Quantum state transmission in a cavity array via two-photon exchange

The dynamical behavior of a coupled cavity array is investigated when each cavity contains a three-level atom. For the uniform and staggered intercavity hopping, the whole system Hamiltonian can be analytically diagonalized in the subspace of single-atom excitation. The quantum state transfer along the cavities is analyzed in detail for distinct regimes of parameters, and some interesting phenomena including binary transmission, selective localization of the excitation population are revealed. We demonstrate that the uniform coupling is more suitable for the quantum state transfer. It is shown that the initial state of polariton located in the first cavity is crucial to the transmission fidelity, and the local entanglement depresses the state transfer probability. Exploiting the metastable state, the distance of the quantum state transfer can be much longer than that of Jaynes-Cummings-Hubbard model. A higher transmission probability and longer distance can be achieved by employing a class of initial encodings and final decodings.Comment: 8 pages, 7 figures. to appear in Phys. Rev.

Improved ACD-based financial trade durations prediction leveraging LSTM networks and Attention Mechanism

The liquidity risk factor of security market plays an important role in the formulation of trading strategies. A more liquid stock market means that the securities can be bought or sold more easily. As a sound indicator of market liquidity, the transaction duration is the focus of this study. We concentrate on estimating the probability density function p({\Delta}t_(i+1) |G_i) where {\Delta}t_(i+1) represents the duration of the (i+1)-th transaction, G_i represents the historical information at the time when the (i+1)-th transaction occurs. In this paper, we propose a new ultra-high-frequency (UHF) duration modelling framework by utilizing long short-term memory (LSTM) networks to extend the conditional mean equation of classic autoregressive conditional duration (ACD) model while retaining the probabilistic inference ability. And then the attention mechanism is leveraged to unveil the internal mechanism of the constructed model. In order to minimize the impact of manual parameter tuning, we adopt fixed hyperparameters during the training process. The experiments applied to a large-scale dataset prove the superiority of the proposed hybrid models. In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer

Fidelity, dynamic structure factor, and susceptibility in critical phenomena

Motivated by the growing importance of fidelity in quantum critical phenomena, we establish a general relation between fidelity and structure factor of the driving term in a Hamiltonian through a newly introduced concept: fidelity susceptibility. Our discovery, as shown by some examples, facilitates the evaluation of fidelity in terms of susceptibility using well developed techniques such as density matrix renormalization group for the ground state, or Monte Carlo simulations for the states in thermal equilibrium.Comment: 4 pages, 2 figures, final version accepted by PR