Search

Predicting future physical behavior through the limited theoretical simulation data available is an emerging research paradigm resulted by the integration of artificial intelligence technology and quantum physics. In this work, the charge-transport(CT) behavior was forecasted over a long time by a deep learning model, the long short-term memory (LSTM) network with error threshold training method in the photosynthesis II reaction center (PSII-RC). The theoretical simulation data within 8 fs was fed to the modified LSTM network for training, which brings out a distinct prediction with difference of $10^{-4}$ orders of magnitude over a long time period compared to the collection time for training sets. The results indicate the potential of employing LSTM to reveal the physics governing CT in addition to quantum physical methods. The implications of this work warrant further investigation to fully elucidate the scope and efficacy of LSTM for advancing our understanding of photosynthesis at the molecular scale., Comment: 8 pages, 4 figures

Constructing models to discover physics underlying magnanimous data is a traditional strategy in data mining which has been proved to be powerful and successful. In this work, a multi-optimized recurrent neural network (MRNN) is utilized to predict the dynamics of photosynthetic excitation energy transfer (EET) in a light-harvesting complex. The original data set produced by the master equation were trained to forecast the EET evolution. An agreement between our prediction and the theoretical deduction with an accuracy of over 99.26\% is found, showing the validity of the proposed MRNN. A time-segment polynomial fitting multiplied by a unit step function results in a loss rate of the order of $10^{-5}$, showing a striking consistence with analytical formulations for the photosynthetic EET. The work sets up a precedent for accurate EET prediction from large data set by establishing analytical descriptions for physics hidden behind, through minimizing the processing cost during the evolution of week-coupling EET., Comment: 16 pages, 9 figures

Bell's theorem states that quantum mechanical description on physical quantity cannot be fully explained by local realistic theories, and lays solid basis for various quantum information applications. Hardy's paradox is celebrated to be the simplest form of Bell's theorem concerning its "All versus Nothing" way to test local realism. However, due to experimental imperfections, existing tests of Hardy's paradox require additional assumptions of experimental systems, which constitute potential loopholes for faithfully testing local realistic theories. Here, we experimentally demonstrate Hardy's nonlocality through a photonic entanglement source. By achieving a detection efficiency of $82.2\%$, a quantum state fidelity of $99.10\%$ and applying high speed quantum random number generators for measurement setting switching, the experiment is implemented in a loophole-free manner. During $6$ hours of running, a strong violation of $P_{\text{Hardy}}=4.646\times 10^{-4}$ up to $5$ standard deviations is observed with $4.32\times 10^{9}$ trials. A null hypothesis test shows that the results can be explained by local realistic theories with an upper bound probability of $10^{-16348}$. These testing results present affirmative evidence against local realism, and provide an advancing benchmark for quantum information applications based on Hardy's paradox.

Hardy's paradox provides an all-versus-nothing fashion to directly certify that quantum mechanics cannot be completely described by local realistic theory. However, when considering potential imperfections in experiments, like imperfect entanglement source and low detection efficiency, the original Hardy's paradox may induce a rather small Hardy violation and only be realized by expensive quantum systems. To overcome this problem, we propose a realigned Hardy's paradox. Compared with the original version of Hardy's paradox, the realigned Hardy's paradox can dramatically improve the Hardy violation. Then, we generalize the realigned Hardy's paradox to arbitrary even $n$ dichotomic measurements. For $n=2$ and $n=4$ cases, the realigned Hardy's paradox can achieve Hardy values $P(00|A_1B_1)$ approximate $0.4140$ and $0.7734$ respectively compared with $0.09$ of the original Hardy's paradox. Meanwhile, the structure of the realigned Hardy's paradox is simpler and more robust in the sense that there is only one Hardy condition rather than three conditions. One can anticipate that the realigned Hardy's paradox can tolerate more experimental imperfections and stimulate more fascinating quantum information applications., Comment: 7 pages, 1 figure

Distributed quantum sensing can provide quantum-enhanced sensitivity beyond the shot-noise limit (SNL) for sensing spatially distributed parameters. To date, distributed quantum sensing experiments have been mostly accomplished in laboratory environments without a real space separation for the sensors. In addition, the post-selection is normally assumed to demonstrate the sensitivity advantage over the SNL. Here, we demonstrate distributed quantum sensing in field and show the unconditional violation (without post-selection) of SNL up to 0.916 dB for the field distance of 240 m. The achievement is based on a loophole free Bell test setup with entangled photon pairs at the averaged heralding efficiency of 73.88%. Moreover, to test quantum sensing in real life, we demonstrate the experiment for long distances (with 10-km fiber) together with the sensing of a completely random and unknown parameter. The results represent an important step towards a practical quantum sensing network for widespread applications., Comment: 8 pages, 5 figures

