# Adaptive Learning for Quantum Linear Regression

arXiv: https://arxiv.org/abs/2408.02833 · HTML: https://mlsanity.com/paper/2408.02833/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Adiabatic Quantum Linear Regression](https://mlsanity.com/paper/2008.02355/) (2020)
- [Quadratic Unconstrained Binary Optimization Problem Preprocessing: Theory and Empirical Analysis](https://mlsanity.com/paper/1705.09844/) (2017)

## Built on by (most cited first, 1 of 1)

- [Minor Embedding for Quantum Annealing with Reinforcement Learning](https://mlsanity.com/paper/2507.16004/) (2025)

## Abstract (arXiv)

The recent availability of quantum annealers as cloud-based services has enabled new ways to handle machine learning problems, and several relevant algorithms have been adapted to run on these devices. In a recent work, linear regression was formulated as a quadratic binary optimization problem that can be solved via quantum annealing. Although this approach promises a computational time advantage for large datasets, the quality of the solution is limited by the necessary use of a precision vector, used to approximate the real-numbered regression coefficients in the quantum formulation. In this work, we focus on the practical challenge of improving the precision vector encoding: instead of setting an array of generic values equal for all coefficients, we allow each one to be expressed by its specific precision, which is tuned with a simple adaptive algorithm. This approach is evaluated on synthetic datasets of increasing size, and linear regression is solved using the D-Wave Advantage quantum annealer, as well as classical solvers. To the best of our knowledge, this is the largest dataset ever evaluated for linear regression on a quantum annealer. The results show that our formulation is able to deliver improved solution quality in all instances, and could better exploit the potential of current quantum devices.