Swap curve fitting in TensorFlow Quant Finance (Jupyter notebook)
This article shows how to use TensorFlow to fit the swap curve and price interest rate derivatives.
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This article discusses a technique for fitting a swap curve using TensorFlow Quantum (TFQ). The swap curve is a financial instrument that is used to measure the cost of borrowing and lending in a currency. The technique involves using a quantum circuit to represent the swap curve and then training it to fit the data. The article provides a code example of how to use TFQ to fit a swap curve.
TFQ is a library that combines the power of quantum computing and machine learning to solve problems in finance. It allows users to build quantum circuits that can be used to represent financial instruments such as the swap curve. The article demonstrates how to use TFQ to fit a swap curve to data.
The article includes a code example of how to use TFQ to fit a swap curve. The code example shows how to define a quantum circuit, train it to fit the data, and then measure the performance of the model. The article also discusses how to interpret the results of the fitting.
This article provides an overview of how to use TensorFlow Quantum to fit a swap curve to data. It includes a code example that demonstrates how to define a quantum circuit, train it to fit the data, and then measure the performance of the model. The article also explains how to interpret the results of the fitting.
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