Live System Simulation
Hybrid Quantum-Classical Video Generation Interface
I2V Input Parameters
Executive Summary
This document defines a complete rebuild of Alibaba's WAN 2.1 video generation system with a revolutionary web-based quantum compute backend.
The system replaces traditional GPU inference with a hybrid quantum-classical architecture running entirely in browser using WebGPU and Qiskit-powered WebWorkers. The core innovation is a novel interface where real quantum circuit evaluations directly influence the diffusion model's latent space, creating a unique AI system where quantum superposition can directly affect generative outputs.
Core Objectives
- Reproduce WAN 2.1's video generation capabilities in a web browser.
- Leverage quantum computing for unique generative variability.
- Implement a privacy-first, high-performance video generator on consumer hardware.
System Architecture Overview
The quantum-enhanced WAN 2.1 system is composed of four main layers orchestrated to deliver the final video output.
1. Browser Client Layer
User-facing front-end running in the browser.
- UI Canvas: Control center for prompt input and parameter adjustment.
- Quantum Visualizer: Real-time display of quantum circuits and state vectors.
- Video Player: Embedded player for generated results.
- Main Thread Orchestrator: Manages state and coordinates visualization.
2. Quantum Compute Backend
Core system leveraging quantum computing for generative influence.
- WebWorker Pool: 4-8 parallel workers for circuit simulation.
- Qiskit.js: Circuit builder for custom gate sequences.
- WebGPU Engine: Accelerates 512D state vector evolution using WGSL shaders.
- State Analyzer: Computes entanglement entropy and fidelity.
3. Web Backend Server
Orchestration and classical deep learning inference (Node.js/Python).
- REST API Gateway: Handles requests and authentication.
- Quantum-Classical Bridge: Translates quantum features into diffusion parameters.
- WAN 2.1 Engine: Distributed inference layer for T5 encoder and VAE/Diffusion models.
4. Storage & Cache
Persistence layer for models and results.
- Redis Cache: In-memory storage for model weights.
- S3/Minio: Durable storage for generated videos.
- Circuit Library: Repository of pre-defined quantum circuits.
Quantum Compute Backend Specification
512-Dimensional Quantum State Architecture
Operating on a 512-qubit system requires sophisticated memory management. The system uses a sparse representation strategy:
Qiskit.js Integration
The QuantumComputeEngine class orchestrates the backend:
- Initialization: Sets up WebGPU device and 16-bit floating point support.
- Circuit Building: Constructs circuits via Qiskit.js API (Gates: H, CNOT, RY, etc.).
- Compilation: Generates optimized WGSL shaders for the specific gate sequence.
- Execution: Dispatches compute shaders to GPU for parallel state evolution.
- Analysis: Extracts metrics (Entropy, Fidelity) for the Bridge.
Development & Optimization
Quantum-Classical Bridge
The critical link between quantum randomness and creative output.
- Feature Extraction: Pulls entropy and phase data from the quantum state.
- Adaptive Sampling: Dynamically adjusts quantum influence based on generation complexity.
- Injection Pipeline: Modifies Text Encoder Latents, modulates Diffusion Noise, or alters VAE Decoding.
Optimization Strategies
- WebGPU Acceleration: Parallel matrix operations on consumer GPUs.
- Circuit Decomposition: simplifying gates before compilation.
- Hybrid Inference: Interleaving CPU classical tasks with GPU quantum tasks.
- ONNX Runtime: Optimized execution for the classical diffusion model.