تقرير
Nonlinear input transformations are ubiquitous in quantum reservoir computing
| العنوان: | Nonlinear input transformations are ubiquitous in quantum reservoir computing |
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| المؤلفون: | Govia, L. C. G., Ribeill, G. J., Rowlands, G. E., Ohki, T. A. |
| سنة النشر: | 2021 |
| المجموعة: | Condensed Matter Quantum Physics |
| مصطلحات موضوعية: | Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks |
| الوصف: | The nascent computational paradigm of quantum reservoir computing presents an attractive use of near-term, noisy-intermediate-scale quantum processors. To understand the potential power and use cases of quantum reservoir computing, it is necessary to define a conceptual framework to separate its constituent components and determine their impacts on performance. In this manuscript, we utilize such a framework to isolate the input encoding component of contemporary quantum reservoir computing schemes. We find that across the majority of schemes the input encoding implements a nonlinear transformation on the input data. As nonlinearity is known to be a key computational resource in reservoir computing, this calls into question the necessity and function of further, post-input, processing. Our findings will impact the design of future quantum reservoirs, as well as the interpretation of results and fair comparison between proposed designs. Comment: 9 pages, 1 figure |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2107.00147 |
| رقم الأكسشن: | edsarx.2107.00147 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |