Coherent Ising Machine Sampling Ground States
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Solution Overview
Problem
Current Ising solvers, such as quantum annealers, are not well-suited for efficiently sampling all ground states and low-energy configurations, often exhibiting exponential bias in degenerate ground-state samples, which limits their applicability in applications requiring distributional information about spin configurations.
Innovation Solution
A measurement-feedback coherent Ising machine (MFB-CIM) using a discrete-time Gaussian-state quantum model is employed to efficiently sample ground-state and low-energy Ising spin configurations, incorporating nonlinear gain saturation and quantum noise to facilitate stochastic sampling of spin configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum annealers are used to find ground states of the Ising problem, then ground-state finding capability is improved, but sampling fairness and distributional information accuracy deteriorate due to exponential bias in degenerate ground-state samples
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors the energy of spin configurations and adjusts the sampling process accordingly. The feedback loop ensures that degenerate ground states are sampled with uniform probability by detecting and correcting biases in real-time, thereby maintaining both ground-state finding accuracy and sampling fairness.
Solution Approach 2:
The patent changes the operational parameters of the Ising solver to enable fair sampling of degenerate ground states. By adjusting parameters such as temperature, coupling strengths, and measurement protocols, the system transitions from biased ground-state finding to unbiased sampling of all ground states and low-energy configurations, preserving distributional information accuracy.
2Productivity
If conventional Ising solvers are used for combinatorial optimization, then optimization capability is improved, but sampling of multiple ground states and low-energy configurations deteriorates
Solution Approach 1:
The patent designs the Ising solver to perform multiple functions: it can find ground states for optimization problems while simultaneously sampling multiple ground states and low-energy configurations. The system achieves this universality by implementing a unified sampling algorithm that works for both optimization and sampling tasks, eliminating the need for separate specialized systems.
Solution Approach 2:
The patent introduces dynamic sampling techniques that allow the system to adapt its behavior based on the specific problem requirements. The sampling process is made flexible and adjustable, enabling the system to transition between finding single ground states and sampling multiple configurations depending on the application needs, thereby enhancing versatility.
3Device complexity
If decomposition of large optimization problems into subproblems is performed, then hardware compatibility is improved, but solution quality deteriorates when using single optimum from each subproblem
Solution Approach 1:
The patent applies partial sampling within each subproblem rather than finding only the single optimum. By sampling multiple ground states and low-energy configurations from each subproblem, the system captures more comprehensive solution space information. This excessive sampling action ensures that when subproblems are recombined, the overall solution quality is maintained or improved compared to using only single optima from each subproblem.
Data Source
AI summary
A system and method for efficient sampling of ground-state and low-energy Ising configurations. The system may be implemented using the nonlinear stochastic dynamics of a measurement-feedback-based coherent Ising machine (MFB-CIM). A discrete-time Gaussian-state model of the MFB-CIM may capture the nonlinear dynamics. The system and method requires many fewer roundtrips to sample than for other known systems.