Search

Neutron stars provide a powerful probe of axion dark matter, especially in higher frequency ranges where there remain fewer laboratory constraints. Populations of neutron stars near the Galactic Centre have been proposed as a means to place strong constraints on axion dark matter. One downside of this approach is that there are very few direct observations of neutron stars in this region, introducing uncertainties in the total number of neutron stars in this ``invisible" population at the Galactic Centre, whose size must be inferred through birth rate modelling. We suggest this number could also be reduced due to stellar dynamics carrying stars away from the Galactic Centre via large kick velocities at birth. We attempt to circumvent the uncertainty on the Galactic Centre population size by modelling the axion signal from better understood populations outside the Galactic Centre using {\tt PsrPopPy} which is normalised against pulsar observations. We consider lower-frequency, wider-angle searches for this signal via a range of instruments including MeerKAT and SKA-low but find that the sensitivity is not competitive with existing constraints. Finally, returning to the Galactic Centre, we compare populations to single objects as targets for axion detection. Using the latest modelling of axion-photon conversion in the Galactic Centre magnetar, we conclude that within astrophysical uncertainties, the Galactic Centre population and the magnetar could give comparable sensitivities to axion dark matter, suggesting one should continue to search for both signals in future surveys., Comment: 49 pages, 23 figures, comments are welcome

Quantum nuclear dynamics with wavepacket time-evolution is classically intractable and viewed as a promising avenue for quantum information processing. Here, we use an IonQ 11-qubit trapped-ion quantum computer, Harmony, to study the quantum wavepacket dynamics of a shared-proton within a short-strong hydrogen-bonded system. We also provide the first application of distributed quantum computing for chemical dynamics problems, where the distributed set of quantum processes is constructed using a tensor network formalism. For a range of initial states, we experimentally drive the ion-trap system to emulate the quantum nuclear wavepacket as it evolves along the potential surface generated from electronic structure. Following the experimental creation of the nuclear wavepacket, we extract measurement observables such as its time-dependent spatial projection and its characteristic vibrational frequencies to good agreement with classical results. Vibrational eigenenergies obtained from quantum computational are in agreement with those obtained from classical simulations to within a fraction of a kcal/mol, thus suggesting chemical accuracy. Our approach opens a new paradigm for studying the quantum chemical dynamics and vibrational spectra of molecules and also provides the first demonstration for parallel quantum computation on a distributed set of ion-trap quantum computers.

Time-domain observations are increasingly important in astronomy, and are often the only way to study certain objects. The volume of time-series data is increasing dramatically as new surveys come online - for example, the Vera Rubin Observatory will produce 15 terabytes of data per night, and its Legacy Survey of Space and Time (LSST) is expected to produce five-year lightcurves for $>10^7$ sources, each consisting of 5 photometric bands. Historically, astronomers have worked with Fourier-based techniques such as the Lomb-Scargle periodogram or information-theoretic approaches; however, in recent years Bayesian and data-driven approaches such as Gaussian Process Regression (GPR) have gained traction. However, the computational complexity and steep learning curve of GPR has limited its adoption. `pgmuvi` makes GPR of multi-band timeseries accessible to astronomers by building on cutting-edge open-source machine-learning libraries, and hence `pgmuvi` retains the speed and flexibility of GPR while being easy to use. It provides easy access to GPU acceleration and Bayesian inference of the hyperparameters (e.g. the periods), and is able to scale to large datasets., Comment: 5 pages, submitted to the Journal of Open Source Software. Feedback welcome. Package documentations at https://pgmuvi.readthedocs.io, code at https://github.com/ICSM/pgmuvi

We present a physically appealing and elegant picture for quantum computing using rules constructed for a game of darts. A dartboard is used to represent the state space in quantum mechanics and the act of throwing the dart is shown to have close similarities to the concept of measurement, or collapse of the wavefunction in quantum mechanics. The analogy is constructed in arbitrary dimensional spaces, that is using arbitrary dimensional dartboards, and for for such arbitrary spaces this also provides us a ``visual'' description of uncertainty. Finally, connections of qubits and quantum computing algorithms is also made opening the possibility to construct analogies between quantum algorithms and coupled dart-throw competitions.

Axion dark matter can be converted into photons in the magnetospheres of neutron stars leading to a spectral line centred on the Compton wavelength of the axion. Due to the rotation of the star and the plasma effects in the magnetosphere the signal is predicted to be periodic with significant time variation - a unique smoking gun for axion dark matter. As a proof of principle and to develop the methodology, we carry out the first time domain search of the signal using data from PSR J2144$-$3933 taken as part of the MeerTIME project on MeerKAT telescope. We search for specific signal templates using a matched filter technique and discuss when a time-domain analysis (as is typically the case in pulsar observations) gives greater sensitivity to the axion-coupling to photons in comparison to a simple time-averaged total flux study. We do not find any candidate signals and, hence, impose an upper limit on the axion-to-photon coupling of $g_{a\gamma\gamma}<4\times 10^{-11}\,{\rm GeV}^{-1}$ over the mass range $m_{\rm a}=3.9-4.7\,\mu{\rm eV}$ using this data. This limit relies on PSR J2144$-$3933 not being an extremely aligned rotator, as strongly supported by simple arguments based on the observed pulse profile width. We discuss the possibilities of improving this limit using future observations with MeerKAT and also SKA1-mid and the possibility of using other objects. Finally, to evade modelling uncertainties in axion radio signals, we also carry out a generic ``any periodic-signal search" in the data, finding no evidence for an axion signal., Comment: 21 pages, 14 figures Comments welcome

We provide a detailed exposition of the connections between Boltzmann machines commonly utilized in machine learning problems and the ideas already well known in quantum statistical mechanics through Feynman's description of the same. We find that this equivalence allows the interpretation that the hidden layers in Boltzmann machines and other neural network formalisms are in fact discrete versions of path elements that are present within the Feynman path-integral formalism. Since Feynman paths are the natural and elegant depiction of interference phenomena germane to quantum mechanics, it appears that in machine learning, the goal is to find an appropriate combination of ``paths'', along with accumulated path-weights, through a network that cumulatively capture the correct $x \rightarrow y$ map for a given mathematical problem. As a direct consequence of this analysis, we are able to provide general quantum circuit models that are applicable to both Boltzmann machines and to Feynman path integral descriptions. Connections are also made to inverse quantum scattering problems which allow a robust way to define ``interpretable'' hidden layers.

We describe a general formalism for quantum dynamics and show how this formalism subsumes several quantum algorithms including the Deutsch, Deutsch-Jozsa, Bernstein-Vazirani, Simon, and Shor algorithms as well as the conventional approach to quantum dynamics based on tensor networks. The common framework exposes similarities among quantum algorithms and natural quantum phenomena: we illustrate this connection by showing how the correlated behavior of protons in water wire systems that are common in many biological and materials systems parallels the structure of Shor's algorithm.