quantum-neuromorphic-computing

name

quantum-neuromorphic-computing

description

Quantum neuromorphic computing framework combining quantum gates, memristive synapses, quantum cognition, optical quantum neurons, and neuromorphic quantum kernels. 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, (6) building HOM interference-based optical quantum neurons, (7) comparing neuromorphic vs parameterized quantum kernels for clustering.

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

1. Search knowledge graph:

kg_tool search kg.db "quantum neural"
kg_tool search kg.db "quantum cognition"
kg_tool search kg.db "memristive quantum"

2. Find related papers:

kg_tool similar kg.db <entity_id> 10

3. 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):

1. Extreme Quantum Cognition Machines (2603.05430): Quantum learning architectures for deliberative decision making with dynamical attention mechanism

2. Quantum Brain Model with Synaptic Feedback (2603.03345): LMG model showing how synaptic plasticity modulates phase transitions

3. Memristive Synapses on Quantum Computer (2007.09574): Quantum gates with memristive behavior for neuromorphic computing

Hybrid Spiking-Quantum Architectures (2026)

4. 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.

5. 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.

6. 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.

7. Quantum-Attention Hebbian (2606.02098): Derives softmax-weighted Hebbian learning rules from quantum probability flow in associative memory. Imaginary-time dynamics → log-sum-exp free energy gradient → attention-like updates. Validated on D-Wave annealers — softmax rule outperforms Lorentzian power law. Gradient-free, local updates only. See references/quantum-attention-hebbian.md.

Spiking-Quantum Hybrid Methodologies

SPATE: Spike-to-Phase Encoding

Core pipeline: Features → LIF spike trains → spike statistics → quantum rotation angles + temporal qubits

Steps:

1. Normalize features to [0,1]
2. Convert to LIF spike trains: τ·dV/dt = -(V - V_rest) + R·I(t), spike when V > V_threshold
3. Extract statistics: firing rate, mean ISI, coefficient of variation
4. Map to R_z(θ) rotation gates with controlled phase operations on temporal qubits
5. 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

SPATE: Spike-to-Phase Encoding

Core pipeline: Features → LIF spike trains → spike statistics → quantum rotation angles + temporal qubits

Steps:

1. Normalize features to [0,1]
2. Convert to LIF spike trains: τ·dV/dt = -(V - V_rest) + R·I(t), spike when V > V_threshold
3. Extract statistics: firing rate, mean ISI, coefficient of variation
4. Map to R_z(θ) rotation gates with controlled phase operations on temporal qubits
5. 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

Hardware-Level Quantum Optical Neurons (2026)

9. Quantum Optical Neuron (2603.28879) — Camera-free image classification via Hong-Ou-Mandel interference of spatially programmable single photons. Two-photon coincidences directly measure image-template overlap, replacing pixel-resolved acquisition with a single global measurement. Performance is resolution-independent under fixed measurement budget. Strong noise robustness from quantum interference. See references/quantum-optical-neuron-2603.28879.md.

10. Neuromorphic Quantum Kernels (2507.07018) — Comparing parameterized quantum kernels (pQK, angle-encoding + grid-search alignment) with QLIF neuromorphic kernels (population coding → spike trains → Victor-Purpura/van Rossum temporal metrics) for spectral clustering. Trade-off: QLIF wins on low-dim data (Iris, synthetic), pQK wins on high-dim (SDSS).

11. Neuromorphic Quantum-Cognitive Transform (2503.07681) — Tutorial for transforming feedforward NN, RNN, ESN reservoir, Bayesian NN into quantum-inspired neuromorphic models using standard laptop quantum simulators. Enables context-dependent processing, interference-based decisions, and non-commutative operations.

Hardware: Quantum Optical Neurons

Hong-Ou-Mandel Interference as Physical-Layer Inner Product

Core insight: Two-photon coincidence rate at a beam splitter directly computes |⟨ψ_in|ψ_template⟩|² — the squared overlap between an input image mode and a learned template. This replaces pixel-by-pixel acquisition with a single global measurement.

