Quantitative risk management, particularly volatility forecasting, is critically important to traders, portfolio managers as well as policy makers. 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. We test the forecast performance over the sub-prime mortgage crisis period (Dec 2007 - Jun 2009) . Our results show a remarkable ability to predict the volatility during the Great Recession using today's NISQs .
Forecasting the CBOE volatility index (VIX) is a highly non-linear and memory-intensive task. In this paper, we use quantum reservoir computing to forecast the VIX using S P500 (SPX) time-series. Our reservoir is a hybrid quantum-classical system executed on IBM's 53-qubit Rochester chip. We encode the SPX values in the rotation angles and linearly combine the average spin of the six-qubit register to predict the value of VIX at the next time step . Our results demonstrate a potential application of noisy intermediate scale quantum (NISQ) devices to complex, real world applications .
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Quantitative risk management, particularly volatility forecasting, is critically important to traders, portfolio managers as well as policy makers. In this paper, we applied quantum reservoir computing for forecasting VIX (
Forecasting
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(VIX) is
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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
memory-intensive task. In this paper, we use quantum reservoir computing to forecast the VIX using S
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P500 (SPX) time-series. Our reservoir is a hybrid quantum-classical system executed on IBM's 53-qubit Rochester chip. We encode the SPX values in the rotation angles and linearly combine
R
a 6-qubit quantum register trained using VIX and SPX data from year 1990 onwards. We test the forecast performance over the sub-prime mortgage crisis period (Dec 2007 - Jun 2009)
the six-qubit register to predict the value of VIX at the next time step
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show a remarkable ability to predict the volatility during the Great Recession using today's NISQs
demonstrate a potential application of noisy intermediate scale quantum (NISQ) devices to complex, real world applications