Leveraging Machine Learning to Gain Insights on Quantum Thermodynamic Entropy - Investigating Information-Driven Thermodynamic Costs

Aug 5, 2026

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Analyzing a quantum thermodynamic engine built from a single quantum particle as the working fluid, the study models a measurement–extraction–memory-reset cycle inspired by the classically chaotic Szilard Map. The work targets thermodynamic costs tied to observing and controlling the particle and contrasts these costs in quantum versus classical limits. It connects the resulting trade-offs to Lindauer’s Principle for information-processing-induced dissipation and shows how machine learning enables energy analysis and simulation under a Second Law framework, while identifying distinct quantum mechanisms driven by partition insertion costs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

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Introduction

Quantum Thermodynamic Nature

The Design of Quantum Engine

Leveraging Machine Learning to Gain Insights on Quantum Thermodynamic Entropy Srinivasa Rao. P ∇×V Techno Labs, Hyderabad, India [er.p.srinivas@gmail.com](er.p.srinivas@gmail.com) Abstract. The engine we are analyzing uses a single quantum particle as its working fluid, similar to Szilard's classical single-particle engine. Szilard resolved Maxwell's Second Law paradox by creating a physical demon to operate his engine. The design of our quantum engine is modelled after the classically-chaotic Szilard Map, which carries out a thermodynamic cycle involving measurement, thermal-energy extraction, and memory reset. Our analysis centers on studying the engine that utilizes a single quantum particle as its working fluid, which is similar in design to the classical single-particle engine constructed by Szilard. The proposed quantum engine follows the pattern of the classically-chaotic Szilard Map, which involves a thermodynamic cycle of measurement, thermal-energy extraction, and memory reset basing the focus is on investigating the thermodynamic costs associated with observing and controlling the particle, and comparing these costs in the quantum and classical limits. Through our study, we aim to shed light on the thermodynamic trade-offs that arise from Lindauer’s Principle for information-processing-induced thermodynamic dissipation in both the quantum and classical regimes. We demonstrate that by using machine learning methods the energy analysis can be performed and the quantum engine can be simulated considering the Szilard engine based Second Law of Thermodynamics in its working condition. However, the quantum engine operates using significantly different mechanisms than its classical counterpart. In the classical implementation, the thermodynamics are determined by the process of measurement and erasure. In contrast, the cost of inserting partitions plays a critical role in the quantum implementation. Keywords: Szilard Engine, Information, Entropy, Maxwell ’s Demon. 1 Introduction 1.1 Quantum Thermodynamic Nature Research on quantum thermodynamic systems is a thriving field with significant implications in various areas, such as quantum computing, quantum communication, and energy conversion. [1] Furthermore, the field plays a critical role in comprehending the behavior of small systems, like those present in nanotechnology and quantum materials. James Clerk Maxwell, a renowned physicist, challenged the concept of irreversibility introduced by the second law of thermodynamics by conducting a thought experiment known as Maxwell's demon. [2][3] In the experiment, he posited that if there existed a demon or an entity with complete particle knowledge in a gas mixture, it could transfer fast-moving particles from cold to hot reservoirs, seemingly defying the very principle that governs them. [3] The significance of Maxwell's demon lies in its contribution to discovering how entropy and information are interconnected. Through the demon's capacity to manipulate the particles in a gas with the use of information, it has become clear that there is potential for relaxation of the second law's restrictions on energy exchange between a system and its surroundings. [4] The resulting conclusions have led to ground-breaking discoveries in terms of the relationship between entropy and information, marking an important milestone within the both fields i.e in the fundamental understanding of Thermodynamics and in the field of Computer science. [5] The original formulations ofthe second law of thermodynamics by Clausius, Kelvin, or Planck did not include any reference to information, however in a more subtle way as a knowledge of the demon. [6][9] There are two challenges particularly in this case, one is to develop a detailed version of the second law that explicitly includes the role of information, besides it is necessary to establish a clear understanding of the physical properties of information, so that it can be inco

FAQs

What is the core model analyzed in this study?

The study analyzes a quantum Szilard engine that uses a single quantum particle as the working fluid, following a thermodynamic cycle of measurement, thermal-energy extraction, and memory reset.

How does the proposed quantum engine relate to Szilard’s and Maxwell’s ideas?

The design is modeled after the classically chaotic Szilard Map and is motivated by the role of information in resolving Maxwell’s Second Law paradox through a physical “demon” mechanism.

What thermodynamic costs are compared between quantum and classical limits?

The analysis focuses on thermodynamic costs associated with observing and controlling the particle, and it compares these costs in the quantum and classical regimes, noting that partition insertion is critical in the quantum implementation.

Aug 5, 2026

Upload by Logic