Entanglement enhancement for two spins assisted by two phase kicks

Burlak, Gennadiy; Sainz, Isabel; Klimov, A. B.

doi:10.1103/physreva.80.024301

Cited by 7 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to study the entanglement of such states we use a standard technique 21 that allow us finding a specific quantity: quantum concurrence C. The value C = 1 indicates the complete two-qubit entanglement in the system. Our calculations (for details see 21,22 and references therein) show that for considered states the value C = 1, i.e. corresponding qubits pair are completely entangled.…”

Section: Fig 3 (Color On Line)mentioning

confidence: 73%

Evaluation of the spectrum of a quantum system using machine learning based on incomplete information about the wavefunctions

Burlak

2020

Applied Physics Letters

Self Cite

View full text Add to dashboard Cite

We propose an effective approach to rapid estimation of the energy spectrum of quantum systems with the use of machine learning (ML) algorithm. In the ML approach (back propagation), the wavefunction data known from experiments is interpreted as the attributes class (input data), while the spectrum of quantum numbers establishes the label class (output data). To evaluate this approach, we employ two exactly solvable models with the random modulated wavefunction amplitude. The random factor allows modeling the incompleteness of information about the state of quantum system. The trial wave functions fed into the neural network, with the goal of making prediction about the spectrum of quantum numbers. We found that in such configuration, the training process occurs with rapid convergence if the number of analyzed quantum states is not too large. The two qubits entanglement is studied as well. The accuracy of the test prediction (after training) reached 98 percent. Considered ML approach opens up important perspectives to plane the quantum measurements and optimal monitoring of complex quantum objects.Introduction. The applications of intelligent machines in various context of scientific research recently become an area of active investigations 1-14 . One of the important perspective directions of quantum physics is the measurement of wave functions and the energy spectrum of quantum objects. At present, the wavefunction is determined using the tomographic methods 9 -13 , which evaluate the wavefunction that is most compatible with a diverse set of measurements. The indirectness of these methods compounds the problem of direct determining the wavefunction. To overcome this problem, it was shown 1 that the photon wavefunction can be measured directly by sequentially measuring two additional system variables. As result, the components of the wavefunction appear directly on the measuring apparatus. The alternative approach can be used to determine the polarization quantum state 7 . In contrast to various works motivated by physically oriented approaches to artificial intelligence, there are much fewer practical studies estimating the energy spectrum (spectral numbers) for a quantum system based on incomplete information about the wavefunction with the use of the machine learning (ML) approaches. The information on the quantum object structure normally is obtained as the result of measurements of the wavefunction in series of experiments. By the measurements we mean the definition values of wavefunction at a number of spatial points x j , see Ref. 1 Fig.2(a). According to the Heisenberg uncertainty principle, in quantum theory, an exact measurement of position X violates the wavefunction of the particle and forces the subsequent measurement of momentum P to become random. To include such a factor into consideration in the machine learning (ML), we model uncertainty as a random modulation of the measured amplitude of the wavefunction. Thus an incomplete wavefunction can be written as Ψ k (x i |n j ) = γΨ(x i |n j ), whe...

show abstract

Section: Fig 3 (Color On Line)mentioning

confidence: 73%

Evaluation of the spectrum of a quantum system using machine learning based on incomplete information about the wavefunctions

Burlak

2020

Applied Physics Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…worth noting that similar local transformations in the form of instant pulses were also used for another purposes: for atomic squeezing enhancement in the Dicke states [22], for intensification of the entanglement in continuous-variables [23] and in two-spin [24] systems. This work is partially supported by the Grant 106525 of CONACyT (Mexico)…”

Section: Discussionmentioning

confidence: 99%

Transient entanglement in a spin chain stimulated by phase pulses

Sainz¹,

Burlak²,

Klimov³

2010

Preprint

Self Cite

View full text Add to dashboard Cite

Dynamics of the one-dimensional open Ising chain under influence of π-pulses is studied. It is shown that the application of a specific sequence of such instant kicks to selective spins stimulates arising of perfect dynamical pairwise entanglement between ends of the spin chain. Analytic formulas for the concurrence dynamics are derived. It is also shown that the time required to perfectly entangle the ends of the chains grows linearly with the number of spins in the chain. The final entangled state of the ending spins is always the same and does not depend on length the chain.

show abstract

“…Here, |S x A,B = M are maximally polarized spin states in the S x direction and the symmetric Hilbert space dimension is D = (M + 1) 2 . Entanglement in one [42][43][44] and two spin ensembles [45][46][47][48][49][50][51] has been well-studied in the context of quantum metrology and information. The pure state (11) is entangled at all times except for the disentangling times at t = πn/2 for integer n [45,46].…”

Section: Examplesmentioning

confidence: 99%

Covariance matrix entanglement criterion for an arbitrary set of operators

Tripathi¹,

Radhakrishnan²,

Byrnes³

2018

Preprint

View full text Add to dashboard Cite

We generalize entanglement detection with covariance matrices for an arbitrary set of observables. A generalized uncertainty relation is constructed using the covariance and commutation matrices, then a criterion is established by performing a partial transposition on the operators. The method is highly efficient and versatile in the sense that the set of measurement operators can be freely chosen, do not need to be complete, and there is no constraint on the commutation relations. The method is particularly suited for systems with higher dimensionality since the computations do not scale with the dimension of the Hilbert space rather they scale with the number of chosen observables which can always be kept small. We illustrate the approach by examining the entanglement between two spin ensembles, and show that it detects entanglement in a basis independent way.

show abstract

Entanglement enhancement for two spins assisted by two phase kicks

Cited by 7 publications

References 27 publications

Evaluation of the spectrum of a quantum system using machine learning based on incomplete information about the wavefunctions

Evaluation of the spectrum of a quantum system using machine learning based on incomplete information about the wavefunctions

Transient entanglement in a spin chain stimulated by phase pulses

Covariance matrix entanglement criterion for an arbitrary set of operators

Contact Info

Product

Resources

About