Time-series quantum reservoir computing with weak and projective measurements
Abstract Time-series processing is a major challenge in machine learning with enormous progress in the last years in tasks such as speech recognition and chaotic series prediction. A promising avenue for sequential data analysis is quantum machine learning, with computational models like quantum neu...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-02-01
|
| Series: | npj Quantum Information |
| Online Access: | https://doi.org/10.1038/s41534-023-00682-z |
| _version_ | 1797863940243324928 |
|---|---|
| author | Pere Mujal Rodrigo Martínez-Peña Gian Luca Giorgi Miguel C. Soriano Roberta Zambrini |
| author_facet | Pere Mujal Rodrigo Martínez-Peña Gian Luca Giorgi Miguel C. Soriano Roberta Zambrini |
| author_sort | Pere Mujal |
| collection | DOAJ |
| description | Abstract Time-series processing is a major challenge in machine learning with enormous progress in the last years in tasks such as speech recognition and chaotic series prediction. A promising avenue for sequential data analysis is quantum machine learning, with computational models like quantum neural networks and reservoir computing. An open question is how to efficiently include quantum measurement in realistic protocols while retaining the needed processing memory and preserving the quantum advantage offered by large Hilbert spaces. In this work, we propose different measurement protocols and assess their efficiency in terms of resources, through theoretical predictions and numerical analysis. We show that it is possible to exploit the quantumness of the reservoir and to obtain ideal performance both for memory and forecasting tasks with two successful measurement protocols. One repeats part of the experiment after each projective measurement while the other employs weak measurements operating online at the trade-off where information can be extracted accurately and without hindering the needed memory, in spite of back-action effects. Our work establishes the conditions for efficient time-series processing paving the way to its implementation in different quantum technologies. |
| first_indexed | 2024-04-09T22:43:42Z |
| format | Article |
| id | doaj.art-50502e1f1e2b4cbf9164672f4f57edb7 |
| institution | Directory Open Access Journal |
| issn | 2056-6387 |
| language | English |
| last_indexed | 2024-04-09T22:43:42Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Quantum Information |
| spelling | doaj.art-50502e1f1e2b4cbf9164672f4f57edb72023-03-22T11:59:25ZengNature Portfolionpj Quantum Information2056-63872023-02-019111010.1038/s41534-023-00682-zTime-series quantum reservoir computing with weak and projective measurementsPere Mujal0Rodrigo Martínez-Peña1Gian Luca Giorgi2Miguel C. Soriano3Roberta Zambrini4IFISC, Institut de Física Interdisciplinària i Sistemes Complexos (UIB-CSIC), UIB CampusIFISC, Institut de Física Interdisciplinària i Sistemes Complexos (UIB-CSIC), UIB CampusIFISC, Institut de Física Interdisciplinària i Sistemes Complexos (UIB-CSIC), UIB CampusIFISC, Institut de Física Interdisciplinària i Sistemes Complexos (UIB-CSIC), UIB CampusIFISC, Institut de Física Interdisciplinària i Sistemes Complexos (UIB-CSIC), UIB CampusAbstract Time-series processing is a major challenge in machine learning with enormous progress in the last years in tasks such as speech recognition and chaotic series prediction. A promising avenue for sequential data analysis is quantum machine learning, with computational models like quantum neural networks and reservoir computing. An open question is how to efficiently include quantum measurement in realistic protocols while retaining the needed processing memory and preserving the quantum advantage offered by large Hilbert spaces. In this work, we propose different measurement protocols and assess their efficiency in terms of resources, through theoretical predictions and numerical analysis. We show that it is possible to exploit the quantumness of the reservoir and to obtain ideal performance both for memory and forecasting tasks with two successful measurement protocols. One repeats part of the experiment after each projective measurement while the other employs weak measurements operating online at the trade-off where information can be extracted accurately and without hindering the needed memory, in spite of back-action effects. Our work establishes the conditions for efficient time-series processing paving the way to its implementation in different quantum technologies.https://doi.org/10.1038/s41534-023-00682-z |
| spellingShingle | Pere Mujal Rodrigo Martínez-Peña Gian Luca Giorgi Miguel C. Soriano Roberta Zambrini Time-series quantum reservoir computing with weak and projective measurements npj Quantum Information |
| title | Time-series quantum reservoir computing with weak and projective measurements |
| title_full | Time-series quantum reservoir computing with weak and projective measurements |
| title_fullStr | Time-series quantum reservoir computing with weak and projective measurements |
| title_full_unstemmed | Time-series quantum reservoir computing with weak and projective measurements |
| title_short | Time-series quantum reservoir computing with weak and projective measurements |
| title_sort | time series quantum reservoir computing with weak and projective measurements |
| url | https://doi.org/10.1038/s41534-023-00682-z |
| work_keys_str_mv | AT peremujal timeseriesquantumreservoircomputingwithweakandprojectivemeasurements AT rodrigomartinezpena timeseriesquantumreservoircomputingwithweakandprojectivemeasurements AT gianlucagiorgi timeseriesquantumreservoircomputingwithweakandprojectivemeasurements AT miguelcsoriano timeseriesquantumreservoircomputingwithweakandprojectivemeasurements AT robertazambrini timeseriesquantumreservoircomputingwithweakandprojectivemeasurements |