Using Machine Learning to Understand the Causes of Quantum Decoherence in Solution-Phase Bond-Breaking Reactions - Abstract Summary

Aug 3, 2026

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Decoherence emerges when an entangled quantum state interacts with its surroundings, collapsing the wave function and imposing fundamental limits on quantum computing. Yet the specific chemical motions in solution that drive decoherence remain insufficiently understood. This work applies quantum molecular dynamics and machine learning to photodissociation of Na2+ in liquid Ar, showing solvent fluctuations trigger electron localization and determine bond-breaking products. It identifies key mechanisms including required photofragment separation and out-of-phase solvent collisions, supporting interpretation of complex solution-phase processes.

LanguageEnglish

CategoryResearch & Report

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Pages10

Reading Time25 min

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Abstract

Quantum decoherence and its relevance to quantum technologies

Simulation and machine-learning approach for Na2+ photodissociation in liquid Ar

Molecular mechanisms: solvent fluctuations, electron localization, and solvent collision timing

UCLA UCLA Previously Published Works Title Using Machine Learning to Understand the Causes of Quantum Decoherence in SolutionPhase Bond-Breaking Reactions. Permalink [https://escholarship.org/uc/item/9zc1d77j](https://escholarship.org/uc/item/9zc1d77j) Journal Journal of Physical Chemistry Letters, 15(4) Authors Mei, Kenneth Borrelli, William Vong, Andyet al. Publication Date 2024-02-01 DOI 10.1021/acs.jpclett.3c03474 Peer reviewed [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California This article is licensed under CC-BY 4.0 [pubs.acs.org/JPCL](pubs.acs.org/JPCL) Letter Using Machine Learning to Understand the Causes of Quantum Decoherence in Solution-Phase Bond-Breaking Reactions Kenneth J. Mei, William R. Borrelli, Andy Vong, and Benjamin J. Schwartz * Cite This: J. Phys. Chem. Lett. 2024, 15, 903−911 Read Online | | | | | | | | --- | --- | --- | --- | --- | --- | | ACCESS | Metrics & More | | Article Recommendations | | *sı Supporting Information | ABSTRACT: Decoherence is a fundamental phenomenon that occurs when an entangled quantum state interacts with its environment, leading to collapse of the wave function. The inevitability of decoherence provides one of the most intrinsic limits of quantum computing. However, there has been little study of the precise chemical motions from the environment that cause decoherence. Here, we use quantum molecular dynamics simulations to explore the photodissociation of Na2+ in liquid Ar, in which solvent fluctuations induce decoherence and thus determine the products of chemical bond breaking. We use machine learning to characterize the solute−solvent environment as a high-dimensional feature space that allows us to predict when and onto which photofragment the bonding electron will localize. We find that reaching a requisite photofragment separation and experiencing out-of-phase solvent collisions underlie decoherence during chemical bond breaking. Our work highlights the utility of machine learning for interpreting complex solution-phase chemical processes as well as identifies the molecular underpinnings of decoherence. The fact that quantum systems can exist in a superposition of coherent quantum states is what gives rise to their utility in the emergent field of quantum information science. When such an entangled quantum system interacts with a fluctuating environment, motions of the bath can make a“measurement” on the system, breaking the entanglement and collapsing the system into an eigenstate. 1−5 This phenomenon, known as quantum decoherence, provides the key limitation on technologies such as quantum computing, quantum communications, and quantum metrology.6−8 The usual approach to decreasing the rate of quantum decoherence is simply to lower the temperature, thus reducing the frequency and amplitude of bath fluctuations that couple to the entangled 6 quantum system. Despite all the interest, there are only a handful of studies9−19 that have worked to provide a microscopic picture of how bath motions couple to a quantum system and causedecoherence or that investigate whether restricting certain types of bath motions might allow chemical systems to remain entangled at higher temperatures. Most common theoretical approaches are derived from a generalized master equation and treat the loss of quantum coherence by introducing empirical off-diagonal terms in the system density matrix,2,5,20−22 which provides little insight into understanding precisely what types of underlying bath motions or coupling are responsible. A few studies have examined decoherence using an explicit bath representation, notably the works of Sanz et al.23 and Elran et al.,24 who used a classical analogue approach involving a Wigner distribution for initial quantum states and molecular dynamics simulations to study the vibrational decoherence of I2 in a bath of liquid xenon. In this

FAQs

Why is quantum decoherence important for quantum computing and related technologies?

Quantum decoherence limits quantum information technologies because interactions with fluctuating environments collapse entangled quantum states into eigenstates.

How does the study use machine learning in the context of quantum decoherence?

The research represents the solute–solvent environment as a high-dimensional feature space to predict when and onto which photofragment the bonding electron localizes during photodissociation.

What mechanisms underlie decoherence during chemical bond breaking in the reported simulations?

Decoherence is associated with reaching a requisite photofragment separation and experiencing out-of-phase solvent collisions that drive solvent-induced collapse.

Aug 3, 2026

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