Neutral Atom Quantum Processor Configuration via Machine Learning
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Solution Overview
Problem
Quantum processors based on neutral atoms face limitations in solving QUBO problems due to the complexity of generating a target Hamiltonian, which is not feasible with existing methods for other quantum processor technologies.
Innovation Solution
A method is developed to determine the configuration of a quantum processor with neutral atoms by using a machine learning tool to minimize the difference between the interaction Hamiltonian and the target Hamiltonian, thereby optimizing the positioning of neutral atoms for solving QUBO problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If quantum processors based on neutral atoms are used to solve QUBO problems, then advantages over superconducting circuits are obtained, but the ability to generate the target Hamiltonian is limited
Solution Approach 1:
The patent applies preliminary action by pre-calculating and optimizing the positioning of neutral atoms before the actual QUBO problem solving process. The method determines optimal atom positions in advance based on the QUBO matrix characteristics, creating a prepared configuration that enables the quantum processor to effectively generate the required Hamiltonian structure without during-execution adjustments.
2Device complexity
If traditional triangular positioning is used for neutral atoms, then device simplicity is maintained, but Hamiltonian approximation quality deteriorates
Solution Approach 1:
The patent applies parameter changes by optimizing the spatial positioning parameters of neutral atoms based on the specific QUBO problem characteristics. Instead of using fixed triangular positioning, the method calculates optimal positions that minimize the difference between the interaction Hamiltonian and target Hamiltonian, thereby improving Hamiltonian approximation quality while adapting to different problem instances.
Solution Approach 2:
The patent applies local quality by making the atom positioning specific to each QUBO problem instance rather than using a universal triangular arrangement. The method determines localized optimal positions for atoms based on the particular structure and requirements of the given optimization problem, allowing each configuration to be tailored to its specific computational task.
3Productivity
If the number of neutral atoms increases, then computational capability improves, but the complexity of satisfying quadratic constraints increases linearly
Solution Approach 1:
The patent applies preliminary action by pre-optimizing the positioning configuration for N atoms based on the QUBO matrix before execution. This advance preparation creates a mapped relationship between problem parameters and optimal atom positions, enabling the system to handle increasing numbers of atoms without proportionally increasing the operational complexity of satisfying constraints during the actual quantum computation.
Data Source
AI summary
The present invention relates to a method for determining a configuration of a quantum processor to solve an optimization problem, the quantum processor comprising neutral atoms that are able to be manipulated to form qubits, the quantum processor having an interaction Hamiltonian that is dependent on the positions of the neutral atoms, the method comprising:a. determining a target Hamiltonian for the quantum processor according to a QUBO matrix, the QUBO matrix describing the optimization problem in the form of a quadratic unconstrained binary optimization, andb. determining the positions of the neutral atoms of the quantum processor so as to minimize a difference between the interaction Hamiltonian of the quantum processor and the target Hamiltonian, the step of determining the positions comprising the use of a machine learning tool that is able to determine the positions of the atoms according to data from the target Hamiltonian.