Use Cases of Quantum Optimization for Finance

Mugel, Samuel; Lizaso, Enrique; Orús, Román

doi:10.48550/arxiv.2010.01312

Cited by 4 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An alternative approach to this problem was presented in Refs. [9,10], where ways to tackle this type of problem using quantum annealers were presented. In particular, a mathematically identical problem was simulated, and the corresponding results measured [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer

Ding

Gonzalez-Conde

Lamata

et al. 2023

Entropy

View full text Add to dashboard Cite

The prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can efficiently find optimal solutions. We experimentally explore a novel approach to this problem by using a D-Wave quantum annealer, benchmarking its performance for attaining a financial equilibrium. To be specific, the equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed into a spin-1/2 Hamiltonian with at most, two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. The size of the simulation is mainly constrained by the necessity of a large number of physical qubits representing a logical qubit with the correct connectivity. Our experiment paves the way for the codification of this quantitative macroeconomics problem in quantum annealers.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In this paper, we experimentally implement the study presented in Refs. [9,10]. Specifically, we compute the equilibrium configuration of a financial network before and after a perturbation with a D-Wave 2000Q quantum annealer and compare the results to alternative methods.…”

Section: Introductionmentioning

confidence: 99%

Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer

Ding

Gonzalez-Conde

Lamata

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…Minimum holding period.-The holding period is the amount of time which elapses between an investment's purchase and its sale (or sale of a security). Because longterm gains are taxed more favourably than short-term w (1) t=0 w (1) t=1 w (1) t=2 w (3) t=2 w (1) t=3 w (4) t=2 w (2) t=3 w (3) t=3 w (4) t=3 w (5) t=3 w (6) t=3 w (7) t=3 w (8) t=3 w (2) t=1 w (2) represents the i th candidate holdings at time t. Green nodes meet the minimum holding period, while grey nodes do not. When the constraint is not met at time t, the node is crossed out and all resulting investment trajectories are eliminated.…”

mentioning

confidence: 99%

“…See Refs. [3][4][5][6][7] for some examples. In this setting, the most paradigmatic optimization problem in finance is that of portfolio optimization, both in its static and dynamic versions.…”

mentioning

confidence: 99%

Hybrid quantum investment optimization with minimal holding period

Mugel

Abad

Bermejo

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio optimization with minimal holding period. Our algorithm is based on sampling the near-optimal portfolios at each trading step using a quantum processor, and efficiently post-selecting to meet the minimal holding constraint. We found the optimal investment trajectory in a dataset of 50 assets spanning a 1 year trading period using the D-Wave 2000Q processor. Our method is remarkably efficient, and produces results much closer to the efficient frontier than typical portfolios. Moreover, we also show how our approach can easily produce trajectories adapted to different risk profiles, as typically offered in financial products. Our results are a clear example of how the combination of quantum and classical techniques can offer novel valuable tools to deal with real-life problems, beyond simple toy models, in current NISQ quantum processors.

show abstract

“…This is a core open problem in finance, which relies on an accurate forecasting of risk and market predictions. Accurate intrinsic long-term value estimation is crucial to portfolio optimization applications, which has been heavily investigated in the context of quantum computing [10][11][12][13][14][15][16][17]. Moreover, we implement the method in two different quantum computing architectures based on trapped ions: one provided by IonQ [18], and one described in Ref.…”

mentioning

confidence: 99%

Quantum portfolio value forecasting

Sanz-Fernández¹,

Hernández²,

Marciniak³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We present an algorithm which efficiently estimates the intrinsic long-term value of a portfolio of assets on a quantum computer. The method relies on quantum amplitude estimation to estimate the mean of a novel implementation of the Gordon-Shapiro formula. The choice of loading and readout algorithms makes it possible to price a five-asset portfolio on present day quantum computers, a feat which has not been realised using quantum computing to date. We compare results from two available trapped ion quantum computers. Our results are consistent with classical benchmarks, but result in smaller statistical errors for the same computational cost.

show abstract

Use Cases of Quantum Optimization for Finance

Cited by 4 publications

References 11 publications

Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer

Toward Prediction of Financial Crashes with a D-Wave Quantum Annealer

Hybrid quantum investment optimization with minimal holding period

Quantum portfolio value forecasting

Contact Info

Product

Resources

About