## 223 Citations

### Recent progress and perspectives on quantum computing for finance

- Computer ScienceService Oriented Computing and Applications
- 2022

This article provides a broad overview of quantum computing for financial applications and discusses that quantum machine learning is expected to boost financial big data analysis, where the training efficiency of the model is significantly better than that of the classical model, making it more suitable to meet the need for financial institutions to offer new big datadriven services.

### Quantum Computing for Finance: State-of-the-Art and Future Prospects

- Computer ScienceIEEE Transactions on Quantum Engineering
- 2020

This article outlines the applicability, state-of-the-art, and potential of quantum computing for problems in finance, and describes in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems.

### Use Cases of Quantum Optimization for Finance

- Physics
- 2020

This paper discusses the prediction of financial crashes as well as dynamic portfolio optimization using quantum strategies based on quantum annealers, universal gate-based quantum processors, and quantum-inspired Tensor Networks.

### A Survey on Quantum Computational Finance for Derivatives Pricing and VaR

- Computer ScienceArchives of Computational Methods in Engineering
- 2022

We review the state of the art and recent advances in quantum computing applied to derivative pricing and the computation of risk estimators like Value at Risk. After a brief description of the…

### Prospects and challenges of quantum finance

- Computer Science
- 2020

Three potential applications of quantum computing to finance are described, starting with the state-of-the-art and focusing in particular on recent works by the QC Ware team, which consider quantum speedups for Monte Carlo methods, portfolio optimization, and machine learning.

### A Survey of Quantum Computing for Finance

- Computer Science
- 2022

A comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning, describing how these solutions, adapted to work on a quantum computer, can potentially help to solve financial problems more efficiently and accurately.

### Quantum pricing with a smile: implementation of local volatility model on quantum computer

- Computer ScienceEPJ Quantum Technology
- 2022

This paper considers the local volatility (LV) model, in which the volatility of the underlying asset price depends on the price and time, and discusses the state preparation step of the QAE, or equivalently, the implementation of the asset price evolution.

### A Structured Survey of Quantum Computing for the Financial Industry

- Computer Science
- 2022

This survey reviews platforms, algorithms, methodologies, and use cases of quantum computing for various applications in ﬁnance in a structured way to gain an overview of the current development and capabilities and understand the potential of quantum Computing in theancial industry.

### Forecasting financial crashes with quantum computing

- EconomicsPhysical Review A
- 2019

A key problem in financial mathematics is the forecasting of financial crashes: if we perturb asset prices, will financial institutions fail on a massive scale? This was recently shown to be a…

### Option Pricing using Quantum Computers

- Physics, Computer ScienceQuantum
- 2020

We present a methodology to price options and portfolios of options on a gate-based quantum computer using amplitude estimation, an algorithm which provides a quadratic speedup compared to classical…

## References

SHOWING 1-10 OF 91 REFERENCES

### Quantum computational finance: Monte Carlo pricing of financial derivatives

- Computer SciencePhysical Review A
- 2018

This work presents a quantum algorithm for the Monte Carlo pricing of financial derivatives and shows 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.

### Quantum risk analysis

- Computer Sciencenpj Quantum Information
- 2019

A quantum algorithm that analyzes risk more efficiently than Monte Carlo simulations traditionally used on classical computers is presented and a near quadratic speed-up compared to Monte Carlo methods is provided.

### Generalized Optimal Trading Trajectories: A Financial Quantum Computing Application

- Computer Science
- 2015

This brief note demonstrates how this financial problem, intractable to modern supercomputers, can be reformulated as an integer optimization problem, which makes it amenable to quantum computers.

### Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers

- Computer Science, PhysicsQuantum Science and Technology
- 2018

It is argued that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning techniques.

### Can small quantum systems learn

- Computer ScienceQuantum Inf. Comput.
- 2017

There may be a connection between fault tolerance and the capacity of a quantum system to learn from its surroundings, and a new adaptive form of approximate quantum Bayesian inference is provided.

### Tomography and generative training with quantum Boltzmann machines

- Computer Science, Physics
- 2017

It is demonstrated that quantum Boltzmann machines enable a form of quantum state tomography that not only estimates a state but provides a prescription for generating copies of the reconstructed state as well as evidence that quantum models outperform their classical counterparts.

### Quantum deep learning

- Computer ScienceQuantum Inf. Comput.
- 2016

It is shown that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function.

### Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

- Computer Science
- 2016

A systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures.

### Quantum Perceptron Models

- Computer Science, PhysicsNIPS
- 2016

Two quantum algorithms for perceptron learning are developed that demonstrate how quantum computation can provide non-trivial improvements in the computational and statistical complexity of the perceptron model.

### Quantum-enhanced machine learning

- Computer SciencePhysical review letters
- 2016

This work proposes an approach for the systematic treatment of machine learning, from the perspective of quantum information, and shows that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.