Architecture:

1. Image encoding: Spatial light modulator (SLM) programs photon spatial mode
2. Template preparation: Programmable beam splitter with learned weights
3. HOM interference: Two photons interfere; coincidence rate = similarity score
4. Classification: Maximum coincidence rate across templates → predicted class

Key advantages over classical:

• Resolution independence: fixed measurement budget works at any input resolution
• Photon efficiency: operates in photon-starved regimes where classical cameras fail
• Hardware simplicity: single beam splitter + SPADs vs. full pixel array + ADC

Scaling to networks: Cascade multiple HOM interferometers with tunable beam splitters as synaptic weights for shallow quantum neural networks.

Neuromorphic Quantum Kernel Selection

Decision rule for spectral clustering:

• Feature dim < 10: use QLIF (Victor-Purpura or van Rossum kernel on spike trains)
• Feature dim > 50: use pQK (angle-encoding with grid-search-optimized rotations)
• Intermediate: test both, select via kernel-target alignment score

QLIF pipeline: tabular data → population coding → spike trains → temporal distance metric → kernel matrix → spectral clustering pQK pipeline: tabular data → angle encoding → parametric circuit → kernel matrix → spectral clustering

Cognitive Transform Recipes

See references/neuromorphic-quantum-cognitive-transform.md for detailed transformation recipes from classical to quantum-inspired architectures.

References

Recent Paradigms

• Quantum Hyperdimensional Computing (QHDC) (arXiv:2511.12664) — brain-inspired HDC maps natively to quantum primitives: LCU+OAA bundling, phase oracle binding, QFT permutation, Hadamard similarity. Validated on 156-qubit IBM Heron r3. See references/quantum-hyperdimensional-computing-2511.12664.md
• Stochastic Quantum Spiking Neural Networks (SQSNN) (arXiv:2506.21324) — multi-qubit quantum memory neurons with single-shot inference and local learning rules (no backprop). See references/stochastic-quantum-spiking-2506.21324.md
• Thermocoherent Cognitive Dynamics (arXiv:2604.04069) — physical basis of information flow in neural matter via thermocoherent effect

For detailed theoretical background:

• Quantum-SNN fusion methodology: see quantum-snn-fusion skill (arXiv: 2606.07657, 2606.03517, 2606.09734)
• QDS-SNN: Quantum deeply-supervised spiking networks with TSA-LIF neurons and QACM
• Butterfly architecture: O(n log n) scalable QNN training
• QUIVER: Forward gradient estimation for parameterized quantum circuits
• June 2026 papers detail: see references/quantum-snn-papers-2026-06.md
• 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

# Build kg_tool if missing
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

1. Decision making: Quantum cognition models for deliberative inference
2. Sequence analysis: Quantum reservoir computing for temporal patterns
3. Anomaly detection: Quantum extreme learning for classification
4. Brain modeling: Understanding synaptic plasticity through quantum dynamics
5. Hardware implementation: Memristive quantum gates for neuromorphic hardware

Notes

• This skill bridges neuroscience and quantum computing

Pitfall: NISQ Gradient Bottleneck

Parameter-shift gradient estimation scales quadratically with parameters, making hardware QNN training impractical beyond ~20 qubits. Use QUIVER forward gradient estimators (O(1) per direction) or Butterfly architecture (O(log n) per step) to scale to 60+ qubits.

Pitfall: SNN Vanishing Gradients

Standard SNNs with LIF neurons suffer from vanishing gradients during backprop through time. Use TSA-LIF (Temporally and Spatially Adaptive LIF) neurons or deep supervision via quantum-assisted modules (QACM) to maintain gradient flow with as few as 6 time steps.

• Focuses on theoretical models with potential hardware implementations
• Uses knowledge graph (kg.db) for paper retrieval and analysis
• Supports both analysis and simulation workflows