Parameterized Quantum Circuit Construction via Error-Based Sub-Circuit Optimization
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Solution Overview
Problem
The design of parameterized quantum circuits is limited by the need for manual intuition and experience, lacking a systematic process for constructing the circuit structure, which restricts the adaptation to errors in quantum hardware and hampers practical utilization of quantum computing.
Innovation Solution
A method and apparatus for constructing parameterized quantum circuits that involve inputting learning data, calculating error rates, updating parameters for sub-circuit blocks based on error rates, and repeating this process with validation to optimize the circuit structure, allowing for adaptive error reduction and performance improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual intuitive design is used for quantum circuit structure, then design flexibility is maintained, but design efficiency and adaptability to hardware errors are limited
Solution Approach 1:
The system performs self-learning by automatically constructing and optimizing quantum circuit structures through machine learning algorithms. The learning module autonomously iterates through circuit configurations, evaluates their performance on quantum hardware, and updates the circuit structure without requiring manual intervention, thereby achieving both high adaptability to hardware errors and design efficiency
Solution Approach 2:
The system implements a feedback mechanism where the learning module continuously receives performance data from quantum hardware executions, calculates error rates, and uses this feedback to iteratively optimize the circuit structure. This closed-loop approach enables the circuit to adapt to hardware-specific errors while maintaining efficient automated design
2Power
If circuit depth and number of gate operators are increased to improve computational capability, then processing power is enhanced, but error rates increase
Solution Approach 1:
The system dynamically adjusts circuit depth and gate operator counts based on hardware-specific error characteristics learned through iterative training. The learning module optimizes circuit parameters to achieve the necessary computational capability while maintaining error rates within acceptable thresholds by adapting circuit structure to match hardware capabilities
Solution Approach 2:
The system changes circuit parameters such as depth, width, and gate types based on learned hardware error profiles. The learning module systematically varies these parameters during training to identify optimal configurations that maximize computational power while minimizing error accumulation on specific quantum hardware
3Adaptability or versatility
If more sub-circuit blocks are added to increase circuit expressiveness, then model capacity is improved, but complexity and training difficulty increase
Solution Approach 1:
The quantum circuit is segmented into multiple sub-circuit blocks that can be independently optimized. The learning module constructs the circuit by composing these modular sub-blocks, allowing complex expressive circuits to be built from simpler, more manageable units that are easier to train and less prone to optimization difficulties
Data Source
AI summary
A method for constructing a parameterized quantum circuit according to an embodiment includes inputting learning data to a quantum circuit, receiving output data for the learning data from the quantum circuit and calculating an error rate therefrom, and updating, based on the error rate, parameters for at least one sub-circuit block to be updated among one or more sub-circuit blocks included in the quantum circuit.