DeepQPrep: Neural Network Augmented Search for Quantum State Preparation
There is an increasing interest in the area of quantum computing but developing quantum algorithms is difficult. Neural Network augmented search algorithms have proven quite successful for general search problems (like program generation) but current approaches to quantum program generation make ver...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10186883/ |
| _version_ | 1797768412718432256 |
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| author | Patrick Selig Niall Murphy David Redmond Simon Caton |
| author_facet | Patrick Selig Niall Murphy David Redmond Simon Caton |
| author_sort | Patrick Selig |
| collection | DOAJ |
| description | There is an increasing interest in the area of quantum computing but developing quantum algorithms is difficult. Neural Network augmented search algorithms have proven quite successful for general search problems (like program generation) but current approaches to quantum program generation make very restricted use of them. In this paper we present DeepQPrep, a Neural Network based approach to generate quantum circuits for state preparation; a common yet expensive task needed in many applications of quantum computing. We illustrate that Neural Network augmented search algorithms have significant promise for automated quantum program generation; DeepQPrep generated programs were able to solve 99% and 76.9% of 20000 previously unseen state prepartion tasks in a supervised machine learning context within two different application scenarios. The circuits produced by DeepQPrep are also shallower (on average) than their ground truth counterparts. We also compare DeepQPrep to IBM Qiskit’s approach to state preparation and illustrate that even when constrained, DeepQPrep generates significantly shallower circuits despite Qiskit solving more of the state preparation tasks. Based on our results, we argue that neural network augmented search approaches exhibit significant promise for generalised approaches to quantum program induction warranting further study in more complex scenarios. |
| first_indexed | 2024-03-12T20:53:53Z |
| format | Article |
| id | doaj.art-6f569a183867467d8b3456b71e148a48 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T20:53:53Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6f569a183867467d8b3456b71e148a482023-07-31T23:00:17ZengIEEEIEEE Access2169-35362023-01-0111763887640210.1109/ACCESS.2023.329680210186883DeepQPrep: Neural Network Augmented Search for Quantum State PreparationPatrick Selig0https://orcid.org/0009-0004-0912-9801Niall Murphy1David Redmond2Simon Caton3https://orcid.org/0000-0001-9379-3879School of Computer Science, University College Dublin, Dublin 4, IrelandEqual1 Labs, Dublin, IrelandEqual1 Labs, Dublin, IrelandSchool of Computer Science, University College Dublin, Dublin 4, IrelandThere is an increasing interest in the area of quantum computing but developing quantum algorithms is difficult. Neural Network augmented search algorithms have proven quite successful for general search problems (like program generation) but current approaches to quantum program generation make very restricted use of them. In this paper we present DeepQPrep, a Neural Network based approach to generate quantum circuits for state preparation; a common yet expensive task needed in many applications of quantum computing. We illustrate that Neural Network augmented search algorithms have significant promise for automated quantum program generation; DeepQPrep generated programs were able to solve 99% and 76.9% of 20000 previously unseen state prepartion tasks in a supervised machine learning context within two different application scenarios. The circuits produced by DeepQPrep are also shallower (on average) than their ground truth counterparts. We also compare DeepQPrep to IBM Qiskit’s approach to state preparation and illustrate that even when constrained, DeepQPrep generates significantly shallower circuits despite Qiskit solving more of the state preparation tasks. Based on our results, we argue that neural network augmented search approaches exhibit significant promise for generalised approaches to quantum program induction warranting further study in more complex scenarios.https://ieeexplore.ieee.org/document/10186883/Quantum state preparationquantum program synthesisgraph neural networksgate-based circuitssupervised machine learning |
| spellingShingle | Patrick Selig Niall Murphy David Redmond Simon Caton DeepQPrep: Neural Network Augmented Search for Quantum State Preparation IEEE Access Quantum state preparation quantum program synthesis graph neural networks gate-based circuits supervised machine learning |
| title | DeepQPrep: Neural Network Augmented Search for Quantum State Preparation |
| title_full | DeepQPrep: Neural Network Augmented Search for Quantum State Preparation |
| title_fullStr | DeepQPrep: Neural Network Augmented Search for Quantum State Preparation |
| title_full_unstemmed | DeepQPrep: Neural Network Augmented Search for Quantum State Preparation |
| title_short | DeepQPrep: Neural Network Augmented Search for Quantum State Preparation |
| title_sort | deepqprep neural network augmented search for quantum state preparation |
| topic | Quantum state preparation quantum program synthesis graph neural networks gate-based circuits supervised machine learning |
| url | https://ieeexplore.ieee.org/document/10186883/ |
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