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Demonstration of Fidelity Improvement Using Dynamical Decoupling with Superconducting Qubits
Bibek Pokharel1, Namit Anand2, Benjamin Fortman3
1Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089, USA.
Physical Review Letters
|December 15, 2018
Summary
Dynamical decoupling (DD) effectively reduces errors in current cloud quantum computers. This method improves qubit fidelity without complex encoding, showing promise for practical quantum computing applications.
Area of Science:
- Quantum Information Science
- Quantum Computing Hardware
- Quantum Error Mitigation
Background:
- Quantum computers are susceptible to decoherence, limiting their performance.
- Dynamical decoupling (DD) is a simple strategy to reduce decoherence without encoding overhead.
- Previous studies have explored quantum error correction, often requiring postselection.
Purpose of the Study:
- To assess the suitability of dynamical decoupling (DD) on current cloud-based quantum computers.
- To demonstrate fidelity improvements for individual and entangled qubits using DD.
- To identify dominant error sources and evaluate DD's mitigation capabilities.
Main Methods:
- Implementation of DD sequences on superconducting transmon qubits using IBM and Rigetti quantum platforms.
- Comparison of DD-protected qubit evolution against unprotected free evolution.
- Analysis of error types, including dephasing and spontaneous emission.
Main Results:
- Substantial fidelity gains were achieved for individual superconducting transmon qubits using DD.
- DD provided a lesser degree of protection for entangled two-qubit states.
- Dephasing and spontaneous emission were identified as dominant error sources, both mitigated by DD sequences.
Conclusions:
- Dynamical decoupling is a viable and effective method for error mitigation in current noisy, small-scale quantum computers.
- DD offers unconditional fidelity improvements against natural decoherence, without requiring postselection.
- The findings support the practical application of DD for enhancing the performance of cloud quantum computing platforms.
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