Before revision:  In this paper, we applied quantum reservoir computing for forecasting VIX ( the CBOE volatility index ), a highly non-linear and memory intensive `real-life' signal that is driven by market dynamics and trader psychology and cannot be expressed by a deterministic equation. As a first step, we lay out the systematic design considerations for using a NISQ reservoir as a computing engine (which should be useful for practitioners) . We then show how to experimentally evaluate the memory capacity of various reservoir topologies (using IBM-Q's Rochester device) to identify the configuration with maximum memory capacity. Once the optimal design is selected, the forecast is produced by a linear combination of  the average spin of a 6-qubit quantum register trained using VIX and SPX data from year 1990 onwards.
After revision:  In this paper, we applied quantum reservoir computing for forecasting VIX ( the CBOE volatility index ), a highly non-linear and memory-intensive task. In this paper, we use quantum reservoir computing to forecast the VIX using S the average spin of a 6-qubit quantum register trained using VIX and SPX data from year 1990 onwards.