quantrs2_ml/quantum_llm/
types.rs

1//! Auto-generated module
2//!
3//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)
4
5use crate::error::{MLError, Result};
6use crate::qnn::{QNNLayerType, QuantumNeuralNetwork};
7use crate::quantum_transformer::{
8    create_causal_mask, PositionEncodingType, QuantumAttentionType, QuantumTransformer,
9    QuantumTransformerConfig,
10};
11use quantrs2_circuit::builder::{Circuit, Simulator};
12use quantrs2_core::gate::{multi::*, single::*, GateOp};
13use quantrs2_sim::statevector::StateVectorSimulator;
14use scirs2_core::ndarray::{s, Array1, Array2, Array3, Array4, Axis};
15use std::collections::HashMap;
16use std::f64::consts::PI;
17
18/// Quantum reasoning module
19#[derive(Debug, Clone)]
20pub struct QuantumReasoningModule {
21    /// Reasoning configuration
22    config: QuantumReasoningConfig,
23    /// Logical reasoning circuits
24    pub(crate) logical_circuits: Vec<Circuit<16>>,
25    /// Causal reasoning networks
26    causal_networks: Vec<QuantumNeuralNetwork>,
27    /// Analogical reasoning system
28    analogical_system: QuantumAnalogyEngine,
29    /// Reasoning state memory
30    reasoning_memory: Array2<f64>,
31    /// Chain-of-thought quantum states
32    cot_states: Vec<Array1<f64>>,
33}
34impl QuantumReasoningModule {
35    /// Create new quantum reasoning module
36    pub fn new(config: QuantumReasoningConfig) -> Result<Self> {
37        let mut logical_circuits = Vec::new();
38        let mut causal_networks = Vec::new();
39        if config.logical_reasoning {
40            for _ in 0..config.reasoning_steps {
41                let mut circuit = Circuit::<16>::new();
42                for i in 0..8 {
43                    circuit.h(i);
44                    circuit.ry(i, 0.0);
45                }
46                for i in 0..7 {
47                    circuit.cnot(i, i + 1);
48                }
49                logical_circuits.push(circuit);
50            }
51        }
52        if config.causal_reasoning {
53            for _ in 0..config.reasoning_steps {
54                let layers = vec![
55                    QNNLayerType::EncodingLayer { num_features: 256 },
56                    QNNLayerType::VariationalLayer { num_params: 128 },
57                    QNNLayerType::EntanglementLayer {
58                        connectivity: "circular".to_string(),
59                    },
60                    QNNLayerType::VariationalLayer { num_params: 256 },
61                    QNNLayerType::MeasurementLayer {
62                        measurement_basis: "computational".to_string(),
63                    },
64                ];
65                let network = QuantumNeuralNetwork::new(layers, 12, 256, 256)?;
66                causal_networks.push(network);
67            }
68        }
69        let analogical_system = QuantumAnalogyEngine::new()?;
70        Ok(Self {
71            config,
72            logical_circuits,
73            causal_networks,
74            analogical_system,
75            reasoning_memory: Array2::zeros((100, 256)),
76            cot_states: Vec::new(),
77        })
78    }
79    /// Apply quantum reasoning to transformer output
80    pub fn apply_reasoning(&mut self, hidden_states: &Array3<f64>) -> Result<Array3<f64>> {
81        let mut reasoned_output = hidden_states.clone();
82        if self.config.logical_reasoning {
83            reasoned_output = self.apply_logical_reasoning(&reasoned_output)?;
84        }
85        if self.config.causal_reasoning {
86            reasoned_output = self.apply_causal_reasoning(&reasoned_output)?;
87        }
88        if self.config.analogical_reasoning {
89            reasoned_output = self.apply_analogical_reasoning(&reasoned_output)?;
90        }
91        Ok(reasoned_output)
92    }
93    /// Apply logical reasoning using quantum circuits
94    fn apply_logical_reasoning(&mut self, hidden_states: &Array3<f64>) -> Result<Array3<f64>> {
95        let mut output = hidden_states.clone();
96        let (batch_size, seq_len, hidden_dim) = hidden_states.dim();
97        for step in 0..self.config.reasoning_steps.min(self.logical_circuits.len()) {
98            let simulator = StateVectorSimulator::new();
99            let register = simulator.run(&self.logical_circuits[step])?;
100            let quantum_state = register.probabilities();
101            let reasoning_features = self.extract_logical_features(&quantum_state)?;
102            for batch_idx in 0..batch_size {
103                for seq_idx in 0..seq_len {
104                    let mut hidden = output.slice_mut(s![batch_idx, seq_idx, ..]);
105                    let reasoning_weight = 0.1;
106                    for (i, &feature) in reasoning_features.iter().enumerate() {
107                        if i < hidden.len() {
108                            hidden[i] =
109                                hidden[i] * (1.0 - reasoning_weight) + feature * reasoning_weight;
110                        }
111                    }
112                }
113            }
114            let reasoning_state = Array1::from_vec(reasoning_features);
115            self.cot_states.push(reasoning_state);
116        }
117        Ok(output)
118    }
119    /// Apply causal reasoning using quantum networks
120    fn apply_causal_reasoning(&self, hidden_states: &Array3<f64>) -> Result<Array3<f64>> {
121        if self.causal_networks.is_empty() {
122            return Ok(hidden_states.clone());
123        }
124        let mut output = hidden_states.clone();
125        let (batch_size, seq_len, hidden_dim) = hidden_states.dim();
126        for batch_idx in 0..batch_size {
127            for seq_idx in 1..seq_len {
128                let current_hidden = hidden_states.slice(s![batch_idx, seq_idx, ..]).to_owned();
129                let prev_hidden = hidden_states
130                    .slice(s![batch_idx, seq_idx - 1, ..])
