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This research introduces a hybrid quantum-classical framework that uses random forest regression to predict optimal regularization parameters for binary compressed sensing, reducing reconstruction errors by up to 15%. By reusing prior search data, the method eliminates the need for exhaustive parameter re-optimization in varying signal dimensions and sparsity levels. While promising for applications like medical imaging, the approach currently relies on controlled conditions that may not fully reflect real-world noise profiles.

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Regression Finds Parameters That Reduce Error by up to 15% during Data Reconstruction

Quantum ZeitgeistSeptember 24, 20263 min read

Phase Transition in Binary Compressed Sensing via Annealing with Adaptive Regularization A quantum, classical hybrid approach now outperforms simulated annealing across portions of its operating range, achieving lower mean squared reconstruction errors on comparable problems. This improvement stems from predicting optimal regularization parameters using random forest regression trained upon prior search data. Consequently, previously computed solutions can be reused to streamline reconstructions without repeated parameter selection.

A regularisation-selection method combining systematic parameter search with random forest regression investigates the relationship between replica phase diagrams of annealing-based binary compressive sensing. Reference parameters are selected from a candidate grid by minimising mean squared reconstruction error over repeated simulated annealing (SA) trials under noiseless Gaussian measurements with known sparsity. The fitted model predicts these reference…

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