I’m building a hybrid system that combines classical AI with quantum-inspired methods to achieve high efficiency, scalability, and robustness. Unlike traditional pipelines, my design adds a quantum encoder + compression stage up front and a refinement/error-correction loop at the end, which together reduce cost, improve accuracy, and make the model more production-friendly.
This project is an active work in progress. My goal is to show how quantum-inspired representations can reshape the economics of AI by delivering stronger performance with fewer resources.
I convert raw input into tokens—structured units the rest of the system can handle consistently.
I encode tokens into a graph-like, qubit-inspired state (e.g., parameterized gates and entanglement structure).
This creates a compact, information-dense representation with two aims:
- Preserve salient structure while enabling reversible mapping back to the original domain.
- Expose topology (graph structure) that downstream components can compress and reason about.
I apply compression on the encoded graph/state to remove redundancy (e.g., pruning weak edges/entanglements and merging equivalent substructures) while tracking fidelity metrics.
Result: a smaller, cheaper latent that retains the signal I care about.
I run a diffusion-style transformer in latent space.
Operating on the compressed representation lets the model:
- Capture long-range dependencies efficiently.
- Train/infer with lower compute and memory than raw-space approaches.
I map the DiT’s latent back onto the quantum-style graph/state so I can:
- Enforce cycle-consistency with the encoder.
- Recover structure needed to reconstruct outputs or pass to downstream tasks.
I estimate reconstruction error (and diffusion noise mismatch) and run a controller (RNN-style) to adjust encoder/decoder/compression parameters.
This closed loop reduces artifacts, stabilizes training, and improves final accuracy over successive passes.
I decode the refined graph/state back to the target domain, producing the final output.
- Efficiency: Compressing before modeling means fewer FLOPs, less memory pressure, and faster training/serving—without sacrificing accuracy.
- Scalability: The latent+graph approach scales to larger datasets and longer contexts with sub-linear cost growth relative to raw-space methods.
- Resilience: Noise injection plus the error-correction loop makes the system more robust to imperfect or shifting data.
- Differentiation: Blending latent diffusion with a quantum-style encoder/decoder and graph compression goes beyond classical pipelines.
- Lower infra cost per task: More throughput on the same hardware footprint.
- Faster iteration cycles: Shorter experiment loops, enabling quicker product improvements.
- Deployability: A smaller, more stable model is easier to operate, monitor, and scale in production.
- Defensibility: The hybrid architecture and compression+refinement loop create clear technical differentiation that’s hard to replicate.
Status (WIP):
- ✅ Core tokenization, noise handling, normalization/regression implemented.
- ✅ DiT backbone training loop running in latent space.
- ✅ Quantum Encoder prototype generating graph/qbit-style states.
- ✅ Compression/pruning pass with fidelity tracking in early testing.
- 🔄 Quantum Decoder + cycle-consistency constraints under active tuning.
- 🔄 Error estimation + RNN controller loop being optimized for stability and convergence.
Next Steps:
- 📊 Benchmark end-to-end cost vs. accuracy against strong classical baselines.
- 🧪 Harden the compression criteria (learned + rule-based) and improve fidelity metrics.
- 🔁 Tighten the encoder/decoder loop for better reversibility and fewer artifacts.
- 🚀 Extend to enterprise-scale datasets and publish performance/efficiency results.