Reinforcement Learning Quantum Circuit Transpilation
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Solution Overview
Problem
Existing quantum circuit transpiling methods struggle to efficiently optimize quantum circuits for execution on noisy quantum systems and devices with specific qubit connectivity constraints, leading to suboptimal performance and increased computational resources required.
Innovation Solution
The use of a machine learning-based system that receives an input quantum circuit representation and constraints, and generates a transpiled quantum circuit representation using reinforcement learning to optimize gate selection and circuit topology, thereby improving performance and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional quantum circuit transpiling methods are used, then quantum circuits can be transpiled to match qubit connectivity constraints, but the optimization performance is suboptimal and computational resources required increase
Solution Approach 1:
The patent replaces traditional mechanical/search-based transpilation systems with a reinforcement learning-based system. The RL agent learns optimal transpilation strategies through training on quantum circuits, substituting conventional algorithmic approaches with a data-driven learning mechanism that can adapt to different quantum hardware constraints and optimize circuit transformations more effectively
Solution Approach 2:
The patent changes the fundamental parameters of the transpilation process by introducing RL training iterations, reward functions, and policy networks. Instead of using fixed transpilation rules, the system dynamically adjusts transpilation strategies based on learned patterns from training data, optimizing parameters like gate selection, qubit mapping, and circuit depth to achieve superior performance
2Reliability
If quantum circuits are optimized for execution on noisy quantum systems, then execution performance improves, but the complexity of the optimization process increases
Solution Approach 1:
The patent segments the complex optimization process into distinct RL training phases and inference steps. The training phase separates data collection, model training, and validation, while the inference phase handles actual transpilation. This segmentation makes the overall complex optimization process more manageable and scalable
Solution Approach 2:
The reinforcement learning system performs self-service optimization by automatically learning from training data and generating optimal transpilation strategies without requiring manual intervention. The RL agent self-adjusts its policy based on reward signals from execution performance metrics, eliminating the need for complex manual optimization procedures
Data Source
AI summary
Systems and techniques that facilitate quantum circuit transpiling are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise a receiver component that receives an input quantum circuit representation and one or more quantum circuit constraints, a machine learning component that generates a transpiled quantum circuit representation based on the one or more quantum circuit constraints and the input quantum circuit representation.