Quantum CFD Simulation Using Lattice Boltzmann Encoding
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Solution Overview
Problem
Classical computational fluid dynamics (CFD) systems face challenges in solving complex fluid dynamics problems due to resource-intensive computations, making simulations of events like airplane takeoff and landing unfeasible.
Innovation Solution
A quantum computing system employing the Lattice Boltzmann Method (LBM) and variational quantum CFD (VQCFD) to convert fluid dynamics variables into quantum parameters, using a quantum computer to efficiently solve for these parameters and visualize fluid flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical computational fluid dynamics systems are used to solve complex fluid dynamics problems, then measurement precision and manufacturing precision can be achieved, but device complexity and loss of energy increase significantly making simulations unfeasible
Solution Approach 1:
The patent replaces the classical mechanical computational system with a quantum computing system. The quantum computer uses quantum bits (qubits) and quantum mechanical principles to perform fluid dynamics simulations, substituting the classical mechanical computation that becomes infeasible for complex problems. This allows solving Navier-Stokes equations and other fluid dynamics problems that are currently unsolvable with classical systems.
Solution Approach 2:
The patent transforms the problem parameters from classical computational variables to quantum state representations. Fluid dynamics variables such as velocity, pressure, and density are encoded into quantum states using parameterized quantum circuits. This parameter transformation enables the quantum system to handle the computational complexity that would otherwise be intractable for classical systems.
2Measurement precision
If classical CFD systems attempt simulations of complex events like airplane takeoff and landing, then measurement precision can be maintained, but loss of time and productivity decrease due to resource-intensive computations
Solution Approach 1:
The patent substitutes quantum mechanical computation for classical mechanical computation to accelerate fluid dynamics simulations. The quantum computer processes fluid dynamics problems exponentially faster for certain problem types, enabling simulations of complex events like airplane takeoff and landing to be completed in practical timeframes while maintaining measurement precision.
Solution Approach 2:
The patent uses parameterized quantum circuits that are pre-configured with initial conditions and boundary conditions for fluid dynamics problems. These circuits are designed in advance to handle specific types of fluid flow problems, allowing the quantum system to rapidly compute solutions without requiring extensive iterative processing that would slow down classical systems.
3Measurement precision
If classical systems use more computational resources to improve simulation accuracy, then measurement precision improves, but loss of energy increases making the system unsustainable
Solution Approach 1:
The patent replaces energy-intensive classical computational systems with a quantum system that uses quantum mechanical effects to perform computations more efficiently. The quantum computer maintains simulation accuracy while consuming less energy by leveraging quantum parallelism and interference to solve fluid dynamics problems without requiring the exponential computational resources that would otherwise be needed.
Data Source
AI summary
A system simulating fluid dynamics on quantum computers includes a quantum system, a processor, and a memory. The memory includes instructions stored thereon, which, when executed by the processor, cause the quantum system to: access initial conditions(θv0,Θv0)and boundary conditions(θvb,Θvb);generate|fv¯n〉based onθvnby the quantum computer; generate|fv¯b〉based onθvbby the quantum computer; receive by the quantum computer values ofθv,newn+1from an optimizer and generate tentative values of|fv¯n+1〉;generate by the quantum computer〈ℱ(fv¯n)|fv¯n+1〉based on|fv¯n〉and the tentative values of|fv¯n+1〉;generate by the quantum computer〈fv¯b|fv¯n+1〉based on|fv¯b〉and the tentative values of|fv¯n+1〉;determine cost function values Cv based on a new value ofΘv,newn+1,inner products〈fv¯b|fv¯n+1〉,and〈(fv¯n)|fv¯n+1〉;and determine by the optimizer values for(θvn+1,θvn+1)based on the cost function values Cv.