Neuro-𝑸uantis™
quantum‑inspired neural architectures
AI That’s Smaller, Faster & More Efficient
parameter reduction 50‑80%
inference speed 1.5‑3x
energy savings 40‑60%
Compression strength:
70%
Model family:
MLPerf · ImageNet · CIFAR-10
parameter reduction by model family
inference acceleration (slope chart)
energy efficiency heat scatter
accuracy vs parameter count
layer‑wise parameter distribution (ResNet)
edge deployment (2GB RAM) inference speed
compression ratio distribution across models
Neuro-𝑸uantis™ performance benchmarks
| metric |
traditional baseline |
Neuro-𝑸uantis |
improvement |
Validated on ImageNet, CIFAR-10, OpenGraphBench
MLPerf, DeepSpeed, TT‑Format, Tucker, CP
⚡ Neuro-𝑸uantis optimized performance
- 50‑80% parameter reduction – QTN compression
- 1.5‑3× inference speedup on classical hardware
- 40‑60% energy savings – lower FLOPS/W
- ≥95% accuracy retained after compression
traditional AI baseline
- model size grows exponentially with depth
- large‑scale GPU/TPU clusters required
- specialized acceleration hardware needed
- high training/inference costs
Quantum Tensor Networks
50‑80% parameter reduction, ≥95% accuracy.
Energy‑Efficient Learning
40‑60% lower power, low‑rank decomposition.
Scalable Expressivity
Outperforms pruning, retains predictive power.
optimized deployment
CPU/GPU/TPU/FPGA
Edge (2GB RAM)
TensorFlow Lite, NVIDIA Jetson
✔ Quantum‑inspired tensor compression, real‑time edge execution.
Quantum Graph Neural Nets
5Ă— scalability gains.
AI‑Powered Quantum Circuit
40% training cost reduction.
Scalable Quantum AI
Enterprise‑grade efficiency.