| name | quantum-neuromorphic-computing |
| description | Quantum neuromorphic computing framework combining quantum gates, memristive synapses, and quantum cognition for decision making. Use when: (1) analyzing quantum brain models, (2) implementing quantum neural networks, (3) studying quantum cognition mechanisms, (4) exploring memristive quantum synapses, (5) simulating quantum neuromorphic systems. |
Quantum Neuromorphic Computing
Framework for quantum-enhanced neuromorphic computing, combining insights from quantum cognition theory, quantum brain models, and memristive quantum synapses.
Activation Keywords
- quantum neuromorphic
- 量子神经形态
- quantum brain model
- quantum cognition
- memristive quantum
- quantum synapse
- quantum reservoir computing
- quantum extreme learning
Tools Used
exec: Run kg_tool for knowledge graph operations, Python scripts for simulation
read: Load skill references, paper abstracts, configuration files
write: Save analysis results, simulation outputs, configuration files
Core Concepts
Quantum Cognition
Decision-making framework using quantum probability to model cognitive processes. Key features:
- Non-commutative measurements: Order-dependent outcomes (contextuality)
- Superposition states: Multiple cognitive states simultaneously
- Entanglement: Correlated cognitive subsystems
- Interference effects: Probability interference in decision outcomes
Quantum Brain Models
Theoretical models treating neural dynamics as quantum processes:
- Lipkin-Meshkov-Glick (LMG) model: Collective spin dynamics with synaptic feedback
- Phase transitions: Paramagnetic/ferromagnetic states modulated by synaptic plasticity
- Husimi distribution: Phase-space representation of quantum states
- Wehrl entropy: Measure of localization and phase-space deformation
Memristive Quantum Synapses
Quantum gates exhibiting memristive behavior:
- Pinched hysteresis loop: Memory-dependent conductance
- Long-term plasticity: Quantum state-encoded synaptic weights
- Ohm's law: Quantum conductance behavior
- Universal quantum computing: Three-layer memristive quantum neural networks
Workflow
Phase 1: Knowledge Retrieval
- Search knowledge graph:
kg_tool search kg.db "quantum neural"
kg_tool search kg.db "quantum cognition"
kg_tool search kg.db "memristive quantum"
- Find related papers:
kg_tool similar kg.db <entity_id> 10
- Get PageRank important papers:
kg_tool pagerank kg.db
Phase 2: Analysis
Run quantum cognition analysis script:
python3 ~/.openclaw/skills/quantum-neuromorphic-computing/scripts/quantum_cognition_analysis.py --input <paper_id> --kg kg.db
Phase 3: Simulation
For quantum brain model simulations:
python3 ~/.openclaw/skills/quantum-neuromorphic-computing/scripts/quantum_brain_simulation.py --model lmg --feedback synaptic
Key Research Papers
From knowledge graph analysis (kg.db):
-
Extreme Quantum Cognition Machines (2603.05430): Quantum learning architectures for deliberative decision making with dynamical attention mechanism
-
Quantum Brain Model with Synaptic Feedback (2603.03345): LMG model showing how synaptic plasticity modulates phase transitions
-
Memristive Synapses on Quantum Computer (2007.09574): Quantum gates with memristive behavior for neuromorphic computing
Hybrid Spiking-Quantum Architectures (2026)
-
SPATE (2604.11022, IJCNN 2026): Spiking-Phase Adaptive Temporal Encoding — converts real-valued features into LIF spike trains, maps spike statistics to quantum rotations with temporal qubits. CKTA 0.966 vs 0.632 (angle encoding). See references/spiking-quantum-encoding.md for methodology details.
-
SQDR-CNN (2512.03895, PeerJ CS 2026): Spiking-Quantum Data Re-upload CNN — end-to-end joint training of convolutional SNNs + quantum circuits via surrogate gradients + data re-uploading. 86% SOTA accuracy at 0.5% parameters. See references/spiking-quantum-cnn.md.
-
Q-SpiRL (2605.20801): Quantum Spiking Reinforcement Learning for robot navigation — QSNN achieves 99% success rate on 40x40 grid worlds, deployed on IBM quantum hardware.
-
Stochastic QNN (2511.11609): Stochastic quantum neural networks with qubits evolving via stochastic differential equations inspired by biological neuronal processes.
Spiking-Quantum Hybrid Methodologies
SPATE: Spike-to-Phase Encoding
Core pipeline: Features → LIF spike trains → spike statistics → quantum rotation angles + temporal qubits
Steps:
- Normalize features to [0,1]
- Convert to LIF spike trains: τ·dV/dt = -(V - V_rest) + R·I(t), spike when V > V_threshold
- Extract statistics: firing rate, mean ISI, coefficient of variation
- Map to R_z(θ) rotation gates with controlled phase operations on temporal qubits
- Feed into variational quantum circuit
Evaluation protocol: CKTA, Fisher separability, silhouette score, normalized entropy, TVpair collapse — assess encoding quality independently of classifier.
Pitfalls:
- Spike train length trade-off: too short loses temporal info, too long → decoherence
- Each temporal qubit doubles circuit depth — use sparingly
- LIF τ and V_threshold must be calibrated per dataset
SQDR-CNN: Joint SNN-Quantum Training
Core innovation: Surrogate gradient + quantum data-reupload enables end-to-end backprop without pretrained SNN encoders.
Architecture: Input → ConvSNN → Flatten spikes → Data Re-upload Layers → Measurement
Key principles:
- Surrogate gradient: smooth approximation of Heaviside for spike backprop
- Quantum data-reupload: N re-uploads ≈ N-qubit expressivity on single qubit
- Hybrid optimizer: Adam for classical, parameter-shift for quantum
Pitfalls:
- Surrogate gradient choice (sigmoid/arctan/triangle) critically affects stability
- Temporal steps: too few → poor SNN dynamics; too many → slow training
- Feature-to-qubit mismatch requires dimensionality reduction
References
For detailed theoretical background:
- Quantum cognition: See
references/quantum_cognition.md
- Quantum brain models: See
references/quantum_brain_models.md
- Memristive quantum: See
references/memristive_quantum.md
- Quantum-classical bridging patterns: See
references/quantum-classical-bridging.md — DBM-NQS for spin glasses, thermodynamic networks, Born-rule DQPT analysis, Leggett-Garg neural tests
Error Handling
kg_tool not found
cd /path/to/sqlite-knowledge-graph
cargo build --release
Embedding generation fails
- Ensure
sentence-transformers installed: pip install sentence-transformers
- Check kg_vectors table exists:
sqlite3 kg.db "SELECT COUNT(*) FROM kg_vectors"
Simulation convergence issues
- Reduce model complexity (fewer spins)
- Increase simulation time steps
- Adjust feedback coupling strength
Applications
- Decision making: Quantum cognition models for deliberative inference
- Sequence analysis: Quantum reservoir computing for temporal patterns
- Anomaly detection: Quantum extreme learning for classification
- Brain modeling: Understanding synaptic plasticity through quantum dynamics
- Hardware implementation: Memristive quantum gates for neuromorphic hardware
Notes
- This skill bridges neuroscience and quantum computing
- Focuses on theoretical models with potential hardware implementations
- Uses knowledge graph (kg.db) for paper retrieval and analysis
- Supports both analysis and simulation workflows