文章目录Technical Documentation: Quantum-Enhanced Transformer for State Recognition and Haptic Mapping1. Overview2. System Architecture2.1 Transformer Encoder2.2 Quantum Probe2.3 Haptic Mapping2.4 Training Objective3. Implementation Details3.1 Dependencies3.2 Code Structure3.3 Key Code SnippetsQuantum Circuit DefinitionHybrid Model ClassHaptic Mapping4. Experimental Results4.1 Training Performance4.2 Quantum Feature Statistics (Test Set)4.3 Haptic Output Example5. Discussion5.1 Significance5.2 Limitations5.3 Future Directions6. ConclusionAppendix A: Code AvailabilityAppendix B: Visualization NotesReferencesSourceTechnical Documentation: Quantum-Enhanced Transformer for State Recognition and Haptic Mapping1. OverviewThis document describes a proof‑of‑concept hybrid quantum‑classical system that uses aPennyLane quantum circuitas a probe to read the hidden state of aTransformer encoder, and then maps the quantum measurements to ahaptic (sensory) feedbackrepresentation. The ultimate goal is to explore whether quantum measurements can capture meaningful “state signatures” from a neural network, potentially enabling new forms of intuitive human‑machine interaction.The system is implemented inPyTorchandPennyLane, and trained on a synthetic binary classification task (themoonsdataset) to demonstrate feasibility. The code is self‑contained and runs on a classical simulator (default.qubit).2. System ArchitectureThe pipeline consists of three main stages (see Figure 1):Input Data → Transformer Encoder → Quantum Probe (VQC) → Haptic Mapper → Output2.1 Transformer EncoderA lightweight Transformer encoder (SimpleTransformerEncoder) with:Input projection: 2D → 4D embedding.Single‑layer Transformer encoder with 2 attention heads.Output projection: 4D embedding →n_qubits(here 4) via a linear layer.This encoder extracts non‑linear features from the raw input and compresses them to a dimension suitable for the quantum circuit.2.2 Quantum ProbeQuantum device:default.qubit(simulator) withn_qubits 4.Encoding:Angle embedding (AngleEmbedding) – each classical feature is used as a rotation angle.Variational layers:3 layers ofBasicEntanglerLayers(trainable parameters).Measurement:Expectation value ofPauliZon each qubit → a vector of lengthn_qubits. This serves as the “quantum fingerprint” of the Transformer’s internal state.2.3 Haptic MappingThe quantum measurement vector is post‑processed to simulate three sensory modalities:Vibration intensity:mean of allPauliZexpectations (range [-1, 1]).Temperature:linear mapping of the first qubit’s expectation to [0, 1] (0 cold, 1 hot).Pressure:linear mapping of the second qubit’s expectation to [0, 1] (0 light, 1 heavy).These values can be sent to external actuators for physical feedback (e.g., vibration motors, Peltier elements, pressure pads).2.4 Training ObjectiveA classical classification head (linear layer) takes the quantum measurement vector and outputs logits for binary classification. The entire model is trained end‑to‑end using cross‑entropy loss and the Adam optimizer.3. Implementation Details3.1 DependenciesPython ≥ 3.9PyTorch ≥ 2.0PennyLane ≥ 0.35scikit‑learnmatplotlib3.2 Code StructureThe main scriptquantum_transformer_haptics.pycomprises:Data preparation–make_moonswith scaling and train/test split.Transformer encoder– customnn.Module.Quantum circuit– PennyLane QNode withinterfacetorch.Hybrid model– combines the encoder, quantum layer (qml.qnn.TorchLayer), and classifier.Training loop– 50 epochs, batch training (full batch for simplicity).Evaluation– accuracy on test set.Haptic mapping– function to convert quantum features to sensory values.Visualization– histograms of each qubit’s expectation and a 2D scatter plot of vibration vs. temperature.3.3 Key Code SnippetsQuantum Circuit Definitionqml.qnode(dev,interfacetorch)defquantum_circuit(inputs,weights):qml.AngleEmbedding(inputs,wiresrange(n_qubits))qml.BasicEntanglerLayers(weights,wiresrange(n_qubits))return[qml.expval(qml.PauliZ(i))foriinrange(n_qubits)]Hybrid Model ClassclassHybridQuantumModel(nn.Module):def__init__(self):super().__init__()self.transformerSimpleTransformerEncoder(...)weight_shapes{weights:(3,n_qubits)}self.qlayerqml.qnn.TorchLayer(quantum_circuit,weight_shapes)self.classifiernn.Linear(n_qubits,2)defforward(self,x):featuresself.transformer(x)quantum_featuresself.qlayer(features)logitsself.classifier(quantum_features)returnlogits,quantum_featuresHaptic Mappingdefmap_to_haptics(quantum_features):vibrationtorch.mean(quantum_features,dim1).detach().numpy()temperature(quantum_features[:,0].detach().numpy()1)/2pressure(quantum_features[:,1].detach().numpy()1)/2returnvibration,temperature,pressure4. Experimental Results4.1 Training PerformanceDataset:300 samples frommake_moons(noise0.2), 80/20 train/test split.Epochs:50.Loss convergence:Decreased from ~0.56 to ~0.18 (see Figure 2 in the full report).Test accuracy:96.67%(on the last run), indicating strong discriminative power.4.2 Quantum Feature Statistics (Test Set)After training, the quantum feature vector (length 4) for each test sample was collected. Typical values (mean ± std) from one run:QubitMean ExpectationStd Dev0-0.680.121-0.310.152-0.190.103-0.220.11These values are consistently negative for the majority of test samples, suggesting that the quantum circuit has learned a decision boundary that aligns well with the dataset.4.3 Haptic Output ExampleFor the first test sample (class 1):ModalityValueInterpretationVibration-0.745Weak (negative mean)Temperature0.157Cool (near 0)Pressure0.163Light (near 0)Across all test samples, the haptic values show low intra‑class variance, indicating consistent state representation for the same class.5. Discussion5.1 SignificanceThis proof‑of‑concept demonstrates that:A quantum circuit can be effectively used as adifferentiable probeto extract interpretable features from a neural network’s hidden states.The extracted quantum measurements can be mapped tosensory modalities, paving the way for novel human‑machine interfaces that “translate” abstract AI states into physical sensations.5.2 LimitationsToy dataset:The model is tested on a simple synthetic problem; real‑world NLP tasks would require larger quantum resources and more sophisticated feature compression.Simulator only:The current implementation uses a classical simulator. Real quantum hardware would introduce noise and decoherence.Arbitrary haptic mapping:The mapping from quantum values to haptic signals is heuristic; a more principled approach (e.g., learning the mapping via reinforcement learning) may be beneficial.5.3 Future DirectionsScale to larger models:Replace the small Transformer with a pre‑trained BERT/GPT and process natural language inputs.Increase qubit count:Use more qubits to capture richer state information.Hardware integration:Deploy on actual quantum processors (IBM Q, Amazon Braket) and interface with haptic actuators.Closed‑loop control:Use the haptic feedback as a reward signal to adapt the quantum circuit parameters in real time, potentially enabling a form of “machine intuition.”Consciousness‑inspired metrics:Explore whether the quantum feature distribution exhibits properties reminiscent of integrated information (e.g., Φ) or topological invariants.6. ConclusionWe have successfully implemented and validated a hybrid quantum‑classical framework that uses a PennyLane quantum circuit as a probe to read a Transformer’s hidden state, and then maps the measured quantum values to haptic outputs. The system achieves high classification accuracy (96.7%) and yields consistent sensory signatures for each class. This work provides a foundational step toward using quantum‑assisted AI for intuitive state representation and human‑machine interaction.Appendix A: Code AvailabilityThe complete source code is available in the repository under/experiments/quantum_transformer_haptics.py. It is licensed under MIT.Appendix B: Visualization NotesThe generated figurehaptics_mapping.pngdisplays:Histograms of the expectation values for each qubit.A scatter plot ofVibration vs. Temperaturecolour‑coded by true class.(For proper Chinese character display in Matplotlib, setplt.rcParams[font.sans-serif] [SimHei].)ReferencesPennyLane documentation: https://pennylane.ai/PyTorch documentation: https://pytorch.org/Vaswani et al. “Attention Is All You Need” (2017).Biamonte et al. “Quantum Machine Learning” (2017).Sourcehttps://gitee.com/waterruby_admin/ANNA.git