Brajesh Gupt
Research Subjects
Brajesh Gupt has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Patrick Rebentrost
Xanadu, 372 Richmond Street West, Toronto, Ontario, Canada M5V 1X6
2 shared publications - Thomas R. Bromley
Xanadu, 372 Richmond Street West, Toronto, Ontario, Canada M5V 1X6
2 shared publications Juan Miguel Arrazola
Xanadu, Toronto, CANADA
1 shared publication- Nicolás Quesada
Ecole Polytechnique de Montréal
1 shared publication Thomas R. Bromley
Xanadu, Toronto, CANADA
1 shared publication
Latest Publications
- 18 July 2020
- Published by Springer Nature in Quantum Information Processing
- Vol. 19 (8) , 1-14
Classical benchmarking of Gaussian Boson Sampling on the Titan supercomputer
- 18 July 2020
- Published by Springer Nature in Quantum Information Processing
- Vol. 19 (8) , 1-14
Classical benchmarking of Gaussian Boson Sampling on the Titan supercomputer
- Open Access
Quantum computational finance: Monte Carlo pricing of financial derivatives
Financial derivatives are contracts that can have a complex payoff dependent upon underlying benchmark assets. In this work, we present a quantum algorithm for the Monte Carlo pricing of financial derivatives. We show how the relevant probability distributions can be prepared in quantum superposition, the payoff functions can be implemented via quantum circuits, and the price of financial derivatives can be extracted via quantum measurements. We show how the amplitude estimation algorithm can be applied to achieve a quadratic quantum speedup in the number of steps required to obtain an estimate for the price with high confidence. This work provides a starting point for further research at the interface of quantum computing and finance.
Open AccessQuantum computational finance: Monte Carlo pricing of financial derivatives
Financial derivatives are contracts that can have a complex payoff dependent upon underlying benchmark assets. In this work, we present a quantum algorithm for the Monte Carlo pricing of financial derivatives. We show how the relevant probability distributions can be prepared in quantum superposition, the payoff functions can be implemented via quantum circuits, and the price of financial derivatives can be extracted via quantum measurements. We show how the amplitude estimation algorithm can be applied to achieve a quadratic quantum speedup in the number of steps required to obtain an estimate for the price with high confidence. This work provides a starting point for further research at the interface of quantum computing and finance.
- Top Cited Papers
- 20 August 2018
- Published by American Physical Society (APS) in Physical Review A
- Vol. 98 (2) , 022321
Quantum computational finance: Monte Carlo pricing of financial derivatives
This work presents a quantum algorithm for the Monte Carlo pricing of financial derivatives. We show how the relevant probability distributions can be prepared in quantum superposition, the payoff functions can be implemented via quantum circuits, and the price of financial derivatives can be extracted via quantum measurements. We show how the amplitude estimation algorithm can be applied to achieve a quadratic quantum speedup in the number of steps required to obtain an estimate for the price with high confidence. This work provides a starting point for further research at the interface of quantum computing and finance.
- 20 August 2018
- Published by American Physical Society (APS) in Physical Review A
- Vol. 98 (2) , 022321
Top Cited PapersQuantum computational finance: Monte Carlo pricing of financial derivatives
This work presents a quantum algorithm for the Monte Carlo pricing of financial derivatives. We show how the relevant probability distributions can be prepared in quantum superposition, the payoff functions can be implemented via quantum circuits, and the price of financial derivatives can be extracted via quantum measurements. We show how the amplitude estimation algorithm can be applied to achieve a quadratic quantum speedup in the number of steps required to obtain an estimate for the price with high confidence. This work provides a starting point for further research at the interface of quantum computing and finance.