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Bhati and Arvind (2022) recently argued that in a specially designed experiment the timing of photon detection events demonstrates photon presence at a location at which they are not present according to the weak value approach. The alleged contradiction is resolved by a subtle interference effect resulting in anomalous sensitivity of the signal imprinted on the postselected photons for the interaction at this location, similarly to the case of a nested Mach-Zehnder interferometer with a Dove prism (Alonso and Jordan (2015)). We perform an in-depth analysis of the characterization of the presence of a pre- and postselected particle at a particular location based on information imprinted on the particle itself. The theoretical results are tested by a computer simulation of the proposed experiment 

Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential for quantum advantage. To address this, we develop a general-purpose, gradient-free, and scalable quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks, including binary and multi-class classification, as well as timeseries prediction. Effective and improving learning is observed with increasing system sizes of up to 108 qubits, demonstrating the largest quantum machine learning experiment to date. We further observe comparative quantum kernel advantage in learning tasks by constructing synthetic datasets based on the geometric differences between generated quantum and classical data kernels. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning. We expect these results to stimulate further extensions to different quantum hardware and machine learning paradigms, including early fault-tolerant hardware and generative machine learning tasks