Gas stripping in galaxy groups - the case of the starburst spiral NGC 2276

Ram pressure stripping of galactic gas is generally assumed to be inefficient in galaxy groups due to the relatively low density of the intragroup medium and the small velocity dispersions of groups. To test this assumption, we obtained Chandra X-ray data of the starbursting spiral NGC 2276 in the NGC 2300 group of galaxies, a candidate for a strong galaxy interaction with hot intragroup gas. The data reveal a shock-like feature along the western edge of the galaxy and a low-surface-brightness tail extending to the east, similar to the morphology seen in other wavebands. Spatially resolved spectroscopy shows that the data are consistent with intragroup gas being pressurized at the leading western edge of NGC 2276 due to the galaxy moving supersonically through the intragroup medium at a velocity ~850 km/s. Detailed modelling of the gravitational potential of NGC 2276 shows that the resulting ram-pressure could significantly affect the morphology of the outer gas disc but is probably insufficient to strip large amounts of cold gas from the disc. We estimate the mass loss rates due to turbulent viscous stripping and starburst outflows being swept back by ram pressure, showing that both mechanisms could plausibly explain the presence of the X-ray tail. Comparison to existing HI measurements shows that most of the gas escaping the galaxy is in a hot phase. With a total mass loss rate of roughly 5 M_Sun/yr, the galaxy could be losing its entire present HI supply within a Gyr. This demonstrates that the removal of galactic gas through interactions with a hot intragroup medium can occur rapidly enough to transform the morphology of galaxies in groups. Implications of this for galaxy evolution in groups and clusters are briefly discussed.Comment: 16 pages, 8 figures, accepted for publication in MNRA

Realizing time crystals in discrete quantum few-body systems

The exotic phenomenon of time translation symmetry breaking under periodic driving - the time crystal - has been shown to occur in many-body systems even in clean setups where disorder is absent. In this work, we propose the realization of time-crystals in few-body systems, both in the context of trapped cold atoms with strong interactions and of a circuit of superconducting qubits. We show how these two models can be treated in a fairly similar way by adopting an effective spin chain description, to which we apply a simple driving protocol. We focus on the response of the magnetization in the presence of imperfect pulses and interactions, and show how the results can be interpreted, in the cold atomic case, in the context of experiments with trapped bosons and fermions. Furthermore, we provide a set of realistic parameters for the implementation of the superconducting circuit.Comment: 6 pages, 4 figure

Coupling of shells in a carbon nanotube quantum dot

We systematically study the coupling of longitudinal modes (shells) in a carbon nanotube quantum dot. Inelastic cotunneling spectroscopy is used to probe the excitation spectrum in parallel, perpendicular and rotating magnetic fields. The data is compared to a theoretical model including coupling between shells, induced by atomically sharp disorder in the nanotube. The calculated excitation spectra show good correspondence with experimental data.Comment: 8 pages, 4 figure

Multiqubit State Learning with Entangling Quantum Generative Adversarial Networks

The increasing success of classical generative adversarial networks (GANs) has inspired several quantum versions of GANs. Fully quantum mechanical applications of such quantum GANs have been limited to one- and two-qubit systems. In this paper, we investigate the entangling quantum GAN (EQ-GAN) for multiqubit learning. We show that the EQ-GAN can learn a circuit more efficiently compared to a swap test. We also consider the EQ-GAN for learning VQE-approximated eigenstates, and find that it generates excellent overlap matrix elements when learning VQE states of small molecules. However, this does not directly translate to a good estimate of the energy due to a lack of phase estimation. Finally, we consider random state learning with the EQ-GAN for up to six qubits, using different two-qubit gates, and show that it is capable of learning completely random quantum states, something which could be useful in quantum state loading.Comment: 6 pages, 4 figures, 1 table + Supporting materia