Pulse Width Modulation for Speeding Up Quantum Optimal Control Design

This paper focuses on accelerating quantum optimal control design for complex quantum systems. Based on our previous work [{arXiv:1607.04054}], we combine Pulse Width Modulation (PWM) and gradient descent algorithm into solving quantum optimal control problems, which shows distinct improvement of computational efficiency in various cases. To further apply this algorithm to potential experiments, we also propose the smooth realization of the optimized control solution, e.g. using Gaussian pulse train to replace rectangular pulses. Based on the experimental data of the D-Norleucine molecule, we numerically find optimal control functions in $3$-qubit and $6$-qubit systems, and demonstrate its efficiency advantage compared with basic GRAPE algorithm

On compression rate of quantum autoencoders: Control design, numerical and experimental realization

Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information. In this paper, we establish an upper bound of the compression rate for a given quantum autoencoder and present a learning control approach for training the autoencoder to achieve the maximal compression rate. The upper bound of the compression rate is theoretically proven using eigen-decomposition and matrix differentiation, which is determined by the eigenvalues of the density matrix representation of the input states. Numerical results on 2-qubit and 3-qubit systems are presented to demonstrate how to train the quantum autoencoder to achieve the theoretically maximal compression, and the training performance using different machine learning algorithms is compared. Experimental results of a quantum autoencoder using quantum optical systems are illustrated for compressing two 2-qubit states into two 1-qubit states

The androgen receptor and signal-transduction pathways in hormone-refractory prostate cancer. Part 2: androgen-receptor cofactors and bypass pathways

Prostate cancer is the second leading cause of cancer related deaths in men from the western world. Treatment of prostate cancer has relied on androgen deprivation therapy for the past 50 years. Response rates are initially high (70-80%), however almost all patients develop androgen escape and subsequently die within 1-2 years. Unlike breast cancer, alternative approaches (chemotherapy and radiotherapy) do not increase survival time. The high rate of prostate cancer mortality is therefore strongly linked to both development of androgen escape and the lack of alternate therapies. AR mutations and amplifications can not explain all cases of androgen escape and post-translational modification of the AR has become an alternative theory. However recently it has been suggested that AR co-activators e.g. SRC-1 or pathways the bypass the AR (Ras/MAP kinase or PI3K/Akt) may stimulated prostate cancer progression independent of the AR. This review will focus on how AR coactivators may act to increase AR transactivation during sub-optimal DHT concentrations and also how signal transduction pathways may promote androgen escape via activation of transcription factors, e.g. AP-1, c-Myc and Myb, that induce cell proliferation or inhibit apoptosis

Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment

Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. Most existing AU detection works often treat face alignment as a preprocessing and handle the two tasks independently. In this paper, we propose a novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before. In particular, multi-scale shared features are learned firstly, and high-level features of face alignment are fed into AU detection. Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively. Finally, the assembled local features are integrated with face alignment features and global features for AU detection. Experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for AU detection.Comment: This paper has been accepted by ECCV 201

Quantum Optimal Control without Arbitrary Waveform Generators

Simple, precise, and robust control is demanded for operating a large quantum information processor. However, existing routes to high-fidelity quantum control rely heavily on arbitrary waveform generators that are difficult to scale up. Here, we show that arbitrary control of a quantum system can be achieved by simply turning on and off the control fields in a proper sequence. The switching instances can be designed by conventional quantum optimal control algorithms, while the required computational resources for matrix exponential can be substantially reduced. We demonstrate the flexibility and robustness of the resulting control protocol, and apply it to superconducting quantum circuits for illustration. We expect this proposal to be readily achievable with current semiconductor and superconductor technologies, which offers a significant step towards scalable quantum computing

Groundwater and climate change: threats and opportunities

The important role of groundwater in adaptation to climate change is explored, and the competing threats and opportunities that climate change pose to groundwater systems are evaluated. This has been achieved through a review of current thinking on the complex interactions between human activities, climate and the hydrological cycle affecting groundwater quantity and quality, across different regions and time scales

IFN-gamma is associated with risk of Schistosoma japonicum infection in China.

Before the start of the schistosomiasis transmission season, 129 villagers resident on a Schistosoma japonicum-endemic island in Poyang Lake, Jiangxi Province, 64 of whom were stool-positive for S. japonicum eggs by the Kato method and 65 negative, were treated with praziquantel. Forty-five days later the 93 subjects who presented for follow-up were all stool-negative. Blood samples were collected from all 93 individuals. S. japonicum soluble worm antigen (SWAP) and soluble egg antigen (SEA) stimulated IL-4, IL-5 and IFN-gamma production in whole-blood cultures were measured by ELISA. All the subjects were interviewed nine times during the subsequent transmission season to estimate the intensity of their contact with potentially infective snail habitats, and the subjects were all re-screened for S. japonicum by the Kato method at the end of the transmission season. Fourteen subjects were found to be infected at that time. There was some indication that the risk of infection might be associated with gender (with females being at higher risk) and with the intensity of water contact, and there was evidence that levels of SEA-induced IFN-gamma production were associated with reduced risk of infection

Surface modification effects on nanocellulose - molecular dynamics simulations using umbrella sampling and computational alchemy

Topochemical modification of nanocellulose particles, in particular acetylation, is commonly used to reduce hygroscopicity and improve their dispersibility in non-polar polymers. Despite enormous experimental efforts on cellulose surface modification, there is currently no comprehensive model which considers both (a) the specific interactions between nanocellulose particles and the surrounding liquid or polymer matrix, and (b) the interactions between the particles themselves. The second mechanism is therefore frequently ignored. The present approach is based on atomistic molecular dynamics (MD) simulations, where computational alchemy is used to calculate the changes in interactions between nanocellulose and the surrounding medium (liquid or polymer) upon modification. This is combined with another method, based on potential of mean force, to calculate interactions between particles. Results show that both contributions are of equal importance for nanoparticle surface acetylation effects. The proposed method is not restricted to either cellulose or acetylation, and has the prospect to find application in a broad context of nanomaterials design