Skip to content
aiqbench takeQuantum Computing

aiqbench analysis

This study applies a variational quantum algorithm to model ion diffusion across electrolyzer membranes, demonstrating how quantum circuits can simulate complex chemical stability conditions in alkaline systems. By utilizing 4 to 6 qubits, the research provides a proof-of-concept for using quantum computing to solve physics problems with non-trivial boundary conditions that are difficult for classical methods. This work highlights a specific niche where quantum algorithms offer potential advantages in modeling electrochemical processes relevant to energy storage and conversion technologies.
Source coveringarXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Variational quantum algorithm for anion exchange across electrolyzer membrane

arXiv3 min read

arXiv:2512.01120v3 Announce Type: replace-cross Abstract: We present a variational quantum algorithm that solves the one-dimensional diffusion problem with a space-dependent diffusion constant $D(x)$. This problem is relevant for the exchange of hydroxide ions across a two-layer membrane in an alkaline electrolyzer, where the concentration of OH$^-$ ion determines the chemical stability for longer time periods. We use $16$ to $64$ grid points across the membrane, resulting from $n=4$ to $6$ data qubits for the ideal statevector and shot-based quantum simulations implemented using Qiskit. For these qubit numbers, the depth of the parametric quantum circuit has been chosen to ensure sufficient expressibility. The state preparation requires particular attention since the diffusivity $D$ is piecewise constant in the different layers with discontinuities at the interface. Furthermore, we compare different classical optimization schemes with respect to their convergence in the VQA method. We demonstrate the applicability of the quantum algorithm to a problem with non-trivial boundary conditions and jump conditions of the diffusion constant and outline possible extensions of the proof-of-concept application case of quantum computing. Our simulations show that pronounced hydroxide ion concentration gradients, and thus chemical instabilities, can occur only when the ratio of diffusivity in both layers of the membrane exceeds approximately 50.

Abstract:We present a variational quantum algorithm that solves the one-dimensional diffusion problem with a space-dependent diffusion constant $D(x)$. This problem is relevant for the exchange of hydroxide ions across a two-layer membrane in an alkaline electrolyzer, where the concentration of OH$^-$ ion determines the chemical stability for longer time periods. We use $16$ to $64$ grid points across the membrane, resulting from $n=4$ to $6$ data qubits for the ideal statevector and shot-based quantum simulations implemented using Qiskit. For these qubit numbers, the depth of the parametric quantum circuit has been chosen to ensure sufficient expressibility. The state preparation requires particular attention since the diffusivity $D$ is piecewise constant in the different layers with discontinuities at the interface. Furthermore, we compare different classical optimization schemes with respect to their convergence in the VQA method. We demonstrate the applicability of the quantum algorithm to a problem with non-trivial boundary conditions and jump conditions of the diffusion constant and outline possible extensions of the proof-of-concept application case of quantum computing. Our simulations show that pronounced hydroxide ion concentration gradients, and thus chemical instabilities, can occur only when the ratio of diffusivity in both layers of the membrane exceeds approximately 50.

Fluid Dynamics (physics.flu-dyn); Quantum Physics (quant-ph)

arXiv:2512.01120 [physics.flu-dyn]

(or arXiv:2512.01120v3 [physics.flu-dyn] for this version)

https://doi.org/10.48550/arXiv.2512.01120 Focus to learn more arXiv-issued DOI via DataCite

Phys. Rev. Applied 26, 014051 (2026)

https://doi.org/10.1103/149q-4z6s Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 309 words

Related coverage