Dataset for Price Optimization under Cross‑Elastic Demand through Hybrid Quantum Annealing

This repository contains the experimental datasets employed in the evaluation of a hybrid quantum–classical approach for price optimization under elastic demand with cross-item dependencies. The datasets simulate item catalogs with varying levels of complexity, including different numbers of items, admissible price points, profit ranges, and cross-elasticity densities. Each dataset consists of two complementary files: Price dataset: specifies the eligible prices and expected profit for each item. Cross-elasticity dataset: encodes the effect of the price of one item on the profit of another, expressed as percentage variations. Four experimental datasets are provided: Baseline: 10 items, 2–3 prices per item, profit range $100–500, elasticity density 0.8. Small: 50 items, 2–3 prices per item, profit range $100–500, elasticity density 0.8. Medium: 500 items, 2–5 prices per item, profit range $50–1,000, elasticity density 0.7. Large: 2,500 items, 2–5 prices per item, profit range $100–2,000, elasticity density 0.6. The datasets were generated synthetically using a controlled random process to ensure reproducibility across runs, with fixed random seeds. They are designed to reflect realistic catalog structures and interdependencies, while remaining suitable for benchmarking hybrid quantum–classical optimization methods. Intended Use: These datasets can be used to validate and replicate the experiments described in Hybrid Quantum Annealing for Price Optimization under Cross-Elastic Demand, as well as to benchmark alternative quantum or classical optimization strategies in pricing and related combinatorial domains. Format: CSV files (price dataset and cross-elasticity dataset for each experimental scenario).