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
The patent application discusses techniques for solving quantum circuit mapping problems using reinforcement learning methods. Quantum circuit mapping involves configuring logical quantum computations to be executed on fixed quantum hardware layouts, often requiring the use of swap gates.
- Reinforcement learning model takes inputs such as a logical quantum circuit, a physical qubit connectivity graph, and an initial qubit allocation scheme to schedule quantum gates for execution on physical qubits of the hardware device.
- The model may include a neural network assisted by a Monte Carlo Tree Search (MCTS) algorithm to guide the routing of quantum gates more efficiently, reducing the need for swap gates.
Potential Applications:
- Quantum computing optimization
- Quantum algorithm design
- Quantum hardware development
Problems Solved:
- Efficient mapping of logical quantum circuits to physical hardware
- Minimization of swap gate usage
- Optimization of quantum gate scheduling
Benefits:
- Improved quantum circuit performance
- Enhanced quantum hardware utilization
- Faster execution of quantum algorithms
Commercial Applications:
- Quantum computing software development
- Quantum hardware optimization services
- Research and development in quantum computing industry
Questions about Quantum Circuit Mapping using Reinforcement Learning: 1. How does reinforcement learning improve quantum circuit mapping efficiency? 2. What are the key challenges in implementing reinforcement learning for quantum gate scheduling?
Frequently Updated Research: Ongoing research focuses on enhancing the performance of reinforcement learning models for quantum circuit mapping and exploring new algorithms for more efficient 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).
