Quantum Hyperdimensional Computing

---
name: quantum-hyperdimensional-computing
description: "Quantum-enhanced Hyperdimensional Computing (HDC) framework using quantum binding operations and SuperClass Construction for robust, efficient high-dimensional vector space computation."
trigger: hyperdimensional computing, HDC, quantum HDC, vector symbolic architecture, quantum binding, SuperClass, QeHDC
category: quantum-computing
---

# Quantum Hyperdimensional Computing

## Description
Hyperdimensional Computing (HDC) is a robust computational framework inspired by human cognition characterized by simple and efficient operations within high-dimensional vector spaces. This skill implements quantum-enhanced HDC using quantum binding operations and SuperClass Construction for improved computational efficiency and robustness.

## Activation Keywords
- hyperdimensional computing
- HDC quantum enhancement
- vector symbolic architecture
- quantum binding operations
- SuperClass construction
- quantum HDC
- QeHDC
- 超维计算量子增强
- 量子超维计算

## Tools Used
- terminal: Run quantum HDC simulation scripts
- read_file: Read HDC algorithm implementations and quantum circuit designs
- write_file: Save HDC model configurations and experimental results
- web_search: Search for latest HDC and quantum computing research

## Usage Patterns

### Quantum-Enhanced Pattern Recognition
Use HDC for pattern recognition tasks with quantum binding operations for improved efficiency.

### Cognitive-Inspired Computing
Use when building systems inspired by human cognition using high-dimensional vector representations.

### Quantum-Classical Hybrid Computing
Use when combining quantum and classical computing resources for HDC operations.

## Instructions for Agents

### Step 1: Define HDC Vector Space
- Choose the dimensionality of the hyperdimensional vectors (typically 10,000+ dimensions)
- Select the vector representation type:
  - Binary vectors (for classical HDC)
  - Quantum state vectors (for quantum-enhanced HDC)
  - Complex-valued vectors (for phase-based operations)

### Step 2: Implement Binding Operations
- For classical HDC: Use element-wise multiplication or circular convolution
- For quantum-enhanced HDC: Implement quantum binding using:
  - Quantum superposition for parallel binding
  - Quantum entanglement for associative binding
  - SuperClass Construction for hierarchical representations

### Step 3: Build SuperClass Hierarchy
- Define the SuperClass structure for organizing concepts hierarchically
- Implement binding operations between different hierarchy levels
- Ensure proper unbinding (inverse) operations for retrieval

### Step 4: Train and Evaluate HDC Model
- Encode training data into hyperdimensional vectors
- Use quantum-enhanced binding for associative memory operations
- Evaluate model performance on pattern recognition tasks

## Error Handling

### Quantum Resource Limitations
If quantum resources are limited:
- Use classical HDC as fallback with approximate quantum operations
- Implement hybrid quantum-classical approach using only critical quantum operations
- Consider using quantum simulators for development

### High Dimensionality Issues
If vector dimensionality causes computational issues:
- Use dimensionality reduction techniques while preserving HDC properties
- Implement sparse vector representations
- Consider using structured HDC (e.g., Fourier HDC) for efficiency

## Resources
- **Reference Paper**: "QeHDC: Hyperdimensional Computing based on Quantum-enhanced binding and SuperClass Construction" (arXiv:2606.22421)
- **HDC Frameworks**: hyperdim, HDC-ML for classical implementations
- **Quantum Libraries**: Qiskit, Cirq for quantum circuit implementation

## Related Skills
- quantum-ml-patterns
- hyperdimensional-stdp-computing
- quantum-neuromorphic-computing