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Quantum systems interacting with an unknown environment are notoriously difficult to model, especially in presence of non-Markovian and non-perturbative effects. Here we introduce a neural network based approach, which has the mathematical simplicity of the Gorini-Kossakowski-Sudarshan-Lindblad master equation, but is able to model non-Markovian effects in different regimes. This is achieved by using recurrent neural networks (RNNs) for defining Lindblad operators that can keep track of memory effects. Building upon this framework, we also introduce a neural network architecture that is able to reproduce the entire quantum evolution, given an initial state. As an application we study how to train these models for quantum process tomography, showing that RNNs are accurate over different times and regimes.

Adversarial learning is one of the most successful approaches to modeling high-dimensional probability distributions from data. The quantum computing community has recently begun to generalize this idea and to look for potential applications. In this work, we derive an adversarial algorithm for the problem of approximating an unknown quantum pure state. Although this could be done on universal quantum computers, the adversarial formulation enables us to execute the algorithm on near-term quantum computers. Two parametrized circuits are optimized in tandem: one tries to approximate the target state, the other tries to distinguish between target and approximated state. Supported by numerical simulations, we show that resilient backpropagation algorithms perform remarkably well in optimizing the two circuits. We use the bipartite entanglement entropy to design an efficient heuristic for the stopping criterion. Our approach may find application in quantum state tomography.

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

Machine learning: Quantum networks for classical and quantum dataQuantum algorithms with hierarchical tensor network structures may provide an efficient approach to machine learning using quantum computers. Recent theoretical work has indicated that quantum algorithms could have an advantage over classical methods for the linear algebra computations involved in machine learning. At the same time, mathematical structures called tensor networks, with some similarities to neural networks, have been shown to represent quantum states and circuits that can be efficiently evaluated. Edward Grant from University College London and colleagues from the UK and China have shown how quantum algorithms based on two tensor network structures can be used to classify both classical and quantum data. If implemented on a large scale quantum computer, their approach may enable classification of two-dimensional images and entangled quantum data more efficiently than is possible with classical methods.

Epidemiological evidence has long associated environmental mutagens with increased cancer risk. However, links between specific mutation-causing processes and the acquisition of individual driver mutations have remained obscure. Here we have used public cancer sequencing data from 11,336 cancers of various types to infer the independent effects of mutation and selection on the set of driver mutations in a cancer type. First, we detect associations between a range of mutational processes, including those linked to smoking, ageing, APOBEC and DNA mismatch repair (MMR) and the presence of key driver mutations across cancer types. Second, we quantify differential selection between well-known alternative driver mutations, including differences in selection between distinct mutant residues in the same gene. These results show that while mutational processes have a large role in determining which driver mutations are present in a cancer, the role of selection frequently dominates. A central question in cancer research is how specific driver mutations are acquired and maintained during cancer development. Here Temko et al. use public sequencing data to infer the effect of mutation and selection on a set of driver mutations and suggest that selection frequently dominates.

The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network we here demonstrate that cancer cells are characterised by an increase in network entropy. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local network entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local correlation patterns. In particular, we find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in network entropy. These findings may have potential implications for identifying novel drug targets.

The Italian legislator has adopted several instruments to discourage undeclared work and exploitative labour in agriculture, mostly of a penal-repressive nature. Among the direct and indirect policy measures, the ‘Quality Agricultural Work Network’ represents an interesting approach to producing a ‘whitelist’ of farmers compliant with labour regulations. A law proposal intends to establish the ‘Quality Agricultural Work’ (QAW) ethical label to incentivise farmers to join the network, to which a limited percentage of farms have signed up. This study aims to investigate consumer preferences for the QAW label in the Italian fruit sector. We conducted a choice experiment on a sample of 324 consumers. Willingness to pay for ethical labels was estimated before and after information treatment was administered to evaluate the prospective effects of promotional and information campaigns. The information treatment conveyed a clear and concise message about the QAW project and its ethical label. The results show that consumers would pay a high price premium for fruit produced under fair working conditions, indicating that there may be a market space for the QAW label. Moreover, consumers perceive environmental and social sustainability claims as complementary contexts where both dimensions of sustainability are relevant.Key pointsA law proposal intends to establish a ‘Quality Agricultural Work’ (QAW) ethical label to incentivise farmers to join the ‘Quality Agricultural Work Network’.We conducted a choice experiment to investigate consumer preferences for the QAW label in the Italian fruit sector.The results show that consumers would pay a high price premium for fruit produced under fair working conditions.

Facioscapulohumeral muscular dystrophy (FSHD) is a prevalent, incurable myopathy, linked to hypomethylation of D4Z4 repeats on chromosome 4q causing expression of the DUX4 transcription factor. However, DUX4 is difficult to detect in FSHD muscle biopsies and it is debatable how robust changes in DUX4 target gene expression are as an FSHD biomarker. PAX7 is a master regulator of myogenesis that rescues DUX4-mediated apoptosis. Here, we show that suppression of PAX7 target genes is a hallmark of FSHD, and that it is as major a signature of FSHD muscle as DUX4 target gene expression. This is shown using meta-analysis of over six FSHD muscle biopsy gene expression studies, and validated by RNA-sequencing on FSHD patient-derived myoblasts. DUX4 also inhibits PAX7 from activating its transcriptional target genes and vice versa. Furthermore, PAX7 target gene repression can explain oxidative stress sensitivity and epigenetic changes in FSHD. Thus, PAX7 target gene repression is a hallmark of FSHD that should be considered in the investigation of FSHD pathology and therapy. Facioscapulohumeral muscular dystrophy is a myopathy linked to ectopic expression of the DUX4 transcription factor. The authors show that the suppression of targets genes of the myogenesis regulator PAX7 is a signature of FSHD, and might explain oxidative stress sensitivity and epigenetic changes.

Much attention has been paid to the impact of the agriculture sector on the environment and its contribution to climate change. However, the sector is also vulnerable to the impacts of climate extremes. Thus, a growing number of studies have focused on adapting to these changes. We conducted a systematic literature review and assessment of the evidence gathered from 124 studies on the impacts of and adaptation to climate change in the agriculture sector in OECD member countries. Results highlight a significant knowledge gap in understanding the full economic effects of climate change as the impacts of climate change on input costs is not extensively studied in the way that impacts on farm output is. Additionally, there is a need to understand the indicators used to assess climate impacts in agriculture for easier comparison across studies. We recommend targeted research and funding to close this knowledge gap including conducting long term analyses to evaluate the costs and benefits of adaptation strategies as well as capacity building and knowledge exchange.

Durum wheat is essential for global food security. Nevertheless, its cultivation is susceptible to hazards, including unpredictability in yield and grain quality. This systematic review and bibliometric analysis identify factors influencing durum wheat yield and quality, assessing the degree of control farmers have over these factors. The goal is to understand their impact on production risks. Peer-reviewed studies in English from 1990 to April 2024 that focused on the yield or quality of durum wheat were included, while those lacking specific data or not peer-reviewed were excluded. Data were acquired via the Web of Science (WoS), with the concluding search conducted in April 2024. Results were synthesized from 2131 studies selected from an initial pool of 5159, using a bibliometric approach to categorize findings into standard, biotic, abiotic, and other factors. Analysis revealed that practices like irrigation and nitrogen fertilization improve yields, while genetic advancements boost stress resilience. These insights support targeted agronomic strategies. Despite potential biases and inconsistencies, the review underscores key strategies to enhance durum wheat risk management and bolster food security. This study was funded by the Italian Ministry of University and Research (CURSA, D.I.Ver.So.) and PRIN—2020 Call.