Reorganizing Quantum Measurement Records Improves Time-Series Prediction

TL;DR

Reorganizing quantum measurement records into split-ensemble groups enhances time-series prediction accuracy in quantum reservoir computing without extra hardware costs.

Contribution

Introduces split-ensemble training, a novel method that improves quantum learning by reorganizing measurement records, applicable without additional quantum hardware.

Findings

Improved prediction accuracy on simulated benchmarks.

Significant gains observed on real hardware.

Enhances quantum reservoir computing without extra cost.

Abstract

Near-term quantum computers are accessed through repeated circuit executions, which produce finite measurement records rather than exact deterministic outputs. In quantum reservoir computing, these records are converted to feature vectors for a classical readout. The standard expectation-value approach averages all shots from one labeled time step into a single feature vector. This reduces finite-shot noise, but it also gives the readout only one training example from many circuit executions. We introduce split-ensemble training: the same shots are split into groups, and each group average is used as a separate, partially denoised feature vector for the same target. The quantum circuit, task, and measurement budget remain unchanged. Across simulated forecasting benchmarks and real hardware experiments, this simple reorganization improves prediction when full averaging leaves the readout with too few training examples, with the strongest gains observed on hardware. Our results establish shot-record organization as a simple, broadly applicable algorithmic lever for improving near-term quantum learning without additional quantum hardware cost.