131                    .to_owned();
132                let causal_input = Array1::from_iter(
133                    current_hidden
134                        .iter()
135                        .chain(prev_hidden.iter())
136                        .take(256)
137                        .cloned(),
138                );
139                let causal_output = self.causal_networks[0].forward(&causal_input)?;
140                let causal_weight = 0.2;
141                output.slice_mut(s![batch_idx, seq_idx, ..]).zip_mut_with(
142                    &causal_output,
143                    |orig, causal| {
144                        *orig = *orig * (1.0 - causal_weight) + causal * causal_weight;
145                    },
146                );
147            }
148        }
149        Ok(output)
150    }
151    /// Apply analogical reasoning
152    fn apply_analogical_reasoning(&self, hidden_states: &Array3<f64>) -> Result<Array3<f64>> {
153        self.analogical_system
154            .apply_analogical_reasoning(hidden_states)
155    }
156    /// Enhance token selection with quantum reasoning
157    pub fn enhance_token_selection(&self, logits: &Array1<f64>) -> Result<Array1<f64>> {
158        let mut enhanced_logits = logits.clone();
159        if !self.cot_states.is_empty() {
160            let latest_reasoning = &self.cot_states[self.cot_states.len() - 1];
161            let reasoning_weight = 0.1;
162            for (i, &reasoning_val) in latest_reasoning.iter().enumerate() {
163                if i < enhanced_logits.len() {
164                    enhanced_logits[i] += reasoning_val * reasoning_weight;
165                }
166            }
167        }
168        Ok(enhanced_logits)
169    }
170    /// Measure quantum coherence in reasoning
171    pub fn measure_coherence(&self) -> Result<f64> {
172        if self.cot_states.is_empty() {
173            return Ok(0.0);
174        }
175        let latest_state = &self.cot_states[self.cot_states.len() - 1];
176        let coherence = 1.0
177            - latest_state
178                .mapv(|x| (x * PI).sin().abs())
179                .mean()
180                .unwrap_or(0.0);
181        Ok(coherence)
182    }
183    /// Extract logical features from quantum state
184    fn extract_logical_features(&self, quantum_state: &[f64]) -> Result<Vec<f64>> {
185        let mut features = Vec::new();
186        for (i, &amplitude) in quantum_state.iter().enumerate() {
187            let logical_feature = amplitude * amplitude;
188            features.push(logical_feature);
189            if features.len() >= 256 {
190                break;
191            }
192        }
193        while features.len() < 256 {
194            features.push(0.0);
195        }
196        Ok(features)
197    }
198    /// Get number of parameters
199    pub fn num_parameters(&self) -> usize {
200        let mut total = 0;
201        for network in &self.causal_networks {
202            total += network.parameters.len();
203        }
204        total += self.analogical_system.num_parameters();
205        total += self.reasoning_memory.len();
206        total
207    }
208}
209/// Quantum associative memory
210#[derive(Debug, Clone)]
211pub struct QuantumAssociativeMemory {
212    /// Memory patterns
213    patterns: Array2<f64>,
214    /// Quantum Hopfield weights
215    hopfield_weights: Array2<f64>,
216    /// Memory circuit parameters
217    memory_circuit_params: Vec<f64>,
218    /// Pattern amplitudes
219    amplitudes: Array1<f64>,
220    /// Retrieval threshold
221    threshold: f64,
222}
223impl QuantumAssociativeMemory {
224    /// Create new quantum associative memory
225    pub fn new(capacity: usize, pattern_size: usize) -> Result<Self> {
226        let patterns = Array2::zeros((capacity, pattern_size));
227        let hopfield_weights = Array2::zeros((pattern_size, pattern_size));
228        let num_qubits = 8;
229        let mut memory_circuit_params = Vec::new();
230        for _ in 0..num_qubits {
231            memory_circuit_params.push(1.0);
232        }
233        for i in 0..num_qubits {
234            for j in i + 1..num_qubits {
235                memory_circuit_params.push(2.0);
236            }
237        }
238        Ok(Self {
239            patterns,
240            hopfield_weights,
241            memory_circuit_params,
242            amplitudes: Array1::zeros(capacity),
243            threshold: 0.5,
244        })
245    }
246    /// Store pattern in memory
247    pub fn store_pattern(&mut self, pattern: &Array1<f64>) -> Result<()> {
248        let store_idx = self
249            .amplitudes
250            .iter()
251            .enumerate()
252            .min_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
253            .map(|(i, _)| i)
254            .unwrap_or(0);
255        self.patterns
256            .slice_mut(s![store_idx, ..pattern.len()])
257            .assign(pattern);
258        for i in 0..pattern.len() {
259            for j in 0..pattern.len() {
260                if i != j {
261                    self.hopfield_weights[[i, j]] += pattern[i] * pattern[j] * 0.1;
262                }
263            }
264        }
265        self.amplitudes[store_idx] = 1.0;
266        Ok(())
267    }
268    /// Retrieve pattern from memory
269    pub fn retrieve(&self, query: &Array1<f64>) -> Result<Array1<f64>> {
270        let mut best_match = query.clone();
271        let mut best_similarity = 0.0;
272        for i in 0..self.patterns.nrows() {
273            if self.amplitudes[i] > 0.1 {
274                let pattern = self.patterns.row(i).to_owned();
275                let similarity = self.compute_similarity(query, &pattern)?;
276                if similarity > best_similarity {
277                    best_similarity = similarity;
278                    best_match = pattern;
279                }
280            }
281        }
282        if best_similarity > self.threshold {
283            let retrieved = self.apply_hopfield_dynamics(&best_match)?;
284            Ok(retrieved)
285        } else {
286            Ok(query.clone())
287        }
288    }
289    /// Compute similarity between patterns
290    fn compute_similarity(&self, pattern1: &Array1<f64>, pattern2: &Array1<f64>) -> Result<f64> {
