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According to a 2019 United Nations report, of all the known species, up to 1 million face extinction globally. Despite being considered a pressing global risk with several international efforts to protect and to restore, biodiversity loss and the degradation of ecosystems continue at an alarming rate. In December 2022, the UN Biodiversity Conference (COP15) saw the adoption of the Kunming-Montreal Global Biodiversity Framework, where four overarching international goals for biodiversity and 23 targets were set. While this is a positive step towards addressing the drivers of biodiversity loss, we will need not just public and political will but also more effective methods to integrate and use scientific information to reach the goals and targets outlined. To facilitate this, scientists and research institutions need to establish alternative and new approaches to transform the way science is conducted, communicated, and integrated into the policymaking process. This will require the scientific community to become proficient at working in interdisciplinary and transdisciplinary teams, establishing connectivity across scientific disciplines and engaging in the policymaking process to ensure that the best available scientific evidence is not only comprehensible to decision-makers but also timely and relevant. This commentary details how scientists can embrace transformative change within and outside of their own communities to increase the impact of their research and help reach global targets that benefit society.

This report outlines recommendations for science policy regarding biodiversity and pandemics that need to be implemented in order to develop broader transformative policies for human, animal, and ecosystem health. In particular, we focus on policies to support and promote research needed to better prevent and manage spillover events in which pathogens originating in wildlife cause disease outbreaks or even pandemics in humans or large-scale disease events in domesticated animals (panzootics). The report was prepared by an Eklipse Expert Working Group (EWG) active from 2022-2023. The EWG consisted of scientists with relevant expertise in the natural, biomedical, and social sciences. The group undertook a scoping review of scientific literature and collected input from a large body of external experts through an online survey and focus group discussions. Using these results, the EWG formulated recommendations for shaping a strategic research agenda on biodiversity and pandemics. These recommendations address the critical interlinkages between relevant sectors and stakeholders via targeted transdisciplinary research to make future actions more effective. Crucially, in the following recommendations we call for moving beyond incremental changes to science policy and bringing about transformative change to how research is carried out, organised, and financed. Only in this way will science itself deliver the knowledge and insights needed to develop policies that will help transform human animal and ecosystem health, avoiding the accumulation of problems generated by the still widespread business as usual approach to both research and broader policymaking. Our ability to deliver this transformative change is essential to confront other global crises, including the climate and biodiversity crises, that are currently threatening our societies.

Boosting is a general method to convert a weak learner (which generates hypotheses that are just slightly better than random) into a strong learner (which generates hypotheses that are much better than random). Recently, Arunachalam and Maity [Srinivasan Arunachalam and Reevu Maity, 2020] gave the first quantum improvement for boosting, by combining Freund and Schapire’s AdaBoost algorithm with a quantum algorithm for approximate counting. Their booster is faