Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine-Learning Approach
Abstract
Three-qubit quantum gates are key ingredients for quantum error correction and quantum-information processing. We generate quantum-control procedures to design three types of three-qubit gates, namely Toffoli, controlled-not-not, and Fredkin gates. The design procedures are applicable to a system comprising three nearest-neighbor-coupled superconducting artificial atoms. For each three-qubit gate, the numerical simulation of the proposed scheme achieves 99.9% fidelity, which is an accepted threshold fidelity for fault-tolerant quantum computing. We test our procedure in the presence of decoherence-induced noise and show its robustness against random external noise generated by the control electronics. The three-qubit gates are designed via the machine-learning algorithm called subspace-selective self-adaptive differential evolution.
- Publication:
-
Physical Review Applied
- Pub Date:
- November 2016
- DOI:
- 10.1103/PhysRevApplied.6.054005
- arXiv:
- arXiv:1511.08862
- Bibcode:
- 2016PhRvP...6e4005Z
- Keywords:
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- Quantum Physics;
- Computer Science - Machine Learning
- E-Print:
- 18 pages, 13 figures. Accepted for publication in Phys. Rev. Applied