Quantum adaptive agents with efficient long-term memories
Central to the success of adaptive systems is their ability to interpret signals from their environment and respond accordingly -- they act as agents interacting with their surroundings. Such agents typically perform better when able to execute increasingly complex strategies. This comes with a cost...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Online Access: | https://hdl.handle.net/10356/165032 |
| _version_ | 1826124391896842240 |
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| author | Elliott, Thomas J. Gu, Mile Garner, Andrew J. P. Thompson, Jayne |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Elliott, Thomas J. Gu, Mile Garner, Andrew J. P. Thompson, Jayne |
| author_sort | Elliott, Thomas J. |
| collection | NTU |
| description | Central to the success of adaptive systems is their ability to interpret signals from their environment and respond accordingly -- they act as agents interacting with their surroundings. Such agents typically perform better when able to execute increasingly complex strategies. This comes with a cost: the more information the agent must recall from its past experiences, the more memory it will need. Here we investigate the power of agents capable of quantum
information processing. We uncover the most general form a quantum agent need adopt to maximise memory compression advantages, and provide a systematic means of encoding their memory states. We show these encodings can exhibit extremely favourable scaling advantages relative to memory-minimal classical agents, particularly when information must be retained about events increasingly far into the past. |
| first_indexed | 2024-10-01T06:19:53Z |
| format | Journal Article |
| id | ntu-10356/165032 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:19:53Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1650322023-03-13T15:34:33Z Quantum adaptive agents with efficient long-term memories Elliott, Thomas J. Gu, Mile Garner, Andrew J. P. Thompson, Jayne School of Physical and Mathematical Sciences Centre for Quantum Technologies, NUS Complexity Institute Nanyang Quantum Hub Science::Physics Long Term Memory Adaptive Agents Central to the success of adaptive systems is their ability to interpret signals from their environment and respond accordingly -- they act as agents interacting with their surroundings. Such agents typically perform better when able to execute increasingly complex strategies. This comes with a cost: the more information the agent must recall from its past experiences, the more memory it will need. Here we investigate the power of agents capable of quantum information processing. We uncover the most general form a quantum agent need adopt to maximise memory compression advantages, and provide a systematic means of encoding their memory states. We show these encodings can exhibit extremely favourable scaling advantages relative to memory-minimal classical agents, particularly when information must be retained about events increasingly far into the past. Ministry of Education (MOE) Nanyang Technological University National Research Foundation (NRF) Published version This work was funded by the Imperial College Borland Fellowship in Mathematics, Grants No. FQXi-RFP-1809 and No. FQXi-RFP-IPW-1903 from the Foundational Questions Institute and Fetzer Franklin Fund (a donor advised fund of Silicon Valley Community Foundation), the Lee Kuan Yew Endowment Fund (Postdoctoral Fellowship), the Ministry of Education Singapore through Tier 1 Grant No. RG190/17, the National Research Foundation (NRF) Singapore under its NRFF Fellow program (Award No. NRF-NRFF2016-02), the NRFANR (Agence Nationale de la Recherche) joint program (NRF2017-NRF-ANR004 VanQuTe), and the Quantum Engineering Program QEP-SF3, and the Nanyang Technological University Start-up Grant. 2023-03-08T06:11:37Z 2023-03-08T06:11:37Z 2022 Journal Article Elliott, T. J., Gu, M., Garner, A. J. P. & Thompson, J. (2022). Quantum adaptive agents with efficient long-term memories. Physical Review X, 12(1), 011007-. https://dx.doi.org/10.1103/PhysRevX.12.011007 2160-3308 https://hdl.handle.net/10356/165032 10.1103/PhysRevX.12.011007 2-s2.0-85122853039 1 12 011007 en RG190/17 NRF- NRFF2016-02 NRF2017-NRF-ANR004 VanQuTe QEP-SF3 NTU-SUG Physical Review X © The Authors. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. application/pdf |
| spellingShingle | Science::Physics Long Term Memory Adaptive Agents Elliott, Thomas J. Gu, Mile Garner, Andrew J. P. Thompson, Jayne Quantum adaptive agents with efficient long-term memories |
| title | Quantum adaptive agents with efficient long-term memories |
| title_full | Quantum adaptive agents with efficient long-term memories |
| title_fullStr | Quantum adaptive agents with efficient long-term memories |
| title_full_unstemmed | Quantum adaptive agents with efficient long-term memories |
| title_short | Quantum adaptive agents with efficient long-term memories |
| title_sort | quantum adaptive agents with efficient long term memories |
| topic | Science::Physics Long Term Memory Adaptive Agents |
| url | https://hdl.handle.net/10356/165032 |
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