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Published on: March 30, 2017
Unveiling quantum phase transitions from traps in variational quantum algorithms
Chenfeng Cao1,2, Filippo Maria Gambetta1, Ashley Montanaro1,3
1Phasecraft Ltd, London, United Kingdom.
This study introduces a hybrid quantum-classical algorithm to detect quantum phase transitions. The method uses machine learning to identify critical points, improving efficiency for low-temperature physical systems.
Area of Science:
- Quantum physics
- Condensed matter physics
- Machine learning
Background:
- Characterizing quantum phase transitions (QPTs) is crucial for understanding low-temperature physical systems.
- Identifying ground states and order parameters are key challenges in QPT research.
Purpose of the Study:
- To develop a hybrid quantum-classical algorithm for efficient QPT detection.
- To leverage near-term quantum computers and machine learning for identifying critical points.
Main Methods:
- A hybrid algorithm combining quantum optimization and classical machine learning (LASSO and Transformer models).
- Utilizing a sliding window scan of Hamiltonian parameters to learn order parameters.
- Validation through numerical simulations and experiments on Rigetti's Ankaa 9Q-1 quantum computer.
Main Results:
- Successful identification of conventional and topological phase transitions.
- Demonstrated capability to locate critical points with enhanced efficiency and precision.
- Validation of the protocol on real quantum hardware.
Conclusions:
- The developed hybrid protocol offers a framework for QPT investigation using shallow quantum circuits.
- This approach integrates near-term quantum computing and machine learning for condensed matter research.
- The method shows potential for improved efficiency and precision in studying QPTs.
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