Real-Time Quantum Compiling via Deep Reinforcement Learning
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Solution Overview
Problem
Current quantum compilers for gate-model quantum computers face high execution and precompilation times, making them impractical for online operations, and existing AI techniques have not satisfactorily addressed the challenge of identifying an optimal quantum compiling strategy for given quantum operations.
Innovation Solution
A computer-implemented method using deep reinforcement learning to determine a quantum circuit for a given quantum operation by training a machine-learning algorithm with a policy encoded through a reinforcement learning procedure, allowing for real-time quantum compiling by combining base quantum gates within a specified tolerance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional quantum compiling algorithms are used, then quantum circuits can be compiled with acceptable accuracy, but the execution time and precompilation time are excessively long, making them impractical for online operations
Solution Approach 1:
The patent applies preliminary action by pre-training the deep reinforcement learning agent offline before actual quantum compiling operations. The agent learns optimal compiling strategies in advance through extensive training with various quantum circuits and hardware constraints, so that during online operations it can rapidly compile circuits without requiring lengthy real-time computation. This separates the time-consuming learning phase from the time-critical execution phase.
Solution Approach 2:
The patent replaces traditional mechanical quantum compiling algorithms (such as quantum Shannon decomposition or Cosine-Sine decomposition) with an intelligent system based on deep reinforcement learning. Instead of following fixed algorithmic procedures that require extensive computational steps, the trained agent directly maps quantum circuits to optimized gate sequences, dramatically reducing compilation time while maintaining or improving circuit fidelity.
2Ease of operation
If existing AI techniques are applied to quantum gate control, then speed and fidelity can be optimized, but the challenge of identifying an optimal quantum compiling strategy for given quantum operations remains unsolved
Solution Approach 1:
The patent achieves universality by designing a deep reinforcement learning agent that can handle multiple quantum compiling tasks with a single unified framework. The agent is trained on diverse quantum circuits and hardware architectures, enabling it to adapt to different compiling scenarios without requiring task-specific algorithms. This single agent provides both gate-level control optimization and overall compiling strategy identification across various quantum operations.
Solution Approach 2:
The patent implements feedback through the reinforcement learning mechanism where the agent receives reward signals based on the quality of compiled circuits (fidelity, depth, gate count). This feedback loop allows the agent to learn from past compiling decisions and continuously improve its strategy. The critic network provides value estimates that guide the policy network, creating a feedback-driven learning system that adapts to different quantum operations and hardware constraints.
Data Source
AI summary
A computer implemented method for real time quantum compiling includes a unitary matrix, representing a single-qubit or multi-qubit quantum operation implemented by a quantum computer, to a machine-learning trained algorithm. Information representing a base of quantum gates for building a quantum circuit corresponding to unitary matrix operation, a tolerance parameter, and processing termination information are provided to the algorithm. A quantum circuit is determined including the combination of base quantum gates. The determining is based on a policy encoded in the algorithm by reinforced learning training. Finally, information is provided on the determined quantum circuit, as a result of the real time quantum compiling. The reinforced learning training phase is based on a Reinforced Learning procedure. The Reinforced Learning procedure includes defining an unbiased set of training target unitary matrices, defining training base sets of quantum gates, and executing episodes of the Reinforced Learning procedure.