Quantum Physics, Quantum Simulation

American Physical Society: MPS Solver Cuts Quantum Turbulence Memory Use by 10000×

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June 30, 2026 by Ivy Delaney

A new matrix-product state (MPS) solver developed by Felipe Gómez-Lozada and colleagues reduces the computational cost of simulating quantum turbulence, achieving a 10,000-fold reduction in memory usage compared to direct numerical simulations. The team, reporting results in Physical Review Applied, efficiently compresses the complex wavefunctions by focusing on key interlength-scale correlations, enabling simulations of previously inaccessible system sizes. Researchers accurately reproduced established results from two-point correlation functions and the incompressible kinetic energy spectrum, demonstrating the solver’s reliability beyond memory savings. The MPS approach “can be adapted to existing quantum algorithms, providing a framework for implementing our time-evolution method on near-future quantum processing units,” potentially linking fundamental physics research with the rapidly advancing field of quantum computing.

Matrix-Product States Compress Wavefunctions for Quantum Turbulence

Researchers led by Felipe Gómez-Lozada compressed wavefunctions by efficiently truncating weak correlations between length scales, a critical step for modeling turbulence which spans vast ranges. Memory compression within the MPS representation proved directly proportional to the density of solitons or vortices present in turbulent states, demonstrating a scalable relationship. These findings open the door to simulating quantum turbulence at system sizes previously considered computationally prohibitive, and the team emphasizes that further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

This efficiency stems from compressing the wavefunction by selectively truncating correlations between different length scales within the system, enabling studies previously limited by computational constraints. Benchmarking focused on nonlinear excitations, dark solitons and quantized vortices, successfully capturing phenomena like Kelvin-wave propagation and vortex-ring emission, demonstrating the solver’s ability to model complex dynamics.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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