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Accuracy-enhanced coherent Ising machine using the quantum adiabatic theorem.

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    The novel adiabatic coherent Ising machine (A-CIM) improves computational accuracy for complex optimization problems by using quantum adiabatic evolution to avoid local minima, outperforming the standard CIM.

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    Area of Science:

    • Quantum Computing
    • Optical Computing
    • Computational Physics

    Background:

    • Coherent Ising Machines (CIMs) utilize degenerate optical parametric oscillator (DOPO) networks for accelerating computations.
    • Increasing problem sizes in CIMs lead to a higher probability of getting trapped in local minima, limiting solution accuracy.
    • The quantum adiabatic theorem states that systems remain in their ground state if Hamiltonian changes slowly.

    Purpose of the Study:

    • To propose a novel method, Adiabatic CIM (A-CIM), to enhance the performance of CIMs.
    • To mitigate the issue of local minima trapping in CIMs by incorporating quantum adiabatic evolution.
    • To improve the accuracy of solving hard combinatorial optimization problems using optical computing platforms.

    Main Methods:

    • Implementation of a new computational approach, A-CIM, by integrating quantum adiabatic evolution into the CIM's ground-state search process.
    • Utilizing degenerate optical parametric oscillator (DOPO) networks as the hardware platform.
    • Conducting numerical simulations to evaluate A-CIM's performance on MAXCUT problems.

    Main Results:

    • A-CIM demonstrates improved solution accuracy compared to standard CIM for MAXCUT problems.
    • The enhanced performance was observed across a range of problem sizes, specifically with 10 to 2000 vertices.
    • The method effectively helps the machine partially avoid local minima during computation.

    Conclusions:

    • The proposed A-CIM, based on the quantum adiabatic theorem, offers a more accurate approach to solving complex optimization problems.
    • This method represents a significant advancement in optical computing for tackling computationally intensive tasks.
    • A-CIM is expected to solve optimization problems with greater correctness, especially for larger problem instances.