Search

Laser light possesses perfect coherence, but cannot be attenuated to single photons via linear optics. An elegant route to convert laser light into single photons is based on photon blockade in a cavity with a single atom in the strong coupling regime. However, the single-photon purity achieved by this method remains relatively low. Here we propose an interference-based approach where laser light can be transformed into single photons by destructively interfering with a weak but super-bunched incoherent field emitted from a cavity coupling to a single quantum emitter. We demonstrate this idea by measuring the reflected light of a laser field which drives a double-sided optical microcavity containing a single artificial atom-quantum dot (QD) in the Purcell regime. The reflected light consists of a superposition of the driving field with the cavity output field. We achieve the second-order autocorrelation g2(0)=0.030+-0.002 and the two-photon interference visibility 94.3%+-0.2. By separating the coherent and incoherent fields in the reflected light, we observe that the incoherent field from the cavity exhibits super-bunching with g2(0)=41+-2 while the coherent field remains Poissonian statistics. By controlling the relative amplitude of coherent and incoherent fields, we verify that photon statistics of reflected light is tuneable from perfect anti-bunching to super-bunching in agreement with our predictions. Our results demonstrate photon statistics of light as a quantum interference phenomenon that a single QD can scatter two photons simultaneously at low driving fields in contrast to the common picture that a single two-level quantum emitter can only scatter (or absorb and emit) single photons. This work opens the door to tailoring photon statistics of laser light via cavity or waveguide quantum electrodynamics and interference., Comment: Comments are welcome

While random forests are commonly used for regression problems, existing methods often lack adaptability in complex situations or lose optimality under simple, smooth scenarios. In this study, we introduce the adaptive split balancing forest (ASBF), capable of learning tree representations from data while simultaneously achieving minimax optimality under the Lipschitz class. To exploit higher-order smoothness levels, we further propose a localized version that attains the minimax rate under the H\"older class $\mathcal{H}^{q,\beta}$ for any $q\in\mathbb{N}$ and $\beta\in(0,1]$. Rather than relying on the widely-used random feature selection, we consider a balanced modification to existing approaches. Our results indicate that an over-reliance on auxiliary randomness may compromise the approximation power of tree models, leading to suboptimal results. Conversely, a less random, more balanced approach demonstrates optimality. Additionally, we establish uniform upper bounds and explore the application of random forests in average treatment effect estimation problems. Through simulation studies and real-data applications, we demonstrate the superior empirical performance of the proposed methods over existing random forests.

Resonance fluorescence (RF) of a two-level emitter displays persistently anti-bunching irrespective of the excitation intensity, but inherits the driving laser's linewidth under weak excitation. These properties are commonly explained disjoinedly as the emitter's single photon saturation or passively scattering light, until a recent theory attributes anti-bunching to the laser-like spectrum's interference with the incoherently scattered light. However, the theory implies higher-order scattering processes, and led to an experiment purporting to validate an atom's simultaneous scattering of two photons. If true, it could complicate RF's prospects in quantum information applications. Here, we propose a unified model that treats all RF photons as spontaneous emission, one at a time, and can explain simultaneously both the RF's spectral and correlation properties. We theoretically derive the excitation power dependencies, with the strongest effects measurable at the single-photon incidence level, of the first-order coherence of the whole RF and super-bunching of the spectrally filtered, followed by experimental confirmation on a semiconductor quantum dot micro-pillar device. Furthermore, our model explains peculiar coincidence bunching observed in phase-dependent two-photon interference experiments. Our work provides novel understandings of coherent light-matter interaction and may stimulate new applications., Comment: Equation 4 is updated to cover all two-level emitters, with or without a cavity

Afterpulsing noise in InGaAs/InP single photon avalanche photodiodes (APDs) is caused by carrier trapping and can be suppressed successfully through limiting the avalanche charge via sub-nanosecond gating. Detection of faint avalanches requires an electronic circuit that is able to effectively remove the gate-induced capacitive response while keeping photon signals intact. Here we demonstrate a novel ultra-narrowband interference circuit (UNIC) that can reject the capacitive response by up to 80 dB per stage with little distortion to avalanche signals. Cascading two UNIC's in a readout circuit, we were able to enable high count rate of up to 700 MC/s and low afterpulsing of 0.5 % at a detection efficiency of 25.3 % for 1.25 GHz sinusoidally gated InGaAs/InP APDs. At -30 degree C, we measured 1 % afterpulsing at a detection efficiency of 21.2 %.

Estimating dynamic treatment effects is essential across various disciplines, offering nuanced insights into the time-dependent causal impact of interventions. However, this estimation presents challenges due to the "curse of dimensionality" and time-varying confounding, which can lead to biased estimates. Additionally, correctly specifying the growing number of treatment assignments and outcome models with multiple exposures seems overly complex. Given these challenges, the concept of double robustness, where model misspecification is permitted, is extremely valuable, yet unachieved in practical applications. This paper introduces a new approach by proposing novel, robust estimators for both treatment assignments and outcome models. We present a "sequential model double robust" solution, demonstrating that double robustness over multiple time points can be achieved when each time exposure is doubly robust. This approach improves the robustness and reliability of dynamic treatment effects estimation, addressing a significant gap in this field.

Estimating dynamic treatment effects is a crucial endeavor in causal inference, particularly when confronted with high-dimensional confounders. Doubly robust (DR) approaches have emerged as promising tools for estimating treatment effects due to their flexibility. However, we showcase that the traditional DR approaches that only focus on the DR representation of the expected outcomes may fall short of delivering optimal results. In this paper, we propose a novel DR representation for intermediate conditional outcome models that leads to superior robustness guarantees. The proposed method achieves consistency even with high-dimensional confounders, as long as at least one nuisance function is appropriately parametrized for each exposure time and treatment path. Our results represent a significant step forward as they provide new robustness guarantees. The key to achieving these results is our new DR representation, which offers superior inferential performance while requiring weaker assumptions. Lastly, we confirm our findings in practice through simulations and a real data application.