Probing Landauer’s Principle in Dissipative Quantum Time-Evolution

Nathan X. Kodama (Case Western Reserve University)

Abstract: The complete statistical characterization of thermodynamic quantities in quantum information processes remains largely unexplored, despite its fundamental importance for quantum technologies. Here, we present an interferometric framework for measuring work distributions of dissipative quantum operations using an ancilla qubit coupled to a system-reservoir setup. Our approach enables non-destructive extraction of heat statistics through measurement of the ancilla's final state. We demonstrate our methodology by implementing quantum erasure protocols on systems coupled to thermal reservoirs modeled using random matrix theory. By measuring characteristic functions and computing inverse Fourier transforms, we successfully extract complete heat distributions and verify Landauer's principle with numerical precision. Our results reveal time-dependent thermodynamic dissipation: heat distributions concentrate around zero for short evolution times but broaden for longer durations. The experimental validation confirms that Landauer's inequality approaches equality as system dynamics become more reversible, establishing the fundamental connection between logical and thermodynamic irreversibility in the quantum regime. This work provides the first direct measurement of work fluctuations in quantum information processing, enabling thermodynamic benchmarking of quantum operations beyond traditional fidelity measures and opening new avenues for optimizing near-term quantum devices.

Introduction

This repository contains code to generate experimental results in the poster, Probing Landauer’s Principle in Dissipative Quantum Time-Evolution.

Getting Started

We created a Jupyter notebook, LandauerInterferometry.ipynb, that simulates a quantum system coupled with a reservoir.

Requirements

We tested the code in Google Collab using a Python environment running

• Python (3.10.6)
• JupyterLab (3.4.1)
• TensorFlow (2.9.1)

Install and Run

1. Open the Jupyter notebook, LandauerInterferometry.ipynb, in Google Collab and install the dependencies.
2. Run all cells to reproduce the results in the poster.