Reinforcement-learning generation of four-qubit entangled states
We have devised an artificial intelligence algorithm with machine reinforcement learning (Q-learning) to construct remarkable entangled states with four qubits. This way, the algorithm is able to generate representative states for some of the 49 true SLOCC classes of the four-qubit entanglement stat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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American Physical Society
2022-10-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.4.043056 |
| _version_ | 1797210630317408256 |
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| author | Sara Giordano Miguel A. Martin-Delgado |
| author_facet | Sara Giordano Miguel A. Martin-Delgado |
| author_sort | Sara Giordano |
| collection | DOAJ |
| description | We have devised an artificial intelligence algorithm with machine reinforcement learning (Q-learning) to construct remarkable entangled states with four qubits. This way, the algorithm is able to generate representative states for some of the 49 true SLOCC classes of the four-qubit entanglement states. In particular, it is possible to reach at least one true SLOCC class for each of the nine entanglement families. The quantum circuits synthesized by the algorithm may be useful for the experimental realization of these important classes of entangled states and to draw conclusions about the intrinsic properties of our universe. We introduce a graphical tool called the state-link graph (SLG) to represent the construction of the quality matrix (Q-matrix) used by the algorithm to build a given objective state belonging to the corresponding entanglement class. This allows us to discover the necessary connections between specific entanglement features and the role of certain quantum gates, which the algorithm needs to include in the quantum gate set of actions. The quantum circuits found are optimal by construction with respect to the quantum gate-set chosen. These SLGs make the algorithm simple, intuitive, and a useful resource for the automated construction of entangled states with a low number of qubits. |
| first_indexed | 2024-04-24T10:13:39Z |
| format | Article |
| id | doaj.art-290dae62e3e6482aac4fac61019c0c9e |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:13:39Z |
| publishDate | 2022-10-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-290dae62e3e6482aac4fac61019c0c9e2024-04-12T17:25:35ZengAmerican Physical SocietyPhysical Review Research2643-15642022-10-014404305610.1103/PhysRevResearch.4.043056Reinforcement-learning generation of four-qubit entangled statesSara GiordanoMiguel A. Martin-DelgadoWe have devised an artificial intelligence algorithm with machine reinforcement learning (Q-learning) to construct remarkable entangled states with four qubits. This way, the algorithm is able to generate representative states for some of the 49 true SLOCC classes of the four-qubit entanglement states. In particular, it is possible to reach at least one true SLOCC class for each of the nine entanglement families. The quantum circuits synthesized by the algorithm may be useful for the experimental realization of these important classes of entangled states and to draw conclusions about the intrinsic properties of our universe. We introduce a graphical tool called the state-link graph (SLG) to represent the construction of the quality matrix (Q-matrix) used by the algorithm to build a given objective state belonging to the corresponding entanglement class. This allows us to discover the necessary connections between specific entanglement features and the role of certain quantum gates, which the algorithm needs to include in the quantum gate set of actions. The quantum circuits found are optimal by construction with respect to the quantum gate-set chosen. These SLGs make the algorithm simple, intuitive, and a useful resource for the automated construction of entangled states with a low number of qubits.http://doi.org/10.1103/PhysRevResearch.4.043056 |
| spellingShingle | Sara Giordano Miguel A. Martin-Delgado Reinforcement-learning generation of four-qubit entangled states Physical Review Research |
| title | Reinforcement-learning generation of four-qubit entangled states |
| title_full | Reinforcement-learning generation of four-qubit entangled states |
| title_fullStr | Reinforcement-learning generation of four-qubit entangled states |
| title_full_unstemmed | Reinforcement-learning generation of four-qubit entangled states |
| title_short | Reinforcement-learning generation of four-qubit entangled states |
| title_sort | reinforcement learning generation of four qubit entangled states |
| url | http://doi.org/10.1103/PhysRevResearch.4.043056 |
| work_keys_str_mv | AT saragiordano reinforcementlearninggenerationoffourqubitentangledstates AT miguelamartindelgado reinforcementlearninggenerationoffourqubitentangledstates |