291        let norm1 = pattern1.mapv(|x| x * x).sum().sqrt();
292        let norm2 = pattern2.mapv(|x| x * x).sum().sqrt();
293        if norm1 < 1e-10 || norm2 < 1e-10 {
294            return Ok(0.0);
295        }
296        let dot_product = pattern1.dot(pattern2);
297        Ok(dot_product / (norm1 * norm2))
298    }
299    /// Apply Hopfield dynamics for pattern completion
300    fn apply_hopfield_dynamics(&self, initial: &Array1<f64>) -> Result<Array1<f64>> {
301        let mut state = initial.clone();
302        for _ in 0..5 {
303            let new_state = self.hopfield_weights.dot(&state);
304            state = new_state.mapv(|x| x.tanh());
305        }
306        Ok(state)
307    }
308}
309/// Model scale variants
310#[derive(Debug, Clone)]
311pub enum ModelScale {
312    /// Small model (< 1B parameters)
313    Small {
314        layers: usize,
315        model_dim: usize,
316        heads: usize,
317    },
318    /// Medium model (1B - 10B parameters)
319    Medium {
320        layers: usize,
321        model_dim: usize,
322        heads: usize,
323    },
324    /// Large model (10B - 100B parameters)
325    Large {
326        layers: usize,
327        model_dim: usize,
328        heads: usize,
329    },
330    /// XL model (> 100B parameters)
331    ExtraLarge {
332        layers: usize,
333        model_dim: usize,
334        heads: usize,
335    },
336}
337/// Subword tokenizer
338#[derive(Debug, Clone)]
339pub struct SubwordTokenizer {
340    /// BPE merges
341    merges: Vec<(String, String)>,
342    /// Token frequencies
343    frequencies: HashMap<String, usize>,
344    /// Quantum encoding of subwords
345    quantum_encodings: HashMap<String, Array1<f64>>,
346}
347impl SubwordTokenizer {
348    /// Create new subword tokenizer
349    pub fn new() -> Self {
350        Self {
351            merges: Vec::new(),
352            frequencies: HashMap::new(),
353            quantum_encodings: HashMap::new(),
354        }
355    }
356}
357/// Quality metrics for generated text
358#[derive(Debug, Clone)]
359pub struct QualityMetrics {
360    /// Perplexity score
361    pub perplexity: f64,
362    /// Coherence score
363    pub coherence: f64,
364    /// Factual accuracy
365    pub factual_accuracy: f64,
366    /// Logical consistency
367    pub logical_consistency: f64,
368    /// Quantum advantage measure
369    pub quantum_advantage: f64,
370}
371/// Text generation parameters
372#[derive(Debug, Clone)]
373pub struct GenerationConfig {
374    /// Maximum generation length
375    pub max_length: usize,
376    /// Temperature for sampling
377    pub temperature: f64,
378    /// Top-k sampling
379    pub top_k: Option<usize>,
380    /// Top-p (nucleus) sampling
381    pub top_p: Option<f64>,
382    /// Repetition penalty
383    pub repetition_penalty: f64,
384    /// Enable quantum reasoning during generation
385    pub use_quantum_reasoning: bool,
386    /// Memory-guided generation
387    pub use_memory: bool,
388    /// Chain-of-thought generation
389    pub chain_of_thought: bool,
390}
391impl GenerationConfig {
392    /// Default generation configuration
393    pub fn default() -> Self {
394        Self {
395            max_length: 100,
396            temperature: 1.0,
397            top_k: Some(50),
398            top_p: Some(0.9),
399            repetition_penalty: 1.1,
400            use_quantum_reasoning: true,
401            use_memory: true,
402            chain_of_thought: false,
403        }
404    }
405    /// Creative generation configuration
406    pub fn creative() -> Self {
407        Self {
408            max_length: 200,
409            temperature: 1.2,
410            top_k: Some(100),
411            top_p: Some(0.95),
412            repetition_penalty: 1.05,
413            use_quantum_reasoning: true,
414            use_memory: true,
415            chain_of_thought: true,
416        }
417    }
418    /// Precise generation configuration
419    pub fn precise() -> Self {
420        Self {
421            max_length: 50,
422            temperature: 0.7,
423            top_k: Some(20),
424            top_p: Some(0.8),
425            repetition_penalty: 1.2,
426            use_quantum_reasoning: true,
427            use_memory: true,
428            chain_of_thought: true,
429        }
430    }
431}
432/// Quantum analogy engine
433#[derive(Debug, Clone)]
434pub struct QuantumAnalogyEngine {
435    /// Analogy mapping circuit parameters
436    mapping_circuit_params: Vec<Vec<f64>>,
437    /// Structural similarity measures
438    similarity_measures: Array2<f64>,
439    /// Analogy transformation matrices
440    transformations: Vec<Array2<f64>>,
441    /// Quantum interference patterns for analogies
442    interference_patterns: Array3<f64>,
443}
444impl QuantumAnalogyEngine {
445    /// Create new quantum analogy engine
446    pub fn new() -> Result<Self> {
447        let mut mapping_circuit_params = Vec::new();
448        for _ in 0..5 {
449            let mut params = Vec::new();
450            for _ in 0..10 {
451                params.push(1.0);
452                params.push(0.0);
453            }
454            for _ in 0..5 {
455                params.push(2.0);
456            }
457            mapping_circuit_params.push(params);
458        }
459        Ok(Self {
460            mapping_circuit_params,
461            similarity_measures: Array2::zeros((100, 100)),
462            transformations: Vec::new(),
463            interference_patterns: Array3::zeros((10, 10, 10)),
464        })
465    }
466    /// Apply analogical reasoning
467    pub fn apply_analogical_reasoning(&self, hidden_states: &Array3<f64>) -> Result<Array3<f64>> {
468        let mut output = hidden_states.clone();
469        let (batch_size, seq_len, hidden_dim) = hidden_states.dim();
470        for batch_idx in 0..batch_size {
471            for seq_idx in 0..seq_len {
472                let hidden = hidden_states.slice(s![batch_idx, seq_idx, ..]);
473                let analogy_weight = 0.05;
474                let analogy_factor = (seq_idx as f64 * 0.1).sin() * analogy_weight;
475                output
476                    .slice_mut(s![batch_idx, seq_idx, ..])
