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Machine learning, one of today’s most rapidly growing interdisciplinary fields, promises an unprecedented perspective for solving intricate quantum many-body problems. Understanding the physical aspects of the representative artificial neural-network states has recently become highly desirable in the applications of machine-learning techniques to quantum many-body physics. In this paper, we explore the data structures that encode the physical features in the network states by studying the quantum entanglement properties, with a focus on the restricted-Boltzmann-machine (RBM) architecture. We prove that the entanglement entropy of all short-range RBM states satisfies an area law for arbitrary dimensions and bipartition geometry. For long-range RBM states, we show by using an exact construction that such states could exhibit volume-law entanglement, implying a notable capability of RBM in representing quantum states with massive entanglement. Strikingly, the neural-network representation for these states is remarkably efficient, in the sense that the number of nonzero parameters scales only linearly with the system size. We further examine the entanglement properties of generic RBM states by randomly sampling the weight parameters of the RBM. We find that their averaged entanglement entropy obeys volume-law scaling, and the meantime strongly deviates from the Page entropy of the completely random pure states. We show that their entanglement spectrum has no universal part associated with random matrix theory and bears a Poisson-type level statistics. Using reinforcement learning, we demonstrate that RBM is capable of finding the ground state (with power-law entanglement) of a model Hamiltonian with a long-range interaction. In addition, we show, through a concrete example of the one-dimensional symmetry-protected topological cluster states, that the RBM representation may also be used as a tool to analytically compute the entanglement spectrum. Our results uncover the unparalleled power of artificial neural networks in representing quantum many-body states regardless of how much entanglement they possess, which paves a novel way to bridge computer-science-based machine-learning techniques to outstanding quantum condensed-matter physics problems.

Machine learning has achieved dramatic success in a broad spectrum of applications. Its interplay with quantum physics may lead to unprecedented perspectives for both fundamental research and commercial applications, giving rise to an emergent research frontier of quantum machine learning. Along this line, quantum classifiers, which are quantum devices that aim to solve classification problems in machine learning, have attracted tremendous attention recently. In this review, we give a relatively comprehensive overview for the studies of quantum classifiers, with a focus on recent advances. First, we will review a number of quantum classification algorithms, including quantum support vector machines, quantum kernel methods, quantum decision tree classifiers, quantum nearest neighbor algorithms, and quantum annealing based classifiers. Then, we move on to introduce the variational quantum classifiers, which are essentially variational quantum circuits for classifications. We will review different architectures for constructing variational quantum classifiers and introduce the barren plateau problem, where the training of quantum classifiers might be hindered by the exponentially vanishing gradient. In addition, the vulnerability aspect of quantum classifiers in the setting of adversarial learning and the recent experimental progress on different quantum classifiers will also be discussed.

High dietary fructose is a major contributor to insulin resistance and metabolic syndrome, disturbing tissue and organ functions. Fructose is mainly absorbed into systemic circulation by glucose transporter 2 (GLUT2) and GLUT5, and metabolized in liver to produce glucose, lactate, triglyceride (TG), free fatty acid (FFA), uric acid (UA) and methylglyoxal (MG). Its extrahepatic absorption and metabolism also take place. High levels of these metabolites are the direct dangerous factors. During fructose metabolism, ATP depletion occurs and induces oxidative stress and inflammatory response, disturbing functions of local tissues and organs to overproduce inflammatory cytokine, adiponectin, leptin and endotoxin, which act as indirect dangerous factors. Fructose and its metabolites directly and/or indirectly cause oxidative stress, chronic inflammation, endothelial dysfunction, autophagy and increased intestinal permeability, and then further aggravate the metabolic syndrome with tissue and organ dysfunctions. Therefore, this review addresses fructose-induced metabolic syndrome, and the disturbance effects of direct and/or indirect dangerous factors on the functions of liver, adipose, pancreas islet, skeletal muscle, kidney, heart, brain and small intestine. It is important to find the potential correlations between direct and/or indirect risk factors and healthy problems under excess dietary fructose consumption.

Private distributed learning studies the problem of how multiple distributed entities collaboratively train a shared deep network with their private data unrevealed. With the security provided by the protocols of blind quantum computation, the cooperation between quantum physics and machine learning may lead to unparalleled prospect for solving private distributed learning tasks. In this paper, we introduce a quantum protocol for distributed learning that is able to utilize the computational power of the remote quantum servers while keeping the private data safe. For concreteness, we first introduce a protocol for private single-party delegated training of variational quantum classifiers based on blind quantum computing and then extend this protocol to multiparty private distributed learning incorporated with differential privacy. We carry out extensive numerical simulations with different real-life datasets and encoding strategies to benchmark the effectiveness of our protocol. We find that our protocol is robust to experimental imperfections and is secure under the gradient attack after the incorporation of differential privacy. Our results show the potential for handling computationally expensive distributed learning tasks with privacy guarantees, thus providing a valuable guide for exploring quantum advantages from the security perspective in the field of machine learning with real-life applications.

