⚛️ PyTorch-Accelerated Quantum Industry Simulation Dashboard

• 
• 
• 
• 

A hybrid quantum-classical simulation framework leveraging PyTorch tensor graphs and automatic differentiation engines. Created by Srinivasta, this system ingests multi-domain telemetry to model environmental, logistics, and post-quantum cryptographic security parameters in real time.

---

🔗 Production Hubs

• Production Live Viewport: Live Streamlit Application

---

📋 System Architecture & Data Schema

The engine ingests continuous and discrete environmental telemetry vectors directly from dashboard viewpoints. The dataset tracks across 265 execution loop cycles with the following schema matrix:

Feature/Target	Column Identifier	Metric Unit	Representation Space	Typical Scale
Input Feature	Temperature	Celsius (°C)	Live Air Matrix (Visakhapatnam)	27.5 to 29.7
Input Feature	Stock_Price	USD ($)	IBM Spot Valuation Asset	286.94 to 287.14
Optimization Target	VQE_Energy	Hartree (Ha)	Ground State Expectation Value	-1.08817 to -1.08940
Security Target	PQC_Public	Bytes	ML-KEM-512 Public Keyspace	1191
Security Target	PQC_Private	Bytes	ML-KEM-512 Private Keyspace	2414

---

🧮 Mathematical Framework

1. Vector Feature Scaling (Min-Max Topology)

To prevent extreme magnitude disparity—where large cryptographic integer boundaries ((\approx 2414.0)) drown out ultra-fine sub-zero quantum expectations ((\approx -1.0882))—all parameters are mapped uniformly onto a shared $[0, 1]$ computational plane:

[\vec{x}{\text{norm}} = \frac{\vec{x} - \vec{x}{\text{min}}}{\vec{x}{\text{max}} - \vec{x}{\text{min}}}]

2. Multi-Objective Objective Function

The loss graph tracks error variance symmetrically across three distinct industrial sectors simultaneously using weighted parameter backpropagation:

[\mathcal{L}{\text{total}}(\vec{\theta}) = \alpha \mathcal{L}{\text{AgriTech}}(VQE) + \beta \mathcal{L}{\text{Logistics}}(QAOA) + \gamma \mathcal{L}{\text{Finance}}(PQC)]

---

⚡ Hardware Acceleration Deployment

The automatic differentiation execution loops are fully compiled for modern GPU architectures using the PyTorch Graph Compiler optimization pass:

import torch

# Instantiate architecture model
model = DirectImageGraphNet().to("cuda")

# Fuse point-wise kernels & optimize for NVIDIA Tensor Cores
optimized_model = torch.compile(model, mode="max-autotune")

• Data Layout Optimization: Precision variables are cast directly to torch.bfloat16 to maximize raw Tensor Core compute efficiency.
• Autograd Vectorization: Evaluates multi-parameter Jacobians simultaneously by enabling vectorized=True inside torch.autograd.functional.jacobian.

---

📊 Industrial Verification Viewports

The pipeline continuously updates an active simulation panel tracking three target domains:

1. AgriTech (VQE Curve): Models inverse correlations showing how local thermal oscillations influence quantum molecule minimization convergence paths down to the physical structural limit of $-1.1350\text{ Ha}$.
2. Logistics (QAOA Network Map): Solves 4-node combinatorial graph constraints ($T_0 \to T_3$) to isolate the optimal path configuration bitstring: 1010.
3. Finance (PQC Vault Envelope): Benchmarks classical RSA-2048 crypto footprints against quantum-resistant ML-KEM-512 parameters to maintain network resilience against future computing threats.

---

🚀 Execution & Setup

1. Clone this ecosystem repository:

   git clone https://github.com.git
   cd quantum-advantage-dashboard

2. Install foundational framework dependencies:

   pip install -r requirements.txt

3. Initialize the real-time Streamlit dashboard environment local node:

   streamlit run app.py

---

✒️ Author and Credits

• Lead Architect & Developer: Srinivasta

Connect with Me

• 
• 
• 
•