IsothermIQ-Quantum-AI-DAC – Jabez Grace CloudTech Solutions Ltd

Overview: The Core Concept
The fundamental goal is to make Direct Air Capture (DAC) more energy-efficient. This is achieved by:
Quantum Simulation: Using a quantum algorithm (Variational Quantum Eigensolver – VQE) to calculate the CO₂ binding energy of a sorbent material with extremely high accuracy. Classical computers struggle with this, leading to less precise results.
Artificial Intelligence: Feeding this highly accurate quantum data into a pre-trained AI model. The AI then calculates the most energy-efficient operating parameters (temperature, pressure, etc.) for the DAC system, something a simple control system cannot do.
The simulation walks you through this entire integrated process.

Step-by-Step Simulation Workflow
Step 1: Configuration (The Control Panel)
This is where you, as the operator or researcher, set the initial conditions for the simulation.
Select Sorbent Material: You begin by choosing a sorbent from the dropdown list (e.g., Mg-MOF-74). Each material has different chemical properties and a theoretical target Binding Energy. This is analogous to choosing the physical filter material for a real-world DAC plant.
Set Environmental Parameters: You adjust the sliders for Temperature, Humidity, and CO₂ Concentration. This simulates the real-world ambient conditions where the DAC unit would be operating. These conditions directly affect the sorbent’s performance.
Enable Sensitivity Analysis (Optional): By toggling this on, you instruct the system to run additional, rapid simulations after the main one. The purpose is to test how sensitive the chosen sorbent is to small fluctuations in the environment (e.g., a 2°C temperature increase), providing a measure of its operational stability.
Step 2: Initiating the Simulation
When you click the “Run Quantum-AI Optimization” button:
The application’s state changes to is Simulating. The button shows a spinner, and the control panel is disabled to prevent changes during the run.
The Log Panel clears and begins to receive live updates from the simulation service.
The main handleRunSimulation function is called, which orchestrates the entire process.
Step 3: Live Quantum VQE Calculation (The “Quantum” Part)
This is the core of the quantum simulation, and its progress is mirrored in the Log Panel and the live-updating VQE Simulation Chart. The simulation doesn’t actually run on a quantum computer but precisely models the steps and outputs of the VQE algorithm.
Initialization (Log): The log reports “Initializing Qubit-ADAPT-VQE algorithm…” and “Defining active space…”. This simulates the system preparing the quantum problem, focusing the computational power on the specific atoms and electron orbitals involved in the CO₂ binding.
Iterative Convergence (Chart & Log): The VQE algorithm is iterative. It makes a “guess” at the molecule’s ground state energy and refines it over many steps.
You see this live on the VQE chart. The blue “Binding Energy” line starts far from the red “Target Energy” line and progressively gets closer with each iteration, simulating the quantum computer refining its calculation.
The Log Panel prints messages like “VQE Iteration 5: Applying RY & RZ gates…”, simulating the actual quantum gate operations.
The data for this live chart is sent via a streamCallback, which allows the UI to update in real-time without waiting for the full simulation to complete.
Step 4: AI-Driven Optimization (The “AI” Part)
Once the VQE simulation converges on a highly accurate binding energy, that value is passed to the AI controller.
AI Activation (Log): The log shows the AI module activating: “Deep Neural Network activated,” “Integrating quantum expectation values…”, “Loading pre-trained weights…”.
Parameter Calculation (Log): The log reports “Adaptive control parameters calculated from AI output layer.” This simulates the AI model processing the VQE result and predicting the optimal settings for the physical DAC hardware to minimize energy consumption. This is the key innovation: using the quantum result to inform a sophisticated AI, which in turn controls the hardware.
Step 5: Displaying Final Results
Once the simulation service returns the final results object:
The isSimulating state is set to false, the UI unlocks, and the final data is displayed.
Key Metrics: The top of the ResultsDisplay card shows the final calculated Binding Energy, the Energy Reduction percentage (the AI’s primary achievement compared to a standard system), and the Qubit Space used for the calculation.
AI-Optimized Control Parameters: This card shows the specific, actionable settings (e.g., Desorption Temperature) that the AI recommends. These would be sent to the physical DAC machinery in a real system.
Final VQE Chart: The chart now displays the complete, converged simulation data.
Step 6 (Conditional): Sensitivity Analysis
If you enabled this option in Step 1:
The Log Panel announces that the sensitivity analysis is starting.
The system calls runQuickSimulation multiple times, once for each environmental variation (e.g., Temp High, Humidity Low).
These are faster, simplified calculations that estimate the binding energy under the new conditions.
The results are populated in the Sensitivity Analysis table, allowing you to see at a glance how stable the material’s performance is. For example, a large negative change in binding energy on a “Humidity High” day would be a major concern.
Step 7: In-Depth Analysis and Reporting
After the simulation, two new panels appear at the bottom, translating the raw results into deeper insights for different audiences.
Technical Details Panel: This is for a scientific or engineering audience. It provides granular data on the quantum computation, compares its accuracy and speed to slower classical methods (DFT), and gives a chemical interpretation.
Techno-Economic Analysis Panel: This is for an investor or business audience. It converts the scientific metric of “energy reduction” into business-critical KPIs like Levelized Cost of Capture (LCOC), OPEX Reduction, and Projected ROI.
Data Export: The “CSV” and “PDF Report” buttons allow you to export the complete findings for offline review, grant applications, or investor presentations.
This entire sequence demonstrates a closed-loop, data-driven approach: setting conditions, running an advanced quantum/AI simulation, and receiving back not just scientific data, but optimized operational parameters and a full economic impact analysis.