Quantum Computing winks at statistics. Is it a good match?

Adriano Baldeschi () and Giuseppe Bruno ()
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Adriano Baldeschi: Bank of Italy
Giuseppe Bruno: Bank of Italy

No 843, Questioni di Economia e Finanza (Occasional Papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: Quantum computers are widely expected to foster innovation in many fields. Statistical modeling surely has an unquenchable thirst for computing cycles, no matter where they come from. Quantum computing holds great potential to push ahead of its current limits in computational statistics. This paper explores the present suitability of a quantum machine to carry out a basic statistical estimation method, such as the maximum likelihood (ML) estimation for binary discrimination, specifically the Logit model. In this adventure, we encountered different modelling tasks required to map our original mathematical model into a form more suitable to be dealt with by a quantum computer, such as the D-Wave quantum annealer. This platform is specialized for solving Quadratic Unconstrained Binary Optimization (QUBO) problems. Our results show how to leverage these types of computing resources, which appear to be emerging as the main players. The performances and accuracy achieved in this example look quite promising.

Keywords: quantum-computing; statistics; econometrics; logit-model (search for similar items in EconPapers)
JEL-codes: C01 C02 C10 (search for similar items in EconPapers)
Date: 2024-04
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