Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation
This work introduces a self-learning protocol that incorporates measurement and feedback into variational quantum circuits for efficient quantum state preparation. By combining projective measurements with conditional feedback, the protocol learns state preparation strategies that extend beyond unit...
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| Format: | Article |
| Language: | English |
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Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2025-07-01
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| Series: | Quantum |
| Online Access: | https://quantum-journal.org/papers/q-2025-07-11-1792/pdf/ |
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| author | Daniel Alcalde Puente Matteo Rizzi |
| author_facet | Daniel Alcalde Puente Matteo Rizzi |
| author_sort | Daniel Alcalde Puente |
| collection | DOAJ |
| description | This work introduces a self-learning protocol that incorporates measurement and feedback into variational quantum circuits for efficient quantum state preparation. By combining projective measurements with conditional feedback, the protocol learns state preparation strategies that extend beyond unitary-only methods, leveraging measurement-based shortcuts to reduce circuit depth. Using the spin-1 Affleck-Kennedy-Lieb-Tasaki state as a benchmark, the protocol learns high-fidelity state preparation by overcoming a family of measurement induced local minima through adjustments of parameter update frequencies and ancilla regularization. Despite these efforts, optimization remains challenging due to the highly non-convex landscapes inherent to variational circuits. The approach is extended to larger systems using translationally invariant ansätze and recurrent neural networks for feedback, demonstrating scalability. Additionally, the successful preparation of a specific AKLT state with desired edge modes highlights the potential to discover new state preparation protocols where none currently exist. These results indicate that integrating measurement and feedback into variational quantum algorithms provides a promising framework for quantum state preparation. |
| format | Article |
| id | doaj-art-550cfe36fd6a40548d0df5981aff9cae |
| institution | Kabale University |
| issn | 2521-327X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
| record_format | Article |
| series | Quantum |
| spelling | doaj-art-550cfe36fd6a40548d0df5981aff9cae2025-08-20T03:28:59ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2025-07-019179210.22331/q-2025-07-11-179210.22331/q-2025-07-11-1792Learning Feedback Mechanisms for Measurement-Based Variational Quantum State PreparationDaniel Alcalde PuenteMatteo RizziThis work introduces a self-learning protocol that incorporates measurement and feedback into variational quantum circuits for efficient quantum state preparation. By combining projective measurements with conditional feedback, the protocol learns state preparation strategies that extend beyond unitary-only methods, leveraging measurement-based shortcuts to reduce circuit depth. Using the spin-1 Affleck-Kennedy-Lieb-Tasaki state as a benchmark, the protocol learns high-fidelity state preparation by overcoming a family of measurement induced local minima through adjustments of parameter update frequencies and ancilla regularization. Despite these efforts, optimization remains challenging due to the highly non-convex landscapes inherent to variational circuits. The approach is extended to larger systems using translationally invariant ansätze and recurrent neural networks for feedback, demonstrating scalability. Additionally, the successful preparation of a specific AKLT state with desired edge modes highlights the potential to discover new state preparation protocols where none currently exist. These results indicate that integrating measurement and feedback into variational quantum algorithms provides a promising framework for quantum state preparation.https://quantum-journal.org/papers/q-2025-07-11-1792/pdf/ |
| spellingShingle | Daniel Alcalde Puente Matteo Rizzi Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation Quantum |
| title | Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation |
| title_full | Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation |
| title_fullStr | Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation |
| title_full_unstemmed | Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation |
| title_short | Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation |
| title_sort | learning feedback mechanisms for measurement based variational quantum state preparation |
| url | https://quantum-journal.org/papers/q-2025-07-11-1792/pdf/ |
| work_keys_str_mv | AT danielalcaldepuente learningfeedbackmechanismsformeasurementbasedvariationalquantumstatepreparation AT matteorizzi learningfeedbackmechanismsformeasurementbasedvariationalquantumstatepreparation |