Compare Papers

Paper 1

Increasing Extractable Work in Small Qubit Landscapes.

Akhouri U, Shandera S, Yesmurzayeva G.

Year

2023

Journal

Entropy (Basel)

DOI

10.3390/e25060947

arXiv

-

No abstract.

Open paper

Paper 2

Quantum Monte Carlo Integration for Simulation-Based Optimisation

Jingjing Cui, Philippe J. S. de Brouwer, Steven Herbert, Philip Intallura, Cahit Kargi, Georgios Korpas, Alexandre Krajenbrink, William Shoosmith, Ifan Williams, Ban Zheng

Year

2024

Journal

arXiv preprint

DOI

arXiv:2410.03926

arXiv

2410.03926

We investigate the feasibility of integrating quantum algorithms as subroutines of simulation-based optimisation problems with relevance to and potential applications in mathematical finance. To this end, we conduct a thorough analysis of all systematic errors arising in the formulation of quantum Monte Carlo integration in order to better understand the resources required to encode various distributions such as a Gaussian, and to evaluate statistical quantities such as the Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR) of an asset. Finally, we study the applicability of quantum Monte Carlo integration for fundamental financial use cases in terms of simulation-based optimisations, notably Mean-Conditional-Value-at-Risk (Mean-CVaR) and (risky) Mean-Variance (Mean-Var) optimisation problems. In particular, we study the Mean-Var optimisation problem in the presence of noise on a quantum device, and benchmark a quantum error mitigation method that applies to quantum amplitude estimation -- a key subroutine of quantum Monte Carlo integration -- showcasing the utility of such an approach.

Open paper