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SPDSolver: continuous correlated solver for p-bit / Ising models. - #138
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The solver maps the discrete Ising parameters (J, h) to a symmetric positive-definite (SPD) representation before sampling. The SPD structure provides a valid continuous correlated state space, allowing proposals to be generated collectively rather than by updating p-bits independently. And tests.
gcattan
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Sep 16, 2026
gcattan
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gcattan
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I trust you on the math.
A couple of things regarding the architectural contract.
| return eigenvectors @ (np.sqrt(eigenvalues) * z) | ||
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| class SPDSolver: |
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This should inherite from Solver and keep Backend/Solver separation.
| raise TypeError(f"{name}.solve(circuit) must return an (I, m) tuple") | ||
| return out[0], out[1] | ||
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| def solve(self, circuit): |
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annealing function is not supported.
| self.energies_ = all_E | ||
| self.acceptance_rate_ = accepted / self.Nt | ||
| self.final_distance_ = riem_dist(X, target) | ||
| return all_I, all_m |
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Only when return_filtered. If not, then the return should be I, m, E
Improved based on review and added diagnostic switch.
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SPDSolver: continuous correlated solver for p-bit / Ising models.
This is the first continous solver in p-kit.
The solver maps the discrete Ising parameters (J, h) to a symmetric positive-definite (SPD) representation before sampling. The SPD structure provides a valid continuous correlated state space, allowing proposals to be generated collectively rather than by updating p-bits independently.
A continuous solver may provide better results, but the problem must first be mapped successfully to a continuous space. This is the first and main obstacle. Therefore, the J,h -> SPD mapping is validated before use. If the mapping quality is below the configured threshold, SPDSolver can fall back to another solver. Use fall_back_solver= in this case.
SPDSolver is particularly useful for correlated sampling, optimization, and future analog implementations where continuous SPD dynamics may be implemented directly into hardware.
For problems requiring very accurate sampling of the target Boltzmann distribution, especially as problem size grows, GibbsSolver may still provide better distribution fidelity.
And tests.