Quantum-Inspired Solver for Simulating Material Deformations
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912216417042432 |
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| author | Ali, Mazen Cortines, Aser Morales, Siddhartha Mugel, Samuel Olave, Mireia Orus, Roman Palmer, Samuel Usabiaga, Hodei |
| author_facet | Ali, Mazen Cortines, Aser Morales, Siddhartha Mugel, Samuel Olave, Mireia Orus, Roman Palmer, Samuel Usabiaga, Hodei |
| contents | This paper explores the application of tensor networks (TNs) to the simulation of material deformations within the framework of linear elasticity. Material simulations are essential computational tools extensively used in both academic research and industrial applications. TNs, originally developed in quantum mechanics, have recently shown promise in solving partial differential equations (PDEs) due to their potential for exponential speedups over classical algorithms. Our study successfully employs TNs to solve linear elasticity equations with billions of degrees of freedom, achieving exponential reductions in both memory usage and computational time. These results demonstrate the practical viability of TNs as a powerful classical backend for executing quantum-inspired algorithms with significant efficiency gains. This work is based on our research conducted with IKERLAN. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12151 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum-Inspired Solver for Simulating Material Deformations Ali, Mazen Cortines, Aser Morales, Siddhartha Mugel, Samuel Olave, Mireia Orus, Roman Palmer, Samuel Usabiaga, Hodei Quantum Physics Numerical Analysis This paper explores the application of tensor networks (TNs) to the simulation of material deformations within the framework of linear elasticity. Material simulations are essential computational tools extensively used in both academic research and industrial applications. TNs, originally developed in quantum mechanics, have recently shown promise in solving partial differential equations (PDEs) due to their potential for exponential speedups over classical algorithms. Our study successfully employs TNs to solve linear elasticity equations with billions of degrees of freedom, achieving exponential reductions in both memory usage and computational time. These results demonstrate the practical viability of TNs as a powerful classical backend for executing quantum-inspired algorithms with significant efficiency gains. This work is based on our research conducted with IKERLAN. |
| title | Quantum-Inspired Solver for Simulating Material Deformations |
| topic | Quantum Physics Numerical Analysis |
| url | https://arxiv.org/abs/2501.12151 |