Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks

Mugel, Samuel; Kuchkovsky, Carlos; Sanchez, Escolastico; Fernández-Lorenzo, Samuel; Luis-Hita, Jorge; Lizaso, Enrique; Orús, Román

doi:10.1103/physrevresearch.4.013006

Cited by 66 publications

(40 citation statements)

References 16 publications

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“…The relative merits of these two methods ultimately depends on the relative effectiveness of relaxation methods and MPS based methods to approximate the optimal solution for optimization problems. There is currently no way to concretely answer this question, but there have been results demonstrating that tensor network based methods are competitive with state of the art commercial solvers for optimization problems [11,12].…”

Section: Comparison To Other Methodsmentioning

confidence: 99%

“…Tensor network based methods are the state of the art for numerical simulations of 1D and 2D spin systems [9], and also for the simulation of quantum circuits [10]. Tensor networks have been used to solve optimization problems such as portofolio optimization, and are found to be competitive with commercial solvers [11,12]. Recently, tensor network based methods have also been used as the basis for machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Matrix Product State Pre-Training for Quantum Machine Learning

Dborin¹,

Barratt²,

Wimalaweera³

et al. 2021

Preprint

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Hybrid Quantum-Classical algorithms are a promising candidate for developing uses for NISQ devices. In particular, Parametrised Quantum Circuits (PQCs) paired with classical optimizers have been used as a basis for quantum chemistry and quantum optimization problems. Training PQCs relies on methods to overcome the fact that the gradients of PQCs vanish exponentially in the size of the circuits used. Tensor network methods are being increasingly used as a classical machine learning tool, as well as a tool for studying quantum systems. We introduce a circuit pretraining method based on matrix product state machine learning methods, and demonstrate that it accelerates training of PQCs for both supervised learning, energy minimization, and combinatorial optimization.

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Section: Comparison To Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Matrix Product State Pre-Training for Quantum Machine Learning

Dborin¹,

Barratt²,

Wimalaweera³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Alternatively, Gilliam et al [137] utilized an extension of Grover adaptive search (Section 6.1.5) to solve Equation (9) with a quadratic speedup over classical unstructured search. Rosenberg et al [286] and Mugel et al [247] solved dynamic versions of Equation (9), where portfolio decisions are made for multiple time steps. Additionally, an analysis of benchmarking quantum annealers for portfolio optimization can be found in a paper by Grant et al [148].…”

Section: Combinatorial Formulations the First Combinatorial Formulati...mentioning

confidence: 99%

A Survey of Quantum Computing for Finance

Herman¹,

Googin²,

Liu³

et al. 2022

Preprint

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Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from quantum computing, not only in the medium and long terms, but even in the short term. This survey paper presents a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on Monte Carlo integration, optimization, and machine learning, showing how these solutions, adapted to work on a quantum computer, can help solve more efficiently and accurately problems such as derivative pricing, risk analysis, portfolio optimization, natural language processing, and fraud detection. We also discuss the feasibility of these algorithms on near-term quantum computers with various hardware implementations and demonstrate how they relate to a wide range of use cases in finance. We hope this article will not only serve as a reference for academic researchers and industry practitioners but also inspire new ideas for future research.

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“…In Ref. [9], we described a way to tackle this problem using quantum and quantum inspired methods. These tools allowed us to find the optimal investment trajectory spanning 52 assets and four years of data.…”

Section: Portfolio Optimizationmentioning

confidence: 99%

“…2 Sharpe ratios obtained using the different optimization algorithms considered in Ref. [9]. The dataset parameters are described in Table 1.…”

Section: Problem Fragmentationmentioning

confidence: 99%

Use Cases of Quantum Optimization for Finance

Mugel¹,

Lizaso²,

Orús³

2020

Preprint

Self Cite

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In this paper we briefly review two recent use-cases of quantum optimization algorithms applied to hard problems in finance and economy. Specifically, we discuss the prediction of financial crashes as well as dynamic portfolio optimization. We comment on the different types of quantum strategies to carry on these optimizations, such as those based on quantum annealers, universal gate-based quantum processors, and quantum-inspired Tensor Networks.

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Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks

Cited by 66 publications

References 16 publications

Matrix Product State Pre-Training for Quantum Machine Learning

Matrix Product State Pre-Training for Quantum Machine Learning

A Survey of Quantum Computing for Finance

Use Cases of Quantum Optimization for Finance

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