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In pathological research, education, and clinical practice, the decision-making process based on pathological images is critically important. This significance extends to digital pathology image analysis: its adequacy is demonstrated by the extensive information contained within tissue structures, which is essential for accurate cancer classification and grading. Additionally, its necessity is highlighted by the inherent requirement for interpretability in the conclusions generated by algorithms. For humans, determining tumor type and grade typically involves multi-scale analysis, which presents a significant challenge for AI algorithms. Traditional patch-based methods are inadequate for modeling such complex structures, as they fail to capture the intricate, multi-scale information inherent in whole slide images. Consequently, there is a pressing need for advanced AI techniques capable of efficiently and accurately replicating this complex analytical process. To address this issue, we introduce HistoGym, an open-source reinforcement learning environment for histopathological image analysis. Following OpenAI Gym APIs, HistoGym aims to foster whole slide image diagnosis by mimicking the real-life processes of doctors. Leveraging the pyramid feature of WSIs and the OpenSlide API, HistoGym provides a unified framework for various clinical tasks, including tumor detection and classification. We detail the observation, action, and reward specifications tailored for the histopathological image analysis domain and provide an open-source Python-based interface for both clinicians and researchers. To accommodate different clinical demands, we offer various scenarios for different organs and cancers, including both WSI-based and selected region-based scenarios, showcasing several noteworthy results.

The quantum Hall effect, fundamental in modern condensed matter physics, continuously inspires new theories and predicts emergent phases of matter. Here we experimentally demonstrate three types of Chern insulators with synthetic dimensions on a programable 30-qubit-ladder superconducting processor. We directly measure the band structures of the 2D Chern insulator along synthetic dimensions with various configurations of Aubry-Andr\'e-Harper chains and observe dynamical localisation of edge excitations. With these two signatures of topology, our experiments implement the bulk-edge correspondence in the synthetic 2D Chern insulator. Moreover, we simulate two different bilayer Chern insulators on the ladder-type superconducting processor. With the same and opposite periodically modulated on-site potentials for two coupled chains, we simulate topologically nontrivial edge states with zero Hall conductivity and a Chern insulator with higher Chern numbers, respectively. Our work shows the potential of using superconducting qubits for investigating different intriguing topological phases of quantum matter., Comment: main text: 18 pages, 9 figures; supplementary information: 25 pages, 26 figures

Simulating response properties of molecules is crucial for interpreting experimental spectroscopies and accelerating materials design. However, it remains a long-standing computational challenge for electronic structure methods on classical computers. While quantum computers hold the promise to solve this problem more efficiently in the long run, existing quantum algorithms requiring deep quantum circuits are infeasible for near-term noisy quantum processors. Here, we introduce a pragmatic variational quantum response (VQR) algorithm for response properties, which circumvents the need for deep quantum circuits. Using this algorithm, we report the first simulation of linear response properties of molecules including dynamic polarizabilities and absorption spectra on a superconducting quantum processor. Our results indicate that a large class of important dynamical properties such as Green's functions are within the reach of near-term quantum hardware using this algorithm in combination with suitable error mitigation techniques., Comment: 22 pages, 4 figures

Generative adversarial networks are an emerging technique with wide applications in machine learning, which have achieved dramatic success in a number of challenging tasks including image and video generation. When equipped with quantum processors, their quantum counterparts--called quantum generative adversarial networks (QGANs)--may even exhibit exponential advantages in certain machine learning applications. Here, we report an experimental implementation of a QGAN using a programmable superconducting processor, in which both the generator and the discriminator are parameterized via layers of single- and multi-qubit quantum gates. The programmed QGAN runs automatically several rounds of adversarial learning with quantum gradients to achieve a Nash equilibrium point, where the generator can replicate data samples that mimic the ones from the training set. Our implementation is promising to scale up to noisy intermediate-scale quantum devices, thus paving the way for experimental explorations of quantum advantages in practical applications with near-term quantum technologies.

It has been a great challenge to achieve the direct light manipulation of matter on a bulk scale. In this work, the direct light propulsion of matter was observed on a macroscopic scale for the first time using a bulk graphene based material. The unique structure and properties of graphene and the morphology of the bulk graphene material make it capable of not only absorbing light at various wavelengths but also emitting energetic electrons efficiently enough to drive the bulk material following Newtonian mechanics. Thus, the unique photonic and electronic properties of individual graphene sheets are manifested in the response of the bulk state. These results offer an exciting opportunity to bring about bulk scale light manipulation with the potential to realize long-sought proposals in areas such as the solar sail and space transportation driven directly by sunlight.

Femtosecond time-resolved spectroscopy using 400 nm-pump and 800 nm-probe in CVD-grown multilayer graphene provides strong evidence for isotropic distribution of photoexcited carrier after initial relaxation. Indicative of such isotropic distribution is a pump polarization independence of differential reflectivity (\DeltaR/R) and transmittance (\DeltaT/T) from pump-probe measurements. Combined with results using 800 nm-pump in [arXiv. 1301.1743v3 (2013)], these pump polarization dependences of time-resolved spectroscopy corroborates the evolution of photo-excited carrier distribution from anisotropic to isotropic with carrier relaxation. And, the absorbance of graphene is identical for in-plane and out-of-plane optical fields. No matter the carrier distribution in momentum space, the influence of carrier on in-plane and out-of-plane optical fields from state filling effect is identical. The sign reversing of ps dynamics signal in graphene/graphite should not directly relate to carrier., Comment: 18 pages, 19 figures

Graphene is an emerging class of two-dimensional (2D) material with unique electrical properties and a wide range of potential practical applications. In addition, graphene hybrid structures combined with other 2D materials, metal microstructures, silicon photonic crystal cavities, and waveguides have more extensive applications in van der Waals heterostructures, hybrid graphene plasmonics, hybrid optoelectronic devices, and optical modulators. Based on well-developed transfer methods, graphene grown by chemical vapor deposition (CVD) is currently used in most of the graphene hybrid applications. Although mechanical exfoliation of highly oriented pyrolytic graphite provides the highest-quality graphene, the transfer of the desired microcleaving graphene (MG) to the structure at a specific position is a critical challenge, that limits the combination of MG with other structures. Herein, we report a new technique for the selective transfer of MG patterns and devices onto chosen targets using a bilayer-polymer structure and femtosecond laser microfabrication. This selective transfer technique, which exactly transfers the patterned graphene onto a chosen target, leaving the other flakes on the original substrate, provides an efficient route for the fabrication of MG-based microdevices. This method will facilitate the preparation of van der Waals heterostructures and enable the optimization of the performance of graphene hybrid devices., Comment: 22 pages, 5 figures,Letter

Polarization characteristic of ultrafast carrier dynamics in multi-layer CVD-grown graphene is probed with tilted beams (with respected to the graphene plane). The graphene ultrafast carrier dynamics measurement greatly depends on both polarization (i.e., orientation of linear polarization) and wave vector of probe beam. The differential reflectivity \Delta R=R signal of picosecond dynamics could be continuously altered from positive to negative by changing the probe polarization from P to S when the dynamics is probed by a total internal reflected beam. The polarization dependent \Delta R=R signal around 0 delay time is positive. It could be altered to negative by changing the probe polarization if the probe beam is non-total internal reflected beam. However, no sign reversal was observed for differential transmittance \Delta T=T . These extremely unexpected results indicate the anisotropy of graphene carrier dynamics. Thus the ultrafast carrier dynamics should be further studied with consideration of the anisotropic structure (in- and out-of-graphene plane) of graphene., Comment: 6 pages,4 figures in main text. 6 pages,7 figures in Supplemental Material