Amazon technologies, inc. (20240330730). QUANTUM CIRCUIT MAPPING USING REINFORCEMENT LEARNING TECHNIQUES simplified abstract
Contents
QUANTUM CIRCUIT MAPPING USING REINFORCEMENT LEARNING TECHNIQUES
Organization Name
Inventor(s)
Yiheng Duan of Seattle WA (US)
Yunong Shi of Old Greenwich CT (US)
QUANTUM CIRCUIT MAPPING USING REINFORCEMENT LEARNING TECHNIQUES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330730 titled 'QUANTUM CIRCUIT MAPPING USING REINFORCEMENT LEARNING TECHNIQUES
- Simplified Explanation:**
The patent application discusses techniques for solving quantum circuit mapping problems using reinforcement learning. This involves scheduling quantum gates of a logical quantum circuit for execution on physical qubits of a quantum hardware device, with the help of a neural network and Monte Carlo Tree Search algorithm.
- Key Features and Innovation:**
- Utilizes reinforcement learning to map logical quantum circuits onto physical qubits efficiently. - Incorporates a neural network assisted by a Monte Carlo Tree Search algorithm. - Addresses the need for swap gates in quantum circuit mapping. - Optimizes quantum gate scheduling for better efficiency.
- Potential Applications:**
- Quantum computing research and development. - Quantum algorithm optimization. - Quantum hardware design and implementation.
- Problems Solved:**
- Efficient mapping of logical quantum circuits onto physical qubits. - Reduction of swap gate usage in quantum circuit mapping. - Improved quantum gate scheduling for better performance.
- Benefits:**
- Enhanced efficiency in quantum circuit mapping. - Optimal utilization of quantum hardware resources. - Potential for faster quantum computations.
- Commercial Applications:**
- Quantum computing software development. - Quantum hardware optimization services. - Integration of quantum algorithms in various industries.
- Prior Art:**
Prior research on quantum circuit mapping algorithms and techniques in the field of quantum computing.
- Frequently Updated Research:**
Stay updated on advancements in reinforcement learning techniques for quantum circuit mapping and optimization.
- Questions about Quantum Circuit Mapping:**
1. How does reinforcement learning improve the efficiency of quantum circuit mapping? 2. What role does the Monte Carlo Tree Search algorithm play in optimizing quantum gate scheduling?
Original Abstract Submitted
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).
