# Quantum Computing Methods for Supply Chain Management

arXiv: https://arxiv.org/abs/2209.08246 · HTML: https://mlsanity.com/paper/2209.08246/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 17 Sep 2022.

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

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Predicting Many Properties of a Quantum System from Very Few Measurements](https://mlsanity.com/paper/2002.08953/) (2020)

## Built on by (most cited first, 2 of 2)

- [Fundamental causal bounds of quantum random access memories](https://mlsanity.com/paper/2307.13460/) (2023)
- [Uncertainty in Supply Chain Digital Twins: A Quantum-Classical Hybrid Approach](https://mlsanity.com/paper/2411.10254/) (2024)

## Abstract (arXiv)

Quantum computing is expected to have transformative influences on many domains, but its practical deployments on industry problems are underexplored. We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management. Many problems in supply chain management involve large state and action spaces and pose computational challenges on classic computers. We develop a quantized policy iteration algorithm to solve an inventory control problem and demonstrative its effectiveness. We also discuss in-depth the hardware requirements and potential challenges on implementing this quantum algorithm in the near term. Our simulations and experiments are powered by textttIBM Qiskit and the textttqBraid system.