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Towards Quantum Advantage in Financial Market Risk using Quantum Gradient Algorithms
Nikitas Stamatopoulos, Guglielmo Mazzola, Stefan Woerner +1
We introduce a quantum algorithm to compute the market risk of financial derivatives. Previous work has shown that quantum amplitude estimation can accelerate derivative pricing qu…
A variational quantum algorithm for the Feynman-Kac formula
Hedayat Alghassi, Amol Deshmukh, Noelle Ibrahim +3
We propose an algorithm based on variational quantum imaginary time evolution for solving the Feynman-Kac partial differential equation resulting from a multidimensional system of…
Error Bounds for Variational Quantum Time Evolution
Christa Zoufal, David Sutter, Stefan Woerner
Variational quantum time evolution allows us to simulate the time dynamics of quantum systems with near-term compatible quantum circuits. Due to the variational nature of this meth…
Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
Sau Lan Wu, Shaojun Sun, Wen Guan +20
Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In…
Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information
Julien Gacon, Christa Zoufal, Giuseppe Carleo +1
The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution.…
Quasiprobability decompositions with reduced sampling overhead
Christophe Piveteau, David Sutter, Stefan Woerner
Quantum error mitigation techniques can reduce noise on current quantum hardware without the need for fault-tolerant quantum error correction. For instance, the quasiprobability me…