[test]
553    fn test_tensor_creation() {
554        let data = vec![
555            C64::new(1.0, 0.0),
556            C64::new(0.0, 0.0),
557            C64::new(0.0, 0.0),
558            C64::new(1.0, 0.0),
559        ];
560        let tensor = Tensor::new(data, vec![2, 2], vec!["in".to_string(), "out".to_string()]);
561
562        assert_eq!(tensor.rank(), 2);
563        assert_eq!(tensor.size(), 4);
564    }
565
566    #[test]
567    fn test_tensor_network() {
568        let mut tn = TensorNetwork::new();
569
570        let t1 = Tensor::identity(2, "a".to_string(), "b".to_string());
571        let t2 = Tensor::identity(2, "c".to_string(), "d".to_string());
572
573        let idx1 = tn.add_tensor(t1);
574        let idx2 = tn.add_tensor(t2);
575
576        tn.add_bond(idx1, "b".to_string(), idx2, "c".to_string())
577            .expect("Failed to add bond between tensors");
578
579        assert_eq!(tn.tensors.len(), 2);
580        assert_eq!(tn.bonds.len(), 1);
581    }
582
583    #[test]
584    fn test_circuit_to_tensor_network() {
585        let mut circuit = Circuit::<2>::new();
586        circuit
587            .add_gate(Hadamard { target: QubitId(0) })
588            .expect("Failed to add Hadamard gate");
589
590        let converter = CircuitToTensorNetwork::<2>::new();
591        let tn = converter
592            .convert(&circuit)
593            .expect("Failed to convert circuit to tensor network");
594
595        assert!(!tn.tensors.is_empty());
596    }
597
598    #[test]
599    fn test_compression() {
600        let circuit = Circuit::<2>::new();
601        let compressor = TensorNetworkCompressor::new(32);
602
603        let compressed = compressor
604            .compress(&circuit)
605            .expect("Failed to compress circuit");
606        assert!(compressed.compression_ratio() <= 1.0);
607    }
608}
quantrs2_circuit/tensor_network.rs

quantrs2_circuit/
tensor_network.rs