In this paper, we tackle the domain adaptive object detection problem, where the main challenge lies in significant domain gaps between source and target domains. Previous work seeks to plainly align image-level and instance-level shifts to eventually minimize the domain discrepancy. However, they still overlook to match crucial image regions and important instances across domains, which will strongly affect domain shift mitigation. In this work, we propose a simple but effective categorical regularization framework for alleviating this issue. It can be applied as a plug-and-play component on a series of Domain Adaptive Faster R-CNN methods which are prominent for dealing with domain adaptive detection. Specifically, by integrating an image-level multi-label classifier upon the detection backbone, we can obtain the sparse but crucial image regions corresponding to categorical information, thanks to the weakly localization ability of the classification manner. Meanwhile, at the instance level, we leverage the categorical consistency between image-level predictions (by the classifier) and instance-level predictions (by the detection head) as a regularization factor to automatically hunt for the hard aligned instances of target domains. Extensive experiments of various domain shift scenarios show that our method obtains a significant performance gain over original Domain Adaptive Faster R-CNN detectors. Furthermore, qualitative visualization and analyses can demonstrate the ability of our method for attending on the key regions/instances targeting on domain adaptation. Our code is open-source and available at \url{https://github.com/Megvii-Nanjing/CR-DA-DET}., Comment: To appear in CVPR 2020. X.-S. Wei is the corresponding author

The ability to produce random numbers that are unknown to any outside party is crucial for many applications. Device-independent randomness generation does not require trusted devices and therefore provides strong guarantees of the security of the output, but it comes at the price of requiring the violation of a Bell inequality for implementation. A further challenge is to make the bounds in the security proofs tight enough to allow randomness expansion with contemporary technology. Although randomness has been generated in recent experiments, the amount of randomness consumed in doing so has been too high to certify expansion based on existing theory. Here we present an experiment that demonstrates device-independent randomness expansion. By developing a Bell test setup with a single-photon detection efficiency of around $84\%$ and by using a spot-checking protocol, we achieve a net gain of $2.57\times10^8$ certified bits with a soundness error $3.09\times10^{-12}$. The experiment ran for $19.2$ h, which corresponds to an average rate of randomness generation of $13,527$ bits per second. By developing the entropy accumulation theorem, we establish security against quantum adversaries. We anticipate that this work will lead to further improvements that push device-independence towards commercial viability., Comment: v2: Update to match published version. Small error in the $K_{alpha}$ term in Theorem 3 in the published supplementary information corrected here

Randomness expansion where one generates a longer sequence of random numbers from a short one is viable in quantum mechanics but not allowed classically. Device-independent quantum randomness expansion provides a randomness resource of the highest security level. Here, we report the first experimental realization of device-independent quantum randomness expansion secure against quantum side information established through quantum probability estimation. We generate $5.47\times10^8$ quantum-proof random bits while consuming $4.39\times10^8$ bits of entropy, expanding our store of randomness by $1.08\times10^8$ bits at a latency of about $13.1$ h, with a total soundness error $4.6\times10^{-10}$. Device-independent quantum randomness expansion not only enriches our understanding of randomness but also sets a solid base to bring quantum-certifiable random bits into realistic applications., Comment: 24 pages, 8 figures, 5 tables

Finding exponential separation between quantum and classical information tasks is like striking gold in quantum information research. Such an advantage is believed to hold for quantum computing but is proven for quantum communication complexity. Recently, a novel quantum resource called the quantum switch---which creates a coherent superposition of the causal order of events, known as quantum causality---has been harnessed theoretically in a new protocol providing provable exponential separation. We experimentally demonstrate such an advantage by realizing a superposition of communication directions for a two-party distributed computation. Our photonic demonstration employs $d$-dimensional quantum systems, qudits, up to $d=2^{13}$ dimensions and demonstrates a communication complexity advantage, requiring less than $(0.696 \pm 0.006)$ times the communication of any causally ordered protocol. These results elucidate the crucial role of the coherence of communication direction in achieving the exponential separation for the one-way processing task, and open a new path for experimentally exploring the fundamentals and applications of advanced features of indefinite causal structures., Comment: Accepted by Phys. Rev. Lett