Parameterized Quantum Circuit Optimization via Bayesian Segmentation
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Solution Overview
Problem
Current methods for molecular structure optimization using variational quantum eigensolver (VQE) require an enormous number of iterative calculations, making practical calculations on quantum computers inefficient due to the need for repeated processing between classical and quantum computers.
Innovation Solution
A molecular structure optimization system that employs a quantum computer and a classical computer connected for Bayesian optimization, where a parameterized quantum circuit calculates a loss function, and an update unit alternately updates circuit and coordinate parameters using Bayesian optimization algorithms until a stop condition is met, reducing the number of iterative calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If variational quantum eigensolver (VQE) is used for molecular structure optimization, then quantum computing capabilities are utilized, but the number of iterative calculations becomes enormous
Solution Approach 1:
The patent divides the optimization parameters into two distinct segments: circuit parameters (quantum) and coordinate parameters (classical). This segmentation allows different optimization strategies to be applied to each type, reducing the overall computational burden by handling them separately rather than jointly optimizing all parameters simultaneously through numerous iterations.
Solution Approach 2:
The patent performs preliminary optimization of circuit parameters using quantum computing to obtain an initial optimal circuit parameter. This preliminary action prepares a good starting point for the subsequent coordinate parameter optimization, reducing the number of iterative calculations needed in the final optimization stage.
2Measurement precision
If repeated processing between classical and quantum computers is performed, then optimization accuracy can be improved, but calculation cost increases significantly
Solution Approach 1:
The patent segments the optimization process into distinct phases: quantum circuit parameter optimization and classical coordinate parameter optimization. This segmentation reduces the need for repeated quantum-classical processing by handling different parameter types in separate optimization stages, thereby reducing calculation cost while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary quantum optimization to obtain optimal circuit parameters before proceeding to classical coordinate optimization. This preliminary action reduces the computational burden of subsequent iterations by establishing a solid foundation with optimized quantum parameters, thus reducing overall calculation cost.
3Measurement precision
If high expressivity trial function is used, then VQE calculation accuracy improves, but device complexity increases
Solution Approach 1:
The patent separates circuit parameter optimization from coordinate parameter optimization, allowing the quantum circuit to focus on achieving high expressivity for electronic structure representation while the classical optimizer handles geometric optimization. This segmentation enables high accuracy without requiring excessively complex quantum circuits.
Data Source
AI summary
A molecular structure optimization system includes a quantum computer and a classical computer. The quantum computer uses a parameterized quantum circuit to calculate a loss function from a coordinate parameter of a target molecule. The classical computer updates the coordinate parameter and the circuit parameter based on the loss function, and determines optimum values of the circuit parameter and the coordinate parameter. The classical computer updates a provisional value of the circuit parameter while fixing the coordinate parameter and changing the circuit parameter. The classical computer updates a provisional value of the coordinate parameter while fixing the circuit parameter and changing the coordinate parameter.