Holographic coherent states from random tensor networks

Random tensor networks provide useful models that incorporate various important features of holographic duality. A tensor network is usually defined for a fixed graph geometry specified by the connection of tensors. In this paper, we generalize the random tensor network approach to allow quantum superposition of different spatial geometries. We set up a framework in which all possible bulk spatial geometries, characterized by weighted adjacent matrices of all possible graphs, are mapped to the boundary Hilbert space and form an overcomplete basis of the boundary. We name such an overcomplete basis as holographic coherent states. A generic boundary state can be expanded on this basis, which describes the state as a superposition of different spatial geometries in the bulk. We discuss how to define distinct classical geometries and small fluctuations around them. We show that small fluctuations around classical geometries define "code subspaces" which are mapped to the boundary Hilbert space isometrically with quantum error correction properties. In addition, we also show that the overlap between different geometries is suppressed exponentially as a function of the geometrical difference between the two geometries. The geometrical difference is measured in an area law fashion, which is a manifestation of the holographic nature of the states considered.Comment: 33 pages, 8 figures. An error corrected on page 14. Reference update

Numerical simulations of winds driven by radiation force from the corona above a thin disk

Observations show that winds can be driven from the innermost region (inside a 50 Schwarschild radius) of a thin disk. It is interesting to study the winds launched from the innermost region. A hot corona above the black hole (BH) thin disk is irradiated by the disk. We perform two-dimensional hydrodynamical simulations to study the winds driven by radiation force from the corona in the innermost regions. The hard X-ray spectrum from active galactic nuclei (AGNs) suggests that the corona temperature is about $10^9$ K, so that we mainly analyze the properties of winds (or outflows) from the $10^9$ K corona. The disk luminosity plays an important role in driving the outflows. The more luminous the disk, the stronger the outflows. Mass outflow rate ($\dot{M}_{\rm out}$) at a 90 Schwarschild radius depends on disk luminosity, which can be described as $\dot{M}_{\rm out}\propto 10^{3.3 \Gamma}$ ($\Gamma$ is the ratio of the disk luminosity to the Eddington luminosity). In the case of high luminosity (e.g. $\Gamma=0.75$), the supersonic outflows with maximum speed $1.0 \times 10^4$ Km s$^{-1}$ are launched at $\sim17^{o}$ --$30^{o}$ and $\sim50^{o}$ --$80^{o}$ away from the pole axis. The Bernoulli parameter keeps increasing with the outward propagation of outflows. The radiation force keeps accelerating the outflows when outflows move outward. Therefore, we can expect the outflows to escape from the BH gravity and go to the galactic scale. The interaction between outflows and interstellar medium may be an important AGN feedback process.Comment: 9 pages, 12 figures, accepted for publication in Ap

Self-Learning Determinantal Quantum Monte Carlo Method

Self-learning Monte Carlo method [arXiv:1610.03137, 1611.09364] is a powerful general-purpose numerical method recently introduced to simulate many-body systems. In this work, we implement this method in the framework of determinantal quantum Monte Carlo simulation of interacting fermion systems. Guided by a self-learned bosonic effective action, our method uses a cumulative update [arXiv:1611.09364] algorithm to sample auxiliary field configurations quickly and efficiently. We demonstrate that self-learning determinantal Monte Carlo method can reduce the auto-correlation time to as short as one near a critical point, leading to $\mathcal{O}(N)$-fold speedup. This enables to simulate interacting fermion system on a $100\times 100$ lattice for the first time, and obtain critical exponents with high accuracy.Comment: 5 pages, 4 figure

Itinerant quantum critical point with frustration and non-Fermi-liquid

Employing the self-learning quantum Monte Carlo algorithm, we investigate the frustrated transverse-field triangle-lattice Ising model coupled to a Fermi surface. Without fermions, the spin degrees of freedom undergoes a second-order quantum phase transition between paramagnetic and clock-ordered phases. This quantum critical point (QCP) has an emergent U(1) symmetry and thus belongs to the (2+1)D XY universality class. In the presence of fermions, spin fluctuations introduce effective interactions among fermions and distort the bare Fermi surface towards an interacting one with hot spots and Fermi pockets. Near the QCP, non-Fermi-liquid behavior are observed at the hot spots, and the QCP is rendered into a different universality with Hertz-Millis type exponents. The detailed properties of this QCP and possibly related experimental systems are also discussed.Comment: 9 pages, 8 figure