Exploiting Symmetry in Quantum Reservoir Computing

Quantum reservoir computing (QRC) uses a quantum processor without training it. The input is encoded into a quantum state, a fixed random circuit evolves it, selected observables are measured, and only a simple linear readout is trained on the measured values. We study QRC for forecasting on a ring of sensors, such as weather stations around a circle of latitude. On such a ring, the same physical rule governs every position, so a model that respects this symmetry can learn one shared local rule from all sensors at once instead of a separate rule per sensor. This is especially valuable when data is scarce. Because the readout sees only the measured numbers, the symmetry can be lost even when the quantum state respects it. We show how to preserve the symmetry by measuring every observable together with all its shifted copies and by using one shared rule for encoding, circuit, and readout, and we prove that this construction is sufficient. In a controlled audit the aligned design consistently outperforms misaligned alternatives, and the advantage persists in noisy simulations, on IBM hardware, and on real weather data.