# Benchmarking Quantum Annealing Controls with Portfolio Optimization

**Authors:** Erica Grant, Travis Humble, Benjamin Stump  
**arXiv:** [2007.03005](https://arxiv.org/abs/2007.03005)  
**Date:** 2020-07-06  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2007.03005v1/

## Key points

- The paper explores the use of quantum annealing for finding optimal solutions in computational problems
- Quantum annealing involves tuning quantum dynamics through control mechanisms
- Portfolio optimization is used as a case study to benchmark quantum annealing controls
- Empirical results from D-Wave 2000Q are compared with computational ground truth
- Forward and reverse annealing methods are evaluated
- Different control variations are identified for optimal performance in terms of success probability and chain breaks
- Analysis of metrics provides insights into how control settings affect accuracy of quantum computations
- Research contributes to advancing the field of quantum computing by evaluating and refining control mechanisms in quantum annealers.

## Summary

The paper titled "Benchmarking Quantum Annealing Controls with Portfolio Optimization" by Erica Grant, Travis Humble, and Benjamin Stump explores the use of quantum annealing for finding optimal solutions in computational problems. Quantum annealing involves tuning the underlying quantum dynamics through control mechanisms to influence performance and error mechanisms. To address this issue, the authors utilize portfolio optimization as a case study to benchmark quantum annealing controls and assess their impact on computational accuracy. They specifically focus on comparing empirical results obtained from the D-Wave 2000Q quantum annealer with the computational ground truth across various portfolio optimization instances. The study evaluates both forward and reverse annealing methods and identifies different control variations that yield optimal performance in terms of probability of success and probability of chain breaks. By analyzing these metrics, the authors gain insights into how different control settings affect the accuracy of quantum computations. Overall, this research provides valuable insights into understanding and optimizing quantum annealing controls for improved computational performance. The findings contribute to advancing the field of quantum computing by providing a framework for evaluating and refining control mechanisms in quantum annealers.