Recommender system expedited quantum control optimization
Quantum control optimization algorithms are routinely used to synthesize optimal quantum gates or to realize efficient quantum state transfers. The computational resource required for the optimization is an essential consideration in order to scale toward quantum control of larger registers. Here, w...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
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| Series: | Physics Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266603262200028X |
| _version_ | 1811166117605933056 |
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| author | Priya Batra M. Harshanth Ram T.S. Mahesh |
| author_facet | Priya Batra M. Harshanth Ram T.S. Mahesh |
| author_sort | Priya Batra |
| collection | DOAJ |
| description | Quantum control optimization algorithms are routinely used to synthesize optimal quantum gates or to realize efficient quantum state transfers. The computational resource required for the optimization is an essential consideration in order to scale toward quantum control of larger registers. Here, we propose and demonstrate the use of a machine learning method, specifically the recommender system (RS), to deal with the challenge of enhancing computational efficiency. Given a sparse database of a set of products and their customer ratings, RS is used to efficiently predict unknown ratings. In the quantum control problem, each iteration of a numerical optimization algorithm typically involves evaluating a large number of parameters, such as gradients or fidelities, which can be tabulated as a rating matrix. We establish that RS can rapidly and accurately predict elements of such a sparse rating matrix. Using this approach, we expedite a gradient ascent based quantum control optimization, namely GRAPE, and demonstrate the faster construction of two-qubit CNOT gate in registers with up to 8 qubits. We also describe and implement the enhancement of the computational speed of a hybrid algorithm involving simulated annealing as well as gradient ascent. Moreover, the faster construction of three-qubit Toffoli gates further confirmed the applicability of RS in larger registers. |
| first_indexed | 2024-04-10T15:47:20Z |
| format | Article |
| id | doaj.art-4aaa754be6fd4cc99f458f9f9d777fd8 |
| institution | Directory Open Access Journal |
| issn | 2666-0326 |
| language | English |
| last_indexed | 2024-04-10T15:47:20Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics Open |
| spelling | doaj.art-4aaa754be6fd4cc99f458f9f9d777fd82023-02-12T04:15:34ZengElsevierPhysics Open2666-03262023-02-0114100127Recommender system expedited quantum control optimizationPriya Batra0M. Harshanth Ram1T.S. Mahesh2Corresponding author.; Department of Physics and NMR Research Center, Indian Institute of Science Education and Research, Pune, 411008, IndiaDepartment of Physics and NMR Research Center, Indian Institute of Science Education and Research, Pune, 411008, IndiaDepartment of Physics and NMR Research Center, Indian Institute of Science Education and Research, Pune, 411008, IndiaQuantum control optimization algorithms are routinely used to synthesize optimal quantum gates or to realize efficient quantum state transfers. The computational resource required for the optimization is an essential consideration in order to scale toward quantum control of larger registers. Here, we propose and demonstrate the use of a machine learning method, specifically the recommender system (RS), to deal with the challenge of enhancing computational efficiency. Given a sparse database of a set of products and their customer ratings, RS is used to efficiently predict unknown ratings. In the quantum control problem, each iteration of a numerical optimization algorithm typically involves evaluating a large number of parameters, such as gradients or fidelities, which can be tabulated as a rating matrix. We establish that RS can rapidly and accurately predict elements of such a sparse rating matrix. Using this approach, we expedite a gradient ascent based quantum control optimization, namely GRAPE, and demonstrate the faster construction of two-qubit CNOT gate in registers with up to 8 qubits. We also describe and implement the enhancement of the computational speed of a hybrid algorithm involving simulated annealing as well as gradient ascent. Moreover, the faster construction of three-qubit Toffoli gates further confirmed the applicability of RS in larger registers.http://www.sciencedirect.com/science/article/pii/S266603262200028XQuantum controlQuantum gatesMachine learningRecommender system |
| spellingShingle | Priya Batra M. Harshanth Ram T.S. Mahesh Recommender system expedited quantum control optimization Physics Open Quantum control Quantum gates Machine learning Recommender system |
| title | Recommender system expedited quantum control optimization |
| title_full | Recommender system expedited quantum control optimization |
| title_fullStr | Recommender system expedited quantum control optimization |
| title_full_unstemmed | Recommender system expedited quantum control optimization |
| title_short | Recommender system expedited quantum control optimization |
| title_sort | recommender system expedited quantum control optimization |
| topic | Quantum control Quantum gates Machine learning Recommender system |
| url | http://www.sciencedirect.com/science/article/pii/S266603262200028X |
| work_keys_str_mv | AT priyabatra recommendersystemexpeditedquantumcontroloptimization AT mharshanthram recommendersystemexpeditedquantumcontroloptimization AT tsmahesh recommendersystemexpeditedquantumcontroloptimization |