Literature DB >> 18232841

Robust quantum error correction via convex optimization.

Robert L Kosut1, Alireza Shabani, Daniel A Lidar.   

Abstract

We present a semidefinite program optimization approach to quantum error correction that yields codes and recovery procedures that are robust against significant variations in the noise channel. Our approach allows us to optimize the encoding, recovery, or both, and is amenable to approximations that significantly improve computational cost while retaining fidelity. We illustrate our theory numerically for optimized 5-qubit codes, using the standard [5,1,3] code as a benchmark. Our optimized encoding and recovery yields fidelities that are uniformly higher by 1-2 orders of magnitude against random unitary weight-2 errors compared to the [5,1,3] code with standard recovery.

Entities:  

Year:  2008        PMID: 18232841     DOI: 10.1103/PhysRevLett.100.020502

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  1 in total

1.  Generalization in quantum machine learning from few training data.

Authors:  Matthias C Caro; Hsin-Yuan Huang; M Cerezo; Kunal Sharma; Andrew Sornborger; Lukasz Cincio; Patrick J Coles
Journal:  Nat Commun       Date:  2022-08-22       Impact factor: 17.694

  1 in total

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