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End-to-End Portfolio Optimization with Quantum Annealing
Kazuki Ikeda / Rakesh Yata / Rushikesh Ubale / Sai Nandan Morapakula / Sangram Deshpande / Uday Wad
convex optimization / optimizationResource overview

End-to-End Portfolio Optimization with Quantum Annealing

Paper2025

by Kazuki Ikeda, Rakesh Yata, Rushikesh Ubale, Sai Nandan Morapakula, Sangram Deshpande, Uday Wad

Tags
convex optimizationoptimizationportfolio managementportfolio optimization

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Understand this paper

Suggested foundations for portfolio construction and optimisation and numerical methods. Specialist prerequisites vary by paper.

Before you read

  1. Eigenvalues and Covariance Matrices

    Understand the covariance structure of portfolio risk.

  2. Constrained and Convex Optimisation

    Identify the objective, constraints and conditions for an optimum.

  3. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

Go further

  1. Mean-Variance Optimisation

    Construct allocations and examine sensitivity to estimated inputs.

  2. Portfolio Constraints

    Turn mathematical weights into feasible portfolio decisions.

  3. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

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