Error Mitigation for Thermodynamic Computing
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917577425420288 |
|---|---|
| author | Aifer, Maxwell Melanson, Denis Donatella, Kaelan Crooks, Gavin Ahle, Thomas Coles, Patrick J. |
| author_facet | Aifer, Maxwell Melanson, Denis Donatella, Kaelan Crooks, Gavin Ahle, Thomas Coles, Patrick J. |
| contents | While physics-based computing can offer speed and energy efficiency compared to digital computing, it also is subject to errors that must be mitigated. For example, many error mitigation methods have been proposed for quantum computing. However this error mitigation framework has yet to be applied to other physics-based computing paradigms. In this work, we consider thermodynamic computing, which has recently captured attention due to its relevance to artificial intelligence (AI) applications, such as probabilistic AI and generative AI. A key source of errors in this paradigm is the imprecision of the analog hardware components. Here, we introduce a method that reduces the overall error from a linear to a quadratic dependence (from $ε$ to $ε^2$) on the imprecision $ε$, for Gaussian sampling and linear algebra applications. The method involves sampling from an ensemble of imprecise distributions associated with various rounding events and then merging these samples. We numerically demonstrate the scalability of this method for dimensions greater than 1000. Finally, we implement this method on an actual thermodynamic computer and show $20\%$ error reduction for matrix inversion; the first thermodynamic error mitigation experiment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16231 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Error Mitigation for Thermodynamic Computing Aifer, Maxwell Melanson, Denis Donatella, Kaelan Crooks, Gavin Ahle, Thomas Coles, Patrick J. Emerging Technologies Statistical Mechanics Quantum Physics While physics-based computing can offer speed and energy efficiency compared to digital computing, it also is subject to errors that must be mitigated. For example, many error mitigation methods have been proposed for quantum computing. However this error mitigation framework has yet to be applied to other physics-based computing paradigms. In this work, we consider thermodynamic computing, which has recently captured attention due to its relevance to artificial intelligence (AI) applications, such as probabilistic AI and generative AI. A key source of errors in this paradigm is the imprecision of the analog hardware components. Here, we introduce a method that reduces the overall error from a linear to a quadratic dependence (from $ε$ to $ε^2$) on the imprecision $ε$, for Gaussian sampling and linear algebra applications. The method involves sampling from an ensemble of imprecise distributions associated with various rounding events and then merging these samples. We numerically demonstrate the scalability of this method for dimensions greater than 1000. Finally, we implement this method on an actual thermodynamic computer and show $20\%$ error reduction for matrix inversion; the first thermodynamic error mitigation experiment. |
| title | Error Mitigation for Thermodynamic Computing |
| topic | Emerging Technologies Statistical Mechanics Quantum Physics |
| url | https://arxiv.org/abs/2401.16231 |