On the Correction of Anomalous Phase Oscillation in Entanglement Witnesses Using Quantum Neural Networks

Entanglement of a quantum system depends upon the relative phase in complicated ways, which no single measurement can reflect. Because of this, “entanglement witnesses” (measures that estimate entanglement) are necessarily limited in applicability and/or utility. We propose here a solution to the problem using quantum neural networks. A quantum system contains the information of its entanglement; thus, if we are clever, we can extract that information efficiently. As proof of concept, we show how this can be done for the case of pure states of a two-qubit system, using an entanglement indicator corrected for the anomalous phase oscillation. Both the entanglement indicator and the phase correction are calculated by the quantum system itself acting as a neural network.

[1]  Yann LeCun,et al.  A theoretical framework for back-propagation , 1988 .

[2]  Elizabeth C. Behrman,et al.  Dynamic learning of pairwise and three-way entanglement , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[3]  Yann Le Cun,et al.  A Theoretical Framework for Back-Propagation , 1988 .

[4]  Elizabeth C. Behrman,et al.  Simulations of quantum neural networks , 2000, Inf. Sci..

[5]  M. Ježek,et al.  Iterative algorithm for reconstruction of entangled states , 2000, quant-ph/0009093.

[6]  Florian Mintert Concurrence via entanglement witnesses , 2006 .

[7]  Elizabeth C. Behrman,et al.  A quantum neural network computes its own relative phase , 2013, 2013 IEEE Symposium on Swarm Intelligence (SIS).

[8]  R. Feynman,et al.  Quantum Mechanics and Path Integrals , 1965 .

[9]  Elizabeth C. Behrman,et al.  Multiqubit entanglement of a general input state , 2011, Quantum Inf. Comput..

[10]  G. Tóth,et al.  Detecting genuine multipartite entanglement with two local measurements. , 2004, Physical review letters.

[11]  W. Wootters Entanglement of Formation of an Arbitrary State of Two Qubits , 1997, quant-ph/9709029.

[12]  N. Mermin Quantum theory: Concepts and methods , 1997 .

[13]  Prem Kumar,et al.  Quantum algorithm design using dynamic learning , 2008, Quantum Inf. Comput..

[14]  O. Astafiev,et al.  Demonstration of conditional gate operation using superconducting charge qubits , 2003, Nature.

[15]  Thierry Paul,et al.  Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.