Technology
Quantum + AI + Agents
The technical foundation of Qaankunka's visual intelligence systems — combining quantum computing, artificial intelligence, and agentic orchestration.
Quantum Computing
Quantum computational approaches
Exploring quantum machine learning, optimization, annealing, and hybrid quantum-classical architectures for visual problems.
Quantum Machine Learning
Investigating how quantum circuits and hybrid architectures can contribute to visual learning and pattern recognition.
Quantum Optimization
Exploring quantum and quantum-inspired methods for combinatorial visual problems with large search spaces.
Quantum Annealing
Evaluating annealing-based approaches for suitable visual optimisation formulations.
Adiabatic Computing
Researching adiabatic quantum computing for specific visual representation problems.
QUBO Formulations
Developing Quadratic Unconstrained Binary Optimization models for visual constraint problems.
Ising Models
Applying Ising model formulations to pixel-level and feature-level visual optimisation.
Hybrid Quantum-Classical
Building architectures where quantum and classical computation work together for visual intelligence.
Artificial Intelligence
AI for visual intelligence
Machine learning, computer vision, generative AI, and image & video intelligence capabilities.
Machine Learning
Classical and deep learning approaches for visual data analysis, classification, and prediction.
Computer Vision
Image and video understanding through feature extraction, detection, segmentation, and scene analysis.
Generative AI
AI-assisted creation of images and video through diffusion models, transformers, and hybrid approaches.
Image Intelligence
Comprehensive image understanding, transformation, and optimisation capabilities.
Video Intelligence
Temporal reasoning, motion analysis, and frame-to-frame consistency for video media.
Agentic Systems
Intelligent coordination
Multi-agent orchestration for decomposing complex visual objectives and coordinating specialised capabilities.
Agent Orchestration
Coordinating multiple specialised agents to decompose and execute complex visual objectives.
Planning
Strategic task decomposition and workflow planning for multi-step visual pipelines.
Reasoning
Logical inference over visual constraints, creative goals, and computational resources.
Task Decomposition
Breaking complex visual objectives into manageable sub-tasks for specialised agents.
Tool Selection
Dynamic selection of AI models, optimisation engines, and computational backends.
Workflow Coordination
Managing data flow, dependencies, and quality gates across the visual intelligence pipeline.
Hybrid Architecture
System architecture
How quantum, AI, and agentic layers combine to deliver visual intelligence for imagery and video.
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