Dr Aonan Zhang

Abstract:

Weak values, emerging from weak measurements in pre- and postselection, exhibit complex-valued properties, enabling the direct characterization of quantum systems. However, conventional weak values do not account for system evolution, restricting their capability to capture quantum dynamical information. To overcome this limitation, we introduce the process weak value (PWV), which incorporates quantum evolution between observables during pre- and postselection. By establishing a connection between PWVs and the matrix elements of unitary operators, we develop a theoretical framework for the direct characterization of quantum processes. Our experimental validation encompasses single-photon and two-photon unitary processes, as well as non-Hermitian parity-time symmetric quantum processes. Compared to standard quantum process tomography, our method reduces the required bases for state preparation and measurements while circumventing complex reconstruction algorithms, enhancing efficiency and scalability. Our PWV formalism opens avenues for broader foundational investigations of weak measurements and enables efficient applications in characterizing sophisticated quantum processes.

Abstract:

High-precision optical frequency measurement underpins modern science and technology, yet conventional spectroscopic techniques struggle to resolve sublinewidth spectral features. Here we introduce a platform for super-resolved frequency estimation based on a mode-selective atomic Raman quantum memory implemented in warm caesium vapour. By precisely engineering the light–matter interaction, the memory coherently stores the optimal temporal mode with high fidelity and retrieves it on demand, achieving mode crosstalk as low as 0.34%. To estimate the separation between two spectral lines, we experimentally measure the mean squared error of the frequency estimate, reaching a sensitivity of 1/20 of the linewidth and a (34 ± 4)-fold enhancement in precision over direct intensity measurements. This enhanced frequency resolution, combined with on-demand storage, retrieval and mode-conversion capabilities, establishes a pathway towards multifunctional memory-based time–frequency sensors and their integration within quantum networks.

Abstract:

Future optical quantum technologies, such as quantum networks, distributed quantum computing and sensing, demand efficient, broadband quantum memories. However, achieving high efficiency without introducing noise, reducing bandwidth, or limiting scalability remains a challenge. Here, we present an approach to enhance quantum memory protocols by leveraging constructive light–matter interference, leading to an increase in memory efficiency without increasing atomic density or laser intensity. We implement this method in a Raman quantum memory in warm cesium vapor and achieve more than a threefold improvement in total efficiency, reaching (34.3±8.4)%, while retaining GHz-bandwidth operation and low noise levels. Numerical simulations predict that this approach can boost efficiencies in systems limited by atomic density, such as cold atomic ensembles, from 65% to beyond 96%, while in warm atomic vapors, it could reduce the laser intensity needed to reach a given efficiency by over an order-of-magnitude, exceeding 95% total efficiency. Furthermore, our method preserves the single-mode nature of the memory at high efficiencies. This protocol is applicable to various memory architectures, paving the way toward scalable, efficient, low-noise, and high-bandwidth quantum memories.

Abstract:

Transition-edge sensors (TESs) are very effective photon-number-resolving (PNR) detectors that have enabled many photonic quantum technologies. However, their relatively slow thermal recovery time severely limits their operation rate in experimental scenarios compared with leading non-PNR detectors. In this work, we develop an algorithmic approach that enables TESs to detect and accurately classify photon pulses without waiting for a full recovery time between detection events. We propose two machine-learning-based signal processing methods: one supervised learning method and one unsupervised clustering method. By benchmarking against data obtained using coherent states and squeezed states, we show that the methods extend the TES operation rate to 800 kHz, achieving at least a four-fold improvement, whilst maintaining accurate photon-number assignment up to at least five photons. Our algorithms will find utility in applications where high rates of PNR detection are required and in technologies that demand fast active feed-forward of PNR detection outcomes.

Abstract:

Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing. While various strategies using distinct quantum measurements have been proposed for overlap estimation, the lack of experimental benchmarks on estimation precision limits strategy selection in different situations. Here we compare the performance of four practical strategies for overlap estimation, including tomography-tomography, tomography-projection, Schur collective measurement and optical swap test using photonic quantum systems. We encode the quantum states on the polarization and path degrees of freedom of single photons. The corresponding measurements are performed by photon detection on certain modes following single-photon mode transformation or two-photon interference. We further propose an adaptive strategy with optimized precision in full-range overlap estimation. Our results shed new light on extracting the parameter of interest from quantum systems, prompting the design of efficient quantum protocols.