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One of the most promising techniques for detecting ultra-high energy neutrinos involves the use of radio antennas to observe the 10-1000 MHz radiation generated by the showers that neutrinos induce in large volumes of ice. The expected neutrino detection rates of one neutrino or less per detector station per 10 years make the characterization of backgrounds a priority. The largest natural background comes from ultra-high energy cosmic rays which are orders of magnitude more abundant than neutrinos. Particularly crucial is the understanding of geometries in which substantial energy of the cosmic-ray-induced air shower is deposited in the ice giving rise to a compact in-ice shower close to the ice surface. We calculated the radio emission of air-shower cores using the novel CORSIKA 8 code and found it to be similar to the predictions for neutrino-induced showers. For the first time, we calculated the detection rates for O(100m) deep antennas yielding 10-100 detections per year and detector station, which makes this a useful calibration source as these downward-going signals can be differentiated from neutrino-induced showers based on the signal arrival direction. However, the presence of reflection layers in the ice confuses the arrival directions, which makes this a potentially important background. We review the existing information on reflecting layers in the South Pole glacier and, for the first time, quantify the corresponding rate of reflected air-shower signals for the proposed IceCube-Gen2 radio array and discuss mitigation strategies. The reflectivity of the layers is the dominant uncertainty resulting in rate predictions of much less than one detection to several detections per year for IceCube-Gen2 if not mitigated.

The multifaceted nature of experience poses a challenge to the study of consciousness. Traditional neuroscientific approaches often concentrate on isolated facets, such as perceptual awareness or the global state of consciousness and construct a theory around the relevant empirical paradigms and findings. Theories of consciousness are, therefore, often difficult to compare; indeed, there might be little overlap in the phenomena such theories aim to explain. Here, we take a different approach: starting with active inference, a first principles framework for modelling behaviour as (approximate) Bayesian inference, and building up to a minimal theory of consciousness, which emerges from the shared features of computational models derived under active inference. We review a body of work applying active inference models to the study of consciousness and argue that there is implicit in all these models a small set of theoretical commitments that point to a minimal (and testable) theory of consciousness.

Prethermal discrete time crystals (PDTCs) are a nonequilibrium state of matter characterized by long-range spatiotemporal order, and exhibiting a subharmonic response stabilized by many-body interactions under periodic driving. The inherent robustness of time crystalline order to perturbations in the drive protocol makes DTCs promising for applications in quantum technologies. We exploit the susceptibility of PDTC order to deviations in its order parameter to devise highly frequency-selective quantum sensors for time-varying (AC) magnetic fields in a system of strongly-driven, dipolar-coupled 13C nuclear spins in diamond. Integrating a time-varying AC field into the PDTC allows us to exponentially increase its lifetime, measuring improvement of up to three orders of magnitude (44,204 cycles), and results in a strong resonant response in the time crystalline order parameter. The linewidth of our sensor is limited by the PDTC lifetime alone, as strong interspin interactions help stabilize DTC order. The sensor operates in the 0.5-50 kHz range - a blind spot for sensors based on atomic vapor or electronic spins - and attains a competitive sensitivity. PDTC sensors are resilient to errors in the drive protocol and sample inhomogeneities, and are agnostic to the macroscopic details of the physical platform: the underlying physical principle applies equally to superconducting qubits, neutral atoms, and trapped ions., Comment: 7+16 pages

Non-stabilizer states are a fundamental resource for universal quantum computation. However,despite broad significance in quantum computing, the emergence of "many-body" non-stabilizerness in interacting quantum systems remains poorly understood due to its analytical intractability. Here we show that Rydberg atom arrays provide a natural reservoir of non-stabilizerness that extends beyond single qubits and arises from quantum correlations engendered by the Rydberg blockade. We demonstrate that this non-stabilizerness can be experimentally accessed in two complementary ways, either by performing quench dynamics or via adiabatic ground state preparation. Using the analytical framework based on matrix product states, we explain the origin of Rydberg nonstabilizerness via a quantum circuit decomposition of the wave function., Comment: 15 pages, 7 figures

Active Inference is a recent framework for modeling planning under uncertainty. Empirical and theoretical work have now begun to evaluate the strengths and weaknesses of this approach and how it might be improved. A recent extension - the sophisticated inference (SI) algorithm - improves performance on multi-step planning problems through recursive decision tree search. However, little work to date has been done to compare SI to other established planning algorithms. SI was also developed with a focus on inference as opposed to learning. The present paper has two aims. First, we compare performance of SI to Bayesian reinforcement learning (RL) schemes designed to solve similar problems. Second, we present an extension of SI - sophisticated learning (SL) - that more fully incorporates active learning during planning. SL maintains beliefs about how model parameters would change under the future observations expected under each policy. This allows a form of counterfactual retrospective inference in which the agent considers what could be learned from current or past observations given different future observations. To accomplish these aims, we make use of a novel, biologically inspired environment designed to highlight the problem structure for which SL offers a unique solution. Here, an agent must continually search for available (but changing) resources in the presence of competing affordances for information gain. Our simulations show that SL outperforms all other algorithms in this context - most notably, Bayes-adaptive RL and upper confidence bound algorithms, which aim to solve multi-step planning problems using similar principles (i.e., directed exploration and counterfactual reasoning). These results provide added support for the utility of Active Inference in solving this class of biologically-relevant problems and offer added tools for testing hypotheses about human cognition., Comment: 31 pages, 5 figures

Recent quantum simulation by Google [Nature 612, 240 (2022)] has demonstrated the formation of bound states of interacting photons in a quantum-circuit version of the XXZ spin chain. While such bound states are protected by integrability in a one-dimensional chain, the experiment found the bound states to be unexpectedly robust when integrability was broken by decorating the circuit with additional qubits, at least for small numbers of qubits ($\leq 24$) within the experimental capability. Here we scrutinize this result by state-of-the-art classical simulations, which greatly exceed the experimental system sizes and provide a benchmark for future studies in larger circuits. We find that the bound states consisting of a small and finite number of photons are indeed robust in the non-integrable regime, even after scaling to the infinite time and infinite system size limit. Moreover, we show that such systems possess unusual spectral properties, with level statistics that deviates from the random matrix theory expectation. On the other hand, for low but finite density of photons, we find a much faster onset of thermalization and significantly weaker signatures of bound states, suggesting that anomalous dynamics may only be a property of dilute systems with zero density of photons in the thermodynamic limit. The robustness of the bound states is also influenced by the number of decoration qubits and, to a lesser degree, by the regularity of their spatial arrangement., Comment: 19 pages, 15 figures