# Application of deep quantum neural networks to finance

@article{Sakuma2020ApplicationOD, title={Application of deep quantum neural networks to finance}, author={T. Sakuma}, journal={arXiv: Computational Finance}, year={2020} }

Use of the deep quantum neural network proposed by Beer et al. (2020) could grant new perspectives on solving numerical problems arising in the field of finance. We discuss this potential in the context of simple experiments such as learning implied volatilites and differential machine proposed by Huge and Savine (2020). The deep quantum neural network is considered to be a promising candidate for developing highly powerful methods in finance.

#### 3 Citations

Neural networks-based algorithms for stochastic control and PDEs in finance

- Mathematics, Economics
- 2021

This paper presents machine learning techniques and deep reinforcement learningbased algorithms for the efficient resolution of nonlinear partial differential equations and dynamic optimization… Expand

Quantum Accelerator Stack: A Research Roadmap

- Computer Science, Physics
- ArXiv
- 2021

This paper presents explicitly the idea of a quantum accelerator which contains the full stack of the layers of an accelerator, and introduces two quantum applications that emphasise the use of perfect qubits and supercomputers to compute the result. Expand

Quantum Machine Learning for Finance

- Computer Science, Physics
- ArXiv
- 2021

The state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning are presented. Expand

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