Shot- and Noise-Aware Pauli Correlation Encoding for Max-Cut Experiments on IBM Heron Quantum Processor
A preprint introduces a Pauli correlation encoding scheme for Max-Cut that compresses binary variables into signs of k-body Pauli correlators and trains a parameterized circuit with a relaxed loss controlled by a sharpness parameter. It notes that the strongest published schedules tune that sharpness assuming exact expectation values are available. The work aims to adapt the approach to the finite shot counts and device noise present on IBM Heron.
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What this could mean
- 0–2 yearsPlausible
Shot- and noise-aware Pauli correlation encoding could enable the first Max-Cut demonstrations on IBM Heron that are trained directly from noisy quantum measurements, rather than exact simulations.
IBM Heron processors are already accessible, and the identified gap is the mismatch between exact-expectation benchmarks and real hardware sampling; a schedule that accounts for shot and device noise addresses that mismatch directly. If the method stabilizes training under finite sampling, compressed Max-Cut encodings could be tested on available devices within two years.
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