Cancer is the second leading cause of death in the world. Chemotherapy and radiotherapy are the common cancer treatments. However, the development of adverse effects resulting from chemotherapy and radiotherapy hinders the clinical use, and negatively reduces the quality of life in cancer patients. Natural products including crude extracts, bioactive components-enriched fractions and pure compounds prepared from herbs as well as herbal formulas have been proved to prevent and treat cancer. Of significant interest, some natural products can reduce chemotherapy and radiotherapy-induced oral mucositis, gastrointestinal toxicity, hepatotoxicity, nephrotoxicity, hematopoietic system injury, cardiotoxicity, and neurotoxicity. This review focuses in detail on the effectiveness of these natural products, and describes the possible mechanisms of the actions in reducing chemotherapy and radiotherapy-induced side effects. Recent advances in the efficacy of natural dietary supplements to counteract these side effects are highlighted. In addition, we draw particular attention to gut microbiotan in the context of prebiotic potential of natural products for the protection against cancer therapy-induced toxicities. We conclude that some natural products are potential therapeutic perspective for the prevention and treatment of chemotherapy and radiotherapy-induced side effects. Further studies are required to validate the efficacy of natural products in cancer patients, and elucidate potential underlying mechanisms.

Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report an experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. We experimentally perform the forward process of the backpropagation algorithm and classically simulate the backward process. In particular, we show that three-layer deep quantum neural networks can be trained efficiently to learn two-qubit quantum channels with a mean fidelity up to 96.0% and the ground state energy of molecular hydrogen with an accuracy up to 93.3% compared to the theoretical value. In addition, six-layer deep quantum neural networks can be trained in a similar fashion to achieve a mean fidelity up to 94.8% for learning single-qubit quantum channels. Our experimental results indicate that the number of coherent qubits required to maintain does not scale with the depth of the deep quantum neural network, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices. Experimental studies about the trainability and generalization capacities of quantum neural networks are highly in need. Here, the authors implement a previously proposed parametrization and training scheme using a 6-qubit superconducting quantum processor.

Quantum many-body systems away from equilibrium host a rich variety of exotic phenomena that are forbidden by equilibrium thermodynamics. A prominent example is that of discrete time crystals 1 – 8 , in which time-translational symmetry is spontaneously broken in periodically driven systems. Pioneering experiments have observed signatures of time crystalline phases with trapped ions 9 , 10 , solid-state spin systems 11 – 15 , ultracold atoms 16 , 17 and superconducting qubits 18 – 20 . Here we report the observation of a distinct type of non-equilibrium state of matter, Floquet symmetry-protected topological phases, which are implemented through digital quantum simulation with an array of programmable superconducting qubits. We observe robust long-lived temporal correlations and subharmonic temporal response for the edge spins over up to 40 driving cycles using a circuit of depth exceeding 240 and acting on 26 qubits. We demonstrate that the subharmonic response is independent of the initial state, and experimentally map out a phase boundary between the Floquet symmetry-protected topological and thermal phases. Our results establish a versatile digital simulation approach to exploring exotic non-equilibrium phases of matter with current noisy intermediate-scale quantum processors 21 . Signatures of non-equilibrium Floquet SPT phases with a programmable superconducting quantum processor are observed in which the discrete time translational symmetry only breaks at the boundaries and not in the bulk.

