Talks

Coherent states conveniently represent the classically meaningful wavelike states of light, as opposed to the corpuscular number states, but superposing coherent states is mind-bogglingly unclassical, including as examples "cat states", "comb states" and "compass states". I present a history of superposing coherent states, especially for oscillators (including electromagnetic field states) and for spin, and extend to entangled coherent states. I then show that superposed coherent states are a viable path for photonic quantum computing and for reaching towards the ultimate limits of sensing. Finally, I discuss our experimental realisations (at University of New South Wales) of superposed coherent states in a spin-7/2 Antimony nucleus in a silicon substrate.

Most physical theories assume that spacetime is four dimensional, a claim strongly supported by high precision accelerator and cosmological measurements. It is also well known that at the Planck scale our quantum theories, and perhaps physics itself, break down. This raises a natural question: what happens to spacetime at the smallest scales, where quantum fluctuations may alter the familiar four dimensional structure? What is the dimension of quantum spacetime? How should we understand topology in this regime, and can matter and spacetime topology be defined independently? My talk will present possible answers to these questions through the lens of Causal Dynamical Triangulations.

I discuss particle physics models for dark matter that can leave a non-trivial imprint in the dark matter density perturbations and serve as targets of precise cosmological measurements. Scenarios include sterile neutrino dark matter enabled by novel neutrino self-interaction, thermal relic via an entropy dilution mechanism, and dark matter mass generated by a first-order phase transition in the early universe. Interplay with other probes will also be discussed when applicable.

Horizon-scale images from the Event Horizon Telescope (EHT) now reveal two stable features of M87⋆: a dark inner shadow and a bright, polarized ring. These two features provide clean and robust standing points for new-physics searches. In the shadow region, any model that adds microwave emission would partially illuminate it, so the observed darkness becomes a background-free intensity test that yields strong bounds, which we showcased with dark matter annihilation. On the ring, the differential linear polarization angle between observing days, predicted and observed to be nearly zero, cancels accretion flow systematics; its stability leads to leading limits on the axion–photon coupling in the ultralight mass range, assuming a superradiant origin. I will also highlight recent progress on controlling observational uncertainties, strengthening which parts of these conclusions can be regarded as robust.

The Hubble tension, recently reaching as high as 7σ, increasingly presents a challenge to the ΛCDM model. A key question is whether this tension stems from our use of the sound horizon as a standard ruler. In this talk, I will present a new, sub-2% measurement of the Hubble constant (H₀) that is independent of the sound horizon scale, using data from the first data release of the Dark Energy Spectroscopic Instrument (DESI). The analysis employs a power spectrum rescaling technique that marginalizes over the sound horizon information, drawing instead on the matter-radiation equality scale as a standard ruler. Combining DESI’s full-shape galaxy clustering with uncalibrated post-reconstruction BAO and the CMB acoustic scale θ*, along with various external sound horizon-free datasets, results in highly robust constraints that are the most precise to date from LSS. Looking ahead, this measurement offers a new window on beyond-ΛCDM physics and the physical origin of the Hubble tension.

Machine-learning-based variational Monte Carlo simulations are a promising approach for targeting quantum many-body ground states, especially in two dimensions and in cases where the ground state is known to have a non-trivial sign structure. While many state-of-the-art variational energies have been reached with these methods for finite-size systems, little work has been done to use these results to extract information about the target state in the thermodynamic limit. In this work, we employ recurrent neural networks (RNNs) as a variational ansätze, and leverage their recurrent nature to simulate the ground states of progressively larger systems through iterative retraining. This transfer learning technique allows us to simulate spin-1/2 systems on very large lattices without beginning optimization from scratch for each system size, thus reducing the demands for computational resources. We first examine the square-lattice Heisenberg antiferromagnet, where it is possible to carefully benchmark our results. We also study the more challenging, sign-problematic triangular-lattice Heisenberg antiferromagnet. In both cases, we show that we are able to systematically improve the accuracy of our simulations by increasing the training time. Furthermore, we use our finite-size results to extract accurate estimates of  ground-state properties in the thermodynamic limit. These works demonstrate that RNN wavefunctions are able to extract accurate information about quantum many-body systems in the thermodynamic limit.