477                    .zip_mut_with(&hidden, |orig, h| {
478                        *orig = *orig + h * analogy_factor;
479                    });
480            }
481        }
482        Ok(output)
483    }
484    /// Get number of parameters
485    pub fn num_parameters(&self) -> usize {
486        self.similarity_measures.len()
487            + self.transformations.iter().map(|t| t.len()).sum::<usize>()
488            + self.interference_patterns.len()
489    }
490}
491/// Quantum memory system
492#[derive(Debug, Clone)]
493pub struct QuantumMemorySystem {
494    /// Memory configuration
495    config: QuantumMemoryConfig,
496    /// Associative memory banks
497    pub(crate) associative_banks: Vec<QuantumAssociativeMemory>,
498    /// Episodic memory store
499    episodic_memory: Vec<QuantumEpisode>,
500    /// Memory retrieval circuit parameters
501    retrieval_circuit_params: Vec<Vec<f64>>,
502    /// Memory compression codebooks
503    compression_codebooks: HashMap<String, Array2<f64>>,
504}
505impl QuantumMemorySystem {
506    /// Create new quantum memory system
507    pub fn new(config: QuantumMemoryConfig) -> Result<Self> {
508        let mut associative_banks = Vec::new();
509        let mut retrieval_circuit_params = Vec::new();
510        if config.associative_memory {
511            for _ in 0..5 {
512                let memory_bank = QuantumAssociativeMemory::new(100, 128)?;
513                associative_banks.push(memory_bank);
514            }
515        }
516        for _ in 0..config.memory_size / 100 {
517            let mut params = Vec::new();
518            for _ in 0..8 {
519                params.push(1.0);
520                params.push(0.0);
521            }
522            for _ in 0..7 {
523                params.push(2.0);
524            }
525            retrieval_circuit_params.push(params);
526        }
527        Ok(Self {
528            config,
529            associative_banks,
530            episodic_memory: Vec::new(),
531            retrieval_circuit_params,
532            compression_codebooks: HashMap::new(),
533        })
534    }
535    /// Enhance embeddings with memory retrieval
536    pub fn enhance_embeddings(&self, embeddings: &Array3<f64>) -> Result<Array3<f64>> {
537        let mut enhanced = embeddings.clone();
538        if self.config.associative_memory && !self.associative_banks.is_empty() {
539            for batch_idx in 0..embeddings.dim().0 {
540                for seq_idx in 0..embeddings.dim().1 {
541                    let query = embeddings.slice(s![batch_idx, seq_idx, ..]).to_owned();
542                    let retrieved_memory = self.retrieve_associative_memory(&query)?;
543                    let combination_weight = 0.1;
544                    enhanced.slice_mut(s![batch_idx, seq_idx, ..]).zip_mut_with(
545                        &retrieved_memory,
546                        |orig, mem| {
547                            *orig = *orig * (1.0 - combination_weight) + mem * combination_weight;
548                        },
549                    );
550                }
551            }
552        }
553        Ok(enhanced)
554    }
555    /// Retrieve from associative memory
556    fn retrieve_associative_memory(&self, query: &Array1<f64>) -> Result<Array1<f64>> {
557        if self.associative_banks.is_empty() {
558            return Ok(query.clone());
559        }
560        self.associative_banks[0].retrieve(query)
561    }
562    /// Update memory with new information
563    pub fn update_memory(
564        &mut self,
565        hidden_states: &Array3<f64>,
566        input_ids: &Array2<usize>,
567    ) -> Result<()> {
568        let (batch_size, seq_len, hidden_dim) = hidden_states.dim();
569        if self.config.episodic_memory {
570            for batch_idx in 0..batch_size {
571                let context = hidden_states.slice(s![batch_idx, 0, ..]).to_owned();
572                let content = hidden_states.slice(s![batch_idx, .., ..]).to_owned();
573                let episode = QuantumEpisode {
574                    context,
575                    content,
576                    quantum_state: Array1::zeros(hidden_dim),
577                    timestamp: self.episodic_memory.len() as f64,
578                    coherence: 0.8,
579                    importance: 1.0,
580                };
581                self.episodic_memory.push(episode);
582                if self.episodic_memory.len() > self.config.memory_size {
583                    self.episodic_memory.remove(0);
584                }
585            }
586        }
587        for bank in &mut self.associative_banks {
588            let sample_hidden = hidden_states.slice(s![0, 0, ..]).to_owned();
589            bank.store_pattern(&sample_hidden)?;
590        }
591        Ok(())
592    }
593    /// Get number of parameters
594    pub fn num_parameters(&self) -> usize {
595        let mut total = 0;
596        for bank in &self.associative_banks {
597            total += bank.hopfield_weights.len();
598            total += bank.patterns.len();
599        }
600        for codebook in self.compression_codebooks.values() {
601            total += codebook.len();
602        }
603        total
604    }
605}
606/// Vocabulary management
607#[derive(Debug, Clone)]
608pub struct Vocabulary {
609    /// Token to ID mapping
610    token_to_id: HashMap<String, usize>,
611    /// ID to token mapping
612    id_to_token: HashMap<usize, String>,
613    /// Special tokens
614    pub(crate) special_tokens: HashMap<String, usize>,
615    /// Subword tokenizer
616    tokenizer: SubwordTokenizer,
617    /// Quantum token embeddings
618    pub(crate) quantum_embeddings: Array2<f64>,
619}
620impl Vocabulary {
621    /// Create new vocabulary
622    pub fn new(vocab_size: usize) -> Result<Self> {
623        let mut token_to_id = HashMap::new();
624        let mut id_to_token = HashMap::new();
625        let mut special_tokens = HashMap::new();
626        special_tokens.insert("<pad>".to_string(), 0);
627        special_tokens.insert("<unk>".to_string(), 1);
628        special_tokens.insert("<sos>".to_string(), 2);
629        special_tokens.insert("<eos>".to_string(), 3);
630        token_to_id.insert("<pad>".to_string(), 0);
631        token_to_id.insert("<unk>".to_string(), 1);
632        token_to_id.insert("<sos>".to_string(), 2);