Background Western-style diets arouse neuroinflammation and impair emotional and cognitive behavior in humans and animals. Our previous study showed that a high-fructose diet caused the hippocampal neuroinflammatory response and neuronal loss in animals, but the underlying mechanisms remained elusive. Here, alterations in the gut microbiota and intestinal epithelial barrier were investigated as the causes of hippocampal neuroinflammation induced by high-fructose diet. Results A high-fructose diet caused the hippocampal neuroinflammatory response, reactive gliosis, and neuronal loss in C57BL/6N mice. Depletion of the gut microbiota using broad-spectrum antibiotics suppressed the hippocampal neuroinflammatory response in fructose-fed mice, but these animals still exhibited neuronal loss. Gut microbiota compositional alteration, short-chain fatty acids (SCFAs) reduction, intestinal epithelial barrier impairment, NOD-like receptor family pyrin domain-containing 6 (NLRP6) inflammasome dysfunction, high levels of serum endotoxin, and FITC-dextran were observed in fructose-fed mice. Of note, SCFAs, as well as pioglitazone (a selective peroxisome proliferator-activated receptor gamma (PPAR-γ) agonist), shaped the gut microbiota and ameliorated intestinal epithelial barrier impairment and NLRP6 inflammasome dysfunction in fructose-fed mice. Moreover, SCFAs-mediated NLRP6 inflammasome activation was inhibited by histamine (a bacterial metabolite) in ex vivo colonic explants and suppressed in murine CT26 colon carcinoma cells transfected with NLRP6 siRNA . However, pioglitazone and GW9662 (a PPAR-γ antagonist) exerted no impact on SCFAs-mediated NLRP6 inflammasome activation in ex vivo colonic explants, suggesting that SCFAs may stimulate NLRP6 inflammasome independently of PPAR-γ activation. SCFAs and pioglitazone prevented fructose-induced hippocampal neuroinflammatory response and neuronal loss in mice. Additionally, SCFAs activated colonic NLRP6 inflammasome and increased DCX + newborn neurons in the hippocampal DG of control mice. Conclusions Our findings reveal that gut dysbiosis is a critical factor for a high-fructose diet-induced hippocampal neuroinflammation in C57BL/6N mice possibly mediated by impairing intestinal epithelial barrier. Mechanistically, the defective colonic NLRP6 inflammasome is responsible for intestinal epithelial barrier impairment. SCFAs can stimulate NLRP6 inflammasome and ameliorate the impairment of intestinal epithelial barrier, resulting in the protection against a high-fructose diet-induced hippocampal neuroinflammation and neuronal loss. This study addresses a gap in the understanding of neuronal injury associated with Western-style diets. A new intervention strategy for reducing the risk of neurodegenerative diseases through SCFAs supplementation or dietary fiber consumption is emphasized.

Microbially mediated nitrogen cycling in carbon-dominated cold seep environments remains poorly understood. So far anaerobic methanotrophic archaea (ANME-2) and their sulfate-reducing bacterial partners (SEEP-SRB1 clade) have been identified as diazotrophs in deep sea cold seep sediments. However, it is unclear whether other microbial groups can perform nitrogen fixation in such ecosystems. To fill this gap, we analyzed 61 metagenomes, 1428 metagenome-assembled genomes, and six metatranscriptomes derived from 11 globally distributed cold seeps. These sediments contain phylogenetically diverse nitrogenase genes corresponding to an expanded diversity of diazotrophic lineages. Diverse catabolic pathways were predicted to provide ATP for nitrogen fixation, suggesting diazotrophy in cold seeps is not necessarily associated with sulfate-dependent anaerobic oxidation of methane. Nitrogen fixation genes among various diazotrophic groups in cold seeps were inferred to be genetically mobile and subject to purifying selection. Our findings extend the capacity for diazotrophy to five candidate phyla (Altarchaeia, Omnitrophota, FCPU426, Caldatribacteriota and UBA6262), and suggest that cold seep diazotrophs might contribute substantially to the global nitrogen balance. Microbial nitrogen fixation could be important in the deep sea. Here the authors investigate metagenomes and metatranscriptomes of diazotrophs from deep sea cold seep sediments, reveal greater phylogenetic and functional diversity than hitherto known.

Anaerobic methane oxidation is coupled to the reduction of electron acceptors, such as sulfate, and contributes to their biogeochemical cycling in the environment. However, whether arsenate acts as an alternative electron acceptor of anaerobic methane oxidation and how this influences global arsenic transformations remains elusive. Here, we present incubations of arsenate-contaminated wetland soils from seven provinces in China. Using isotopically labelled methane, we find that anaerobic methane oxidation was linked to arsenate reduction at a rate approaching the theoretical arsenic/methane stoichiometric ratio of 4. In microcosm incubations with natural wetland soils, we find that the coupled pathway of anaerobic methane oxidation and arsenate reduction contributed 26 to 49% of total arsenic release from soils, with arsenic in the more soluble and toxic form arsenite. Comparative gene quantification and metagenomic sequencing suggest that the coupled pathway was facilitated by anaerobic methanotrophs, either independently or synergistically with arsenate-reducing bacteria through reverse methanogenesis and respiratory arsenate reduction. Further bioinformatic analyses show that genes coding for reverse methanogenesis and respiratory arsenate reduction are universally co-distributed in nature. This suggests that coupling of anaerobic methane oxidation and arsenate reduction is a potentially global but previously overlooked process, with implications for arsenic mobilization and environmental contamination.The coupling of anaerobic oxidation of methane and arsenate reduction is an important pathway of releasing arsenic from soils, according to incubation experiments of arsenate-contaminated wetland soils.