# Non-Ideal Measurement Heat Engines

**Authors:** Abhisek Panda, Felix C. Binder, Sai Vinjanampathy  
**arXiv:** [2308.02381](https://arxiv.org/abs/2308.02381)  
**Date:** 2023-08-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2308.02381v1/

## Key points

- Authors Abhisek Panda, Felix C. Binder, and Sai Vinjanampathy explore the role of non-ideal measurements in measurement engines.
- Thermally correlated engines can outperform entanglement counterparts in efficiency for a given work output.
- Understanding and leveraging thermal correlations is crucial for enhancing performance in measurement engines.
- Factors such as the cost of resetting, correlating the engine to a pointer state, and cooling the pointer state impact engine performance.
- Different types of correlations and temperatures affect the effectiveness of measurement engines.
- Optimal efficiency in measurement engines is achieved with thermally correlated engines operating at higher temperature pointers compared to entanglement engines at optimal conditions.
- The study sheds light on how thermal correlations enhance performance in measurement engines.

## Summary

In their paper titled "Non-Ideal Measurement Heat Engines," authors Abhisek Panda, Felix C. Binder, and Sai Vinjanampathy delve into the intricate role of non-ideal measurements within the realm of measurement engines. Through their analysis, Panda et al. demonstrate that thermally correlated engines have the potential to outperform their entanglement counterparts in terms of efficiency for a given work output. This finding underscores the importance of understanding and leveraging thermal correlations in measurement engines to enhance performance. The authors present examples of measurement engines that yield the same work output but exhibit varying levels of entanglement, highlighting the significance of considering factors such as the cost of resetting, correlating the engine to a pointer state, and cooling the pointer state. Their study provides valuable insights into how different types of correlations and temperatures can impact the overall effectiveness of measurement engines. Furthermore, Panda et al. reveal an intriguing insight regarding optimal efficiency in measurement engines. They show that thermally correlated measurement engines achieve peak efficiency when operating with a higher temperature pointer compared to entanglement engines operating at optimal conditions. This observation sheds light on how thermal correlations play a crucial role in enhancing performance. Overall,<Organization>, Binder, and Vinjanampathy's study provides valuable insights into the intricate interplay between non-ideal measurements, entanglement, thermal correlations, and efficiency in measurement engines. Their findings pave the way for further exploration and optimization of these systems for enhanced performance in various applications.