Financial Risk Management on a Neutral Atom Quantum Processor

Leclerc, Lucas; Ortiz-Guitierrez, Luis; Grijalva, Sebastián; Albrecht, Boris; Cline, Julia R. K.; Elfving, Vincent E.; Signoles, Adrien; Henriet, Loïc; Bimbo, Gianni Del; Sheikh, Usman Ayub; Shah, Maitree; Andrea, Luc; Ishtiaq, Faysal; Duarte, Andoni; Mugel, Samuel; Caceres, Irene; Kurek, Michel; Orús, Román; Hammammi, Oumaima; Isselnane, Hacene; M'tamon, Didier

doi:10.48550/arxiv.2212.03223

Cited by 4 publications

(1 citation statement)

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“…terms with 3 or more Z operators) and different interpretations of how the coefficients 2. A broader series of techniques for embedding the problem information into the atom register has been considered in [37,38] constitute a register matrix. A different resource Hamiltonian, such as one with XY interactions, would imply a different choice of subset.…”

Section: Atom Register and Initial Statementioning

confidence: 99%

A blueprint for a Digital-Analog Variational Quantum Eigensolver using Rydberg atom arrays

Antoine¹,

Grijalva²,

Henriet³

et al. 2023

Preprint

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We address the task of estimating the ground-state energy of Hamiltonians coming from chemistry. We study numerically the behavior of a digital-analog variational quantum eigensolver for the H2, LiH and BeH2 molecules, and we observe that one can estimate the energy to a few percent points of error leveraging on learning the atom register positions with respect to selected features of the molecular Hamiltonian and then an iterative pulse shaping optimization, where each step performs a derandomization energy estimation.

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Section: Atom Register and Initial Statementioning

confidence: 99%

A blueprint for a Digital-Analog Variational Quantum Eigensolver using Rydberg atom arrays

Antoine¹,

Grijalva²,

Henriet³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Improved financial forecasting via quantum machine learning

Thakkar,

Kazdaghli,

Mathur

et al. 2024

Quantum Mach. Intell.

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Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware.

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Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing (May 2023)

Giron,

Korpas,

Parvaiz

et al. 2023

IEEE Trans. Quantum Eng.

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Collateral optimization refers to the systematic allocation of financial assets to satisfy obligations or secure transactions, while simultaneously minimizing costs and optimizing the usage of available resources. This involves assessing number of characteristics, such as cost of funding and quality of the underlying assets to ascertain the optimal collateral quantity to be posted to cover exposure arising from a given transaction or a set of transactions. One of the common objectives is to minimise the cost of collateral required to mitigate the risk associated with a particular transaction or a portfolio of transactions while ensuring sufficient protection for the involved parties. Often, this results in a large-scale combinatorial optimization problem. In this study, we initially present a Mixed Integer Linear Programming (MILP) formulation for the collateral optimization problem, followed by a Quadratic Unconstrained Binary optimization (QUBO) formulation in order to pave the way towards approaching the problem in a hybridquantum and NISQ-ready way. We conduct local computational small-scale tests using various Software Development Kits (SDKs) and discuss the behavior of our formulations as well as the potential for performance enhancements. We find that while the QUBO based approaches fail to find the global optima in the small scale experiments, they are reasonably close suggesting their potential for large instances. We further survey the recent literature that proposes alternative ways to attack combinatorial optimization problems suitable for collateral optimization.

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Financial Risk Management on a Neutral Atom Quantum Processor

Cited by 4 publications

References 0 publications

A blueprint for a Digital-Analog Variational Quantum Eigensolver using Rydberg atom arrays

A blueprint for a Digital-Analog Variational Quantum Eigensolver using Rydberg atom arrays

Improved financial forecasting via quantum machine learning

Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing (May 2023)

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