633        token_to_id.insert("<eos>".to_string(), 3);
634        id_to_token.insert(0, "<pad>".to_string());
635        id_to_token.insert(1, "<unk>".to_string());
636        id_to_token.insert(2, "<sos>".to_string());
637        id_to_token.insert(3, "<eos>".to_string());
638        let quantum_embeddings = Array2::from_shape_fn((vocab_size, 768), |(i, j)| {
639            0.02 * (i as f64 * 0.1 + j as f64 * 0.01).sin()
640        });
641        let tokenizer = SubwordTokenizer::new();
642        Ok(Self {
643            token_to_id,
644            id_to_token,
645            special_tokens,
646            tokenizer,
647            quantum_embeddings,
648        })
649    }
650    /// Tokenize text
651    pub fn tokenize(&self, text: &str) -> Result<Vec<usize>> {
652        let tokens: Vec<usize> = text
653            .split_whitespace()
654            .map(|word| self.token_to_id.get(word).copied().unwrap_or(1))
655            .collect();
656        Ok(tokens)
657    }
658    /// Get token embedding
659    pub fn get_embedding(&self, token_id: usize) -> Result<Array1<f64>> {
660        if token_id < self.quantum_embeddings.nrows() {
661            Ok(self.quantum_embeddings.row(token_id).to_owned())
662        } else {
663            Ok(self.quantum_embeddings.row(1).to_owned())
664        }
665    }
666    /// Decode token
667    pub fn decode_token(&self, token_id: usize) -> Result<String> {
668        Ok(self
669            .id_to_token
670            .get(&token_id)
671            .cloned()
672            .unwrap_or_else(|| "<unk>".to_string()))
673    }
674    /// Check if token is end-of-sequence
675    pub fn is_eos_token(&self, token_id: usize) -> bool {
676        token_id == 3
677    }
678}
679/// Quantum parameter update strategies
680#[derive(Debug, Clone)]
681pub enum QuantumParameterUpdate {
682    /// Classical gradient descent on quantum parameters
683    ClassicalOnQuantum,
684    /// Quantum natural gradients
685    QuantumNatural,
686    /// Quantum BFGS optimization
687    QuantumBFGS,
688    /// Parameter shift rule
689    ParameterShift,
690    /// Quantum Adam optimizer
691    QuantumAdam,
692}
693/// Training configuration for QLLMs
694#[derive(Debug, Clone)]
695pub struct QLLMTrainingConfig {
696    /// Quantum-classical hybrid training
697    pub hybrid_training: bool,
698    /// Quantum parameter update strategy
699    pub parameter_update: QuantumParameterUpdate,
700    /// Gradient accumulation steps
701    pub gradient_accumulation: usize,
702    /// Quantum noise injection for regularization
703    pub quantum_noise: bool,
704    /// Quantum advantage optimization
705    pub quantum_advantage_opt: bool,
706}
707impl QLLMTrainingConfig {
708    /// Default training configuration
709    pub fn default() -> Self {
710        Self {
711            hybrid_training: true,
712            parameter_update: QuantumParameterUpdate::ClassicalOnQuantum,
713            gradient_accumulation: 1,
714            quantum_noise: false,
715            quantum_advantage_opt: false,
716        }
717    }
718    /// Advanced training configuration
719    pub fn advanced() -> Self {
720        Self {
721            hybrid_training: true,
722            parameter_update: QuantumParameterUpdate::QuantumAdam,
723            gradient_accumulation: 8,
724            quantum_noise: true,
725            quantum_advantage_opt: true,
726        }
727    }
728}
729/// Quantum Large Language Model configuration
730#[derive(Debug, Clone)]
731pub struct QuantumLLMConfig {
732    /// Base transformer configuration
733    pub transformer_config: QuantumTransformerConfig,
734    /// Vocabulary size
735    pub vocab_size: usize,
736    /// Maximum context length
737    pub max_context_length: usize,
738    /// Number of quantum memory layers
739    pub quantum_memory_layers: usize,
740    /// Quantum reasoning module configuration
741    pub reasoning_config: QuantumReasoningConfig,
742    /// Quantum memory configuration
743    pub memory_config: QuantumMemoryConfig,
744    /// Model scale
745    pub model_scale: ModelScale,
746    /// Training configuration
747    pub training_config: QLLMTrainingConfig,
748}
749impl QuantumLLMConfig {
750    /// Create small model configuration
751    pub fn small(vocab_size: usize) -> Self {
752        Self {
753            transformer_config: QuantumTransformerConfig {
754                model_dim: 768,
755                num_heads: 12,
756                ff_dim: 3072,
757                num_layers: 12,
758                max_seq_len: 2048,
759                num_qubits: 10,
760                dropout_rate: 0.1,
761                attention_type: QuantumAttentionType::HybridQuantumClassical,
762                position_encoding: PositionEncodingType::Rotary,
763            },
764            vocab_size,
765            max_context_length: 2048,
766            quantum_memory_layers: 4,
767            reasoning_config: QuantumReasoningConfig::default(),
768            memory_config: QuantumMemoryConfig::default(),
769            model_scale: ModelScale::Small {
770                layers: 12,
771                model_dim: 768,
772                heads: 12,
773            },
774            training_config: QLLMTrainingConfig::default(),
775        }
776    }
777    /// Create medium model configuration
778    pub fn medium(vocab_size: usize) -> Self {
779        Self {
780            transformer_config: QuantumTransformerConfig {
781                model_dim: 1024,
782                num_heads: 16,
783                ff_dim: 4096,
784                num_layers: 24,
785                max_seq_len: 4096,
786                num_qubits: 16,
787                dropout_rate: 0.1,
788                attention_type: QuantumAttentionType::QuantumEnhancedMultiHead,
789                position_encoding: PositionEncodingType::LearnableQuantum,
790            },
791            vocab_size,
792            max_context_length: 4096,
793            quantum_memory_layers: 8,
794            reasoning_config: QuantumReasoningConfig::enhanced(),
795            memory_config: QuantumMemoryConfig::enhanced(),
796            model_scale: ModelScale::Medium {
797                layers: 24,
798                model_dim: 1024,
799                heads: 16,
800            },
801            training_config: QLLMTrainingConfig::default(),
802        }
803    }
804    /// Create large model configuration
805    pub fn large(vocab_size: usize) -> Self {
806        Self {
807            transformer_config: QuantumTransformerConfig {
808                model_dim: 1536,
809                num_heads: 24,
810                ff_dim: 6144,
811                num_layers: 48,
812                max_seq_len: 8192,
813                num_qubits: 12,
814                dropout_rate: 0.1,
815                attention_type: QuantumAttentionType::FullQuantum,
816                position_encoding: PositionEncodingType::QuantumPhase,
817            },
818            vocab_size,
819            max_context_length: 8192,
820            quantum_memory_layers: 16,
821            reasoning_config: QuantumReasoningConfig::advanced(),
822            memory_config: QuantumMemoryConfig::advanced(),
823            model_scale: ModelScale::Large {
824                layers: 48,
825                model_dim: 1536,
826                heads: 24,
827            },
828            training_config: QLLMTrainingConfig::advanced(),
829        }
830    }
831}
832/// Quantum memory configuration
833#[derive(Debug, Clone)]
834pub struct QuantumMemoryConfig {
835    /// Memory bank size
836    pub memory_size: usize,
837    /// Quantum associative memory
838    pub associative_memory: bool,
839    /// Episodic memory with quantum states
840    pub episodic_memory: bool,
841    /// Memory retrieval mechanism
842    pub retrieval_mechanism: MemoryRetrievalType,
843    /// Memory compression using quantum algorithms
844    pub quantum_compression: bool,
845    /// Memory coherence time
846    pub coherence_time: f64,
847}
848impl QuantumMemoryConfig {
849    /// Default memory configuration
850    pub fn default() -> Self {
851        Self {
852            memory_size: 1000,
853            associative_memory: true,
854            episodic_memory: false,
855            retrieval_mechanism: MemoryRetrievalType::QuantumAssociative,
856            quantum_compression: false,
857            coherence_time: 100.0,
858        }
859    }
860    /// Enhanced memory configuration
861    pub fn enhanced() -> Self {
862        Self {
863            memory_size: 5000,
864            associative_memory: true,
865            episodic_memory: true,
866            retrieval_mechanism: MemoryRetrievalType::ContentAddressable,
867            quantum_compression: true,
868            coherence_time: 200.0,
869        }
870    }
871    /// Advanced memory configuration
872    pub fn advanced() -> Self {
873        Self {
874            memory_size: 20000,
875            associative_memory: true,
876            episodic_memory: true,
877            retrieval_mechanism: MemoryRetrievalType::Holographic,
878            quantum_compression: true,
879            coherence_time: 500.0,
880        }
881    }
882}
883/// Memory retrieval mechanisms
884#[derive(Debug, Clone)]
885pub enum MemoryRetrievalType {
886    /// Quantum associative retrieval
887    QuantumAssociative,
888    /// Content-addressable memory
889    ContentAddressable,
890    /// Holographic memory retrieval
891    Holographic,
892    /// Quantum Hopfield networks
893    QuantumHopfield,
894    /// Hierarchical memory access
895    Hierarchical,
896}
897/// Generation statistics
898#[derive(Debug, Clone)]
899pub struct GenerationStatistics {
900    /// Total tokens generated
901    pub total_tokens: usize,
902    /// Average generation speed (tokens/sec)
903    pub avg_speed: f64,
904    /// Quantum coherence during generation
905    pub quantum_coherence: f64,
906    /// Reasoning steps taken
907    pub reasoning_steps: usize,
908    /// Memory retrievals
909    pub memory_retrievals: usize,
910    /// Generation quality metrics
911    pub quality_metrics: QualityMetrics,
912}
913impl GenerationStatistics {
914    /// Create new generation statistics
915    pub fn new() -> Self {
916        Self {
917            total_tokens: 0,
918            avg_speed: 0.0,
919            quantum_coherence: 0.0,
920            reasoning_steps: 0,
921            memory_retrievals: 0,
922            quality_metrics: QualityMetrics {
923                perplexity: 0.0,
924                coherence: 0.0,
925                factual_accuracy: 0.0,
926                logical_consistency: 0.0,
927                quantum_advantage: 0.0,
928            },
929        }
930    }
931}
932/// Quantum reasoning configuration
933#[derive(Debug, Clone)]
934pub struct QuantumReasoningConfig {
935    /// Enable quantum logical reasoning
936    pub logical_reasoning: bool,
937    /// Enable quantum causal reasoning
938    pub causal_reasoning: bool,
939    /// Enable quantum analogical reasoning
940    pub analogical_reasoning: bool,
941    /// Number of reasoning steps
942    pub reasoning_steps: usize,
943    /// Reasoning circuit depth
944    pub circuit_depth: usize,
945    /// Quantum entanglement strength for reasoning
946    pub entanglement_strength: f64,
947}
948impl QuantumReasoningConfig {
949    /// Default reasoning configuration
950    pub fn default() -> Self {
951        Self {
952            logical_reasoning: true,
953            causal_reasoning: false,
954            analogical_reasoning: false,
955            reasoning_steps: 3,
956            circuit_depth: 5,
957            entanglement_strength: 0.5,
958        }
959    }
960    /// Enhanced reasoning configuration
961    pub fn enhanced() -> Self {
962        Self {
963            logical_reasoning: true,
964            causal_reasoning: true,
965            analogical_reasoning: false,
966            reasoning_steps: 5,
967            circuit_depth: 8,
968            entanglement_strength: 0.7,
969        }
970    }
971    /// Advanced reasoning configuration
972    pub fn advanced() -> Self {
973        Self {
974            logical_reasoning: true,
975            causal_reasoning: true,
976            analogical_reasoning: true,
977            reasoning_steps: 8,
978            circuit_depth: 12,
979            entanglement_strength: 0.9,
980        }
981    }
982}
983/// Main Quantum Large Language Model
984#[derive(Debug, Clone)]
985pub struct QuantumLLM {
986    /// Model configuration
987    config: QuantumLLMConfig,
988    /// Token embedding layer
989    token_embedding: QuantumNeuralNetwork,
990    /// Core transformer
991    transformer: QuantumTransformer,
