QUANTUM CIRCUIT MAPPING USING REINFORCEMENT LEARNING TECHNIQUES

Techniques for solving quantum circuit mapping problems using reinforcement learning techniques are disclosed. Quantum circuit mapping often requires the use of SWAP gates in order to configure logical quantum computations to be executed using fixed quantum hardware device layouts. A reinforcement l...

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Bibliographic Details
Main Authors Duan, Yiheng, Shi, Yunong
Format Patent
LanguageEnglish
Published 03.10.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:Techniques for solving quantum circuit mapping problems using reinforcement learning techniques are disclosed. Quantum circuit mapping often requires the use of SWAP gates in order to configure logical quantum computations to be executed using fixed quantum hardware device layouts. A reinforcement learning model takes inputs such as a logical quantum circuit, a physical qubit connectivity graph corresponding to a quantum hardware device, and an initial qubit allocation scheme, and uses such information to schedule quantum gates of the logical quantum circuit for execution using respective physical qubits of the quantum hardware device. A reinforcement learning model that is configured to solve such quantum circuit mapping problems may comprise a neural network that is assisted by a Monte Carlo Tree Search (MCTS) algorithm, wherein the MCTS algorithm guides the neural network towards quantum circuit routing pathways which are more efficient (e.g., require fewer SWAP gates to be scheduled).
Bibliography:Application Number: US202318192954