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The connection between classical neural networks and Gaussian processes is a fundamental result in machine learning. It has now been shown that many quantum neural networks converge to Gaussian processes, enabling their use for regression tasks.

The barren plateau problem represents one of the major bottlenecks for parametrized quantum circuits algorithms. Here, the authors study the known sources of BP using the lens of Lie algebraic theory, finding an expression of the variance of the loss function depending on the dynamical Lie algebra of the circuit.

There has been growing evidence that strategies to circumvent the barren plateau problem in variational quantum computing might also kill potential quantum advantages. In this Perspective, the authors gather this evidence, discuss what is still missing to provide a definitive answer, and provide new research directions.

Chemotherapy priming sensitizes triple-negative breast cancers to immune checkpoint blockade. However, immune suppressive myeloid cells may impede its optimal effect. Here authors characterise the immune suppressive myeloid cells via single-cell analyses of immune cells from low dose chemotherapy treated breast tumours and identify STAT1 signalling as a regulator for immune suppressive state.

Variational quantum algorithms (VQAs) are a leading candidate for useful applications of near-term quantum computing, but limitations due to unavoidable noise have not been clearly characterized. Here, the authors prove that local Pauli noise can cause vanishing gradients rendering VQAs untrainable.

Adipose tissue accommodates large volume changes upon expansion, but the molecular mechanisms involved are not fully understood. Here, Aboy et al. describe CAV1 Y14 phosphorylation as required for appropriate adipocyte caveolae flattening and homeostasis.

Parametrised quantum circuits are a promising hybrid classical-quantum approach, but rigorous results on their effective capabilities are rare. Here, the authors explore the feasibility of training depending on the type of cost functions, showing that local ones are less prone to the barren plateau problem.

Quantum kernel methods are usually believed to enjoy better trainability than quantum neural networks which may suffer from a well-studied barren plateau. Here, building over previous evidence, the authors show that practical implications of exponential concentration result in a trivial data-insensitive model after training, and identify commonly used features that induce the concentration.

The translation initiation complex mediates tumor immune escape in melanoma by controlling STAT1 mRNA levels and T lymphocyte–induced PD-L1 expression.

Sinking of organic matter represents the dominant mechanism for sequestration of anthropogenic CO₂in the deep sea. Here, the authors report the presence of healthy photosynthetic cells in the deep dark ocean, implying the fast injection of fresh organic carbon at depth across the global oligotrophic ocean.

DNA analysis of 6 individuals from eastern and south-central Africa spanning the past approximately 18,000 years, and of 28 previously published ancient individuals, provides genetic evidence supporting hypotheses of increasing regionalization at the end of the Pleistocene.

The power of quantum machine learning algorithms based on parametrised quantum circuits are still not fully understood. Here, the authors report rigorous bounds on the generalisation error in variational QML, confirming how known implementable models generalize well from an efficient amount of training data.

Field survey data from 1,034 bird species worldwide are used to show that species’ sensitivities to habitat fragmentation are more strongly related to their dispersal ability than to latitude and past habitat disturbance, and that variation in dispersal ability is in turn strongly associated with climate.

Barren plateaus are widely considered as one of the main limitations for variational quantum algorithms. This Review summarizes the latest understandings of barren plateaus, indicating its causes, architecture that will suffer from this phenomenon, and discusses strategies that can — and cannot — avoid it.

Fragmentation of forest ecosystems produces forest edges, which affect the distribution of many analysed vertebrate species; smaller-bodied amphibians, larger reptiles and medium-sized mammals experience a larger reduction in suitable habitat than other forest-core species.

Hypoxia promotes tumour aggressiveness and resistance of cancers to oncological treatment. Here, the authors show that caveolin-1 can down-regulate global membrane protein endocytosis in hypoxic cells with potential implications for targeting the hypoxic 3microenvironment of aggressive tumours.

Amyotrophic Lateral Sclerosis (ALS) is highly heritable but the mechanisms of sporadic ALS are not fully understood. In this study, the authors identify drivers of variation and disease-relevant changes in the epigenomic profile of iPSC-derived motor neuron lines generated from ALS patients and healthy controls as part of the Answer ALS program.

A machine learning approach is presented to identify dominant patterns in disease progression in amyotrophic lateral sclerosis (ALS), Alzheimer’s disease and Parkinson’s disease. The nonlinearity of ALS progression has important clinical implications.

Results for the final phase of the 1000 Genomes Project are presented including whole-genome sequencing, targeted exome sequencing, and genotyping on high-density SNP arrays for 2,504 individuals across 26 populations, providing a global reference data set to support biomedical genetics.

Answer ALS is a resource of patient-derived iPS cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This serves as a foundation to identify distinct disease subgroups.

The push for smaller microelectronics poses many challenges, such as locating dopant atoms in semiconductors with ever-increasing precision. The ideal technique must be able to detect single dopants with atomic resolution, and identify their electronic state. Neither is an easy task.

The advent of commercial quantum devices has ushered in the era of near-term quantum computing. Variational quantum algorithms are promising candidates to make use of these devices for achieving a practical quantum advantage over classical computers.

Quantum machine learning has become an essential tool to process and analyze the increased amount of quantum data. Despite recent progress, there are still many challenges to be addressed and myriad future avenues of research.