992    /// Quantum memory system
993    quantum_memory: QuantumMemorySystem,
994    /// Quantum reasoning module
995    quantum_reasoning: QuantumReasoningModule,
996    /// Language modeling head
997    lm_head: QuantumNeuralNetwork,
998    /// Vocabulary mappings
999    vocab: Vocabulary,
1000    /// Model statistics
1001    generation_stats: GenerationStatistics,
1002}
1003impl QuantumLLM {
1004    /// Create new quantum large language model
1005    pub fn new(config: QuantumLLMConfig) -> Result<Self> {
1006        let embed_layers = vec![
1007            QNNLayerType::EncodingLayer {
1008                num_features: config.vocab_size,
1009            },
1010            QNNLayerType::VariationalLayer {
1011                num_params: config.transformer_config.model_dim,
1012            },
1013            QNNLayerType::MeasurementLayer {
1014                measurement_basis: "computational".to_string(),
1015            },
1016        ];
1017        let token_embedding = QuantumNeuralNetwork::new(
1018            embed_layers,
1019            config.transformer_config.num_qubits,
1020            config.vocab_size,
1021            config.transformer_config.model_dim,
1022        )?;
1023        let transformer = QuantumTransformer::new(config.transformer_config.clone())?;
1024        let quantum_memory = QuantumMemorySystem::new(config.memory_config.clone())?;
1025        let quantum_reasoning = QuantumReasoningModule::new(config.reasoning_config.clone())?;
1026        let lm_layers = vec![
1027            QNNLayerType::EncodingLayer {
1028                num_features: config.transformer_config.model_dim,
1029            },
1030            QNNLayerType::VariationalLayer {
1031                num_params: config.vocab_size,
1032            },
1033            QNNLayerType::MeasurementLayer {
1034                measurement_basis: "computational".to_string(),
1035            },
1036        ];
1037        let lm_head = QuantumNeuralNetwork::new(
1038            lm_layers,
1039            config.transformer_config.num_qubits,
1040            config.transformer_config.model_dim,
1041            config.vocab_size,
1042        )?;
1043        let vocab = Vocabulary::new(config.vocab_size)?;
1044        let generation_stats = GenerationStatistics::new();
1045        Ok(Self {
1046            config,
1047            token_embedding,
1048            transformer,
1049            quantum_memory,
1050            quantum_reasoning,
1051            lm_head,
1052            vocab,
1053            generation_stats,
1054        })
1055    }
1056    /// Forward pass through the model
1057    pub fn forward(
1058        &mut self,
1059        input_ids: &Array2<usize>,
1060        attention_mask: Option<&Array3<bool>>,
1061        use_memory: bool,
1062        use_reasoning: bool,
1063    ) -> Result<Array3<f64>> {
1064        let (batch_size, seq_len) = input_ids.dim();
1065        if seq_len > self.config.max_context_length {
1066            return Err(MLError::ConfigurationError(format!(
1067                "Sequence length {} exceeds maximum context length {}",
1068                seq_len, self.config.max_context_length
1069            )));
1070        }
1071        let mut embeddings = Array3::zeros((
1072            batch_size,
1073            seq_len,
1074            self.config.transformer_config.model_dim,
1075        ));
1076        for batch_idx in 0..batch_size {
1077            for seq_idx in 0..seq_len {
1078                let token_id = input_ids[[batch_idx, seq_idx]];
1079                let token_embedding = self.vocab.get_embedding(token_id)?;
1080                let embedded = self.token_embedding.forward(&token_embedding)?;
1081                embeddings
1082                    .slice_mut(s![batch_idx, seq_idx, ..])
1083                    .assign(&embedded);
1084            }
1085        }
1086        if use_memory {
1087            embeddings = self.quantum_memory.enhance_embeddings(&embeddings)?;
1088        }
1089        let transformer_output = self.transformer.forward(&embeddings, attention_mask)?;
1090        let reasoned_output = if use_reasoning {
1091            self.quantum_reasoning
1092                .apply_reasoning(&transformer_output)?
1093        } else {
1094            transformer_output
1095        };
1096        let mut logits = Array3::zeros((batch_size, seq_len, self.config.vocab_size));
1097        for batch_idx in 0..batch_size {
1098            for seq_idx in 0..seq_len {
1099                let hidden_state = reasoned_output.slice(s![batch_idx, seq_idx, ..]).to_owned();
1100                let token_logits = self.lm_head.forward(&hidden_state)?;
1101                logits
1102                    .slice_mut(s![batch_idx, seq_idx, ..])
1103                    .assign(&token_logits);
1104            }
1105        }
1106        if use_memory {
1107            self.quantum_memory
1108                .update_memory(&reasoned_output, input_ids)?;
1109        }
1110        Ok(logits)
1111    }
1112    /// Generate text with quantum enhancement
1113    pub fn generate(&mut self, prompt: &str, config: GenerationConfig) -> Result<String> {
1114        let input_ids = self.vocab.tokenize(prompt)?;
1115        let mut current_ids = Array1::from_vec(input_ids);
1116        let mut generated_text = prompt.to_string();
1117        for step in 0..config.max_length {
1118            let batch_input = current_ids.clone().insert_axis(Axis(0));
1119            let input_2d = Array2::from_shape_vec((1, current_ids.len()), current_ids.to_vec())
1120                .map_err(|e| {
1121                    MLError::MLOperationError(format!("Failed to create input array: {}", e))
1122                })?;
1123            let seq_len = current_ids.len();
1124            let causal_mask = create_causal_mask(1, seq_len);
1125            let logits = self.forward(
1126                &input_2d,
1127                Some(&causal_mask),
1128                config.use_memory,
1129                config.use_quantum_reasoning,
1130            )?;
1131            let next_token_logits = logits.slice(s![0, seq_len - 1, ..]).to_owned();
1132            let final_logits = if config.use_quantum_reasoning && config.chain_of_thought {
1133                self.quantum_reasoning
1134                    .enhance_token_selection(&next_token_logits)?
1135            } else {
1136                next_token_logits
1137            };
1138            let next_token = self.sample_token(&final_logits, &config)?;
1139            if self.vocab.is_eos_token(next_token) {
1140                break;
1141            }
1142            let new_current = Array1::from_iter(
1143                current_ids
1144                    .iter()
1145                    .cloned()
1146                    .chain(std::iter::once(next_token)),
1147            );
1148            current_ids = new_current;
1149            let token_text = self.vocab.decode_token(next_token)?;
1150            generated_text.push_str(&token_text);
1151            self.generation_stats.total_tokens += 1;
1152            if step % 10 == 0 {
1153                let coherence = self.quantum_reasoning.measure_coherence()?;
1154                self.generation_stats.quantum_coherence = coherence;
1155            }
1156        }
1157        Ok(generated_text)
1158    }
1159    /// Sample next token from logits
1160    fn sample_token(&self, logits: &Array1<f64>, config: &GenerationConfig) -> Result<usize> {
1161        let mut scores = logits.clone();
1162        if config.temperature != 1.0 {
1163            scores = scores / config.temperature;
1164        }
1165        if config.repetition_penalty != 1.0 {
1166            scores = scores * config.repetition_penalty;
1167        }
1168        let max_score = scores.iter().cloned().fold(f64::NEG_INFINITY, f64::max);
1169        let exp_scores = scores.mapv(|x| (x - max_score).exp());
1170        let sum_exp = exp_scores.sum();
1171        let mut probs = exp_scores / sum_exp;
1172        if let Some(k) = config.top_k {
1173            let mut indexed_probs: Vec<(usize, f64)> =
1174                probs.iter().enumerate().map(|(i, &p)| (i, p)).collect();
1175            indexed_probs
1176                .sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
1177            for (i, &(idx, _)) in indexed_probs.iter().enumerate() {
1178                if i >= k {
1179                    probs[idx] = 0.0;
1180                }
1181            }
1182            let sum_probs = probs.sum();
1183            if sum_probs > 0.0 {
1184                probs = probs / sum_probs;
1185            }
1186        }
1187        if let Some(p) = config.top_p {
1188            let mut indexed_probs: Vec<(usize, f64)> = probs
1189                .iter()
1190                .enumerate()
1191                .map(|(i, &prob)| (i, prob))
1192                .collect();
1193            indexed_probs
1194                .sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
1195            let mut cumulative = 0.0;
1196            for (idx, prob) in &indexed_probs {
1197                cumulative += prob;
1198                if cumulative > p {
1199                    for (i, &(remaining_idx, _)) in indexed_probs.iter().enumerate() {
1200                        if cumulative - prob > p {
1201                            probs[remaining_idx] = 0.0;
1202                        }
1203                    }
1204                    break;
1205                }
1206            }
1207            let sum_probs = probs.sum();
1208            if sum_probs > 0.0 {
1209                probs = probs / sum_probs;
1210            }
1211        }
1212        let mut cumulative = 0.0;
1213        let random_val = fastrand::f64();
1214        for (i, &prob) in probs.iter().enumerate() {
1215            cumulative += prob;
1216            if random_val <= cumulative {
1217                return Ok(i);
1218            }
1219        }
1220        Ok(probs
1221            .iter()
1222            .enumerate()
1223            .max_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
1224            .map(|(i, _)| i)
1225            .unwrap_or(0))
1226    }
1227    /// Get model configuration
1228    pub fn config(&self) -> &QuantumLLMConfig {
1229        &self.config
1230    }
1231    /// Get generation statistics
1232    pub fn generation_stats(&self) -> &GenerationStatistics {
1233        &self.generation_stats
1234    }
1235    /// Calculate total model parameters
1236    pub fn num_parameters(&self) -> usize {
1237        let mut total = 0;
1238        total += self.token_embedding.parameters.len();
1239        total += self.transformer.num_parameters();
1240        total += self.lm_head.parameters.len();
1241        total += self.quantum_memory.num_parameters();
1242        total += self.quantum_reasoning.num_parameters();
1243        total += self.vocab.quantum_embeddings.len();
1244        total
1245    }
1246    /// Evaluate model perplexity on a dataset
1247    pub fn evaluate_perplexity(&mut self, texts: &[String]) -> Result<f64> {
1248        let mut total_log_likelihood = 0.0;
1249        let mut total_tokens = 0;
1250        for text in texts {
1251            let tokens = self.vocab.tokenize(text)?;
1252            if tokens.len() < 2 {
1253                continue;
1254            }
1255            let tokens_len = tokens.len();
1256            let input_ids = Array2::from_shape_vec((1, tokens_len), tokens.clone())?;
1257            let logits = self.forward(&input_ids, None, false, false)?;
1258            for i in 0..tokens_len - 1 {
1259                let target_token = tokens[i + 1];
1260                let token_logits = logits.slice(s![0, i, ..]);
1261                let max_logit = token_logits
1262                    .iter()
1263                    .cloned()
1264                    .fold(f64::NEG_INFINITY, f64::max);
1265                let exp_logits = token_logits.mapv(|x| (x - max_logit).exp());
1266                let sum_exp = exp_logits.sum();
1267                let prob = exp_logits[target_token] / sum_exp;
1268                if prob > 1e-10 {
1269                    total_log_likelihood += prob.ln();
1270                    total_tokens += 1;
1271                }
1272            }
1273        }
1274        if total_tokens == 0 {
1275            return Ok(f64::INFINITY);
1276        }
1277        let avg_log_likelihood = total_log_likelihood / total_tokens as f64;
1278        let perplexity = (-avg_log_likelihood).exp();
1279        Ok(perplexity)
1280    }
1281}
1282/// Quantum episodic memory
1283#[derive(Debug, Clone)]
1284pub struct QuantumEpisode {
1285    /// Episode context
1286    context: Array1<f64>,
1287    /// Episode content
1288    content: Array2<f64>,
1289    /// Quantum state representation
1290    quantum_state: Array1<f64>,
1291    /// Episode timestamp
1292    timestamp: f64,
1293    /// Coherence measure
1294    coherence: f64,
1295    /// Episode importance
1296    importance: f64,
1297}
quantrs2_ml/quantum_llm/types.rs

quantrs2_ml/quantum_llm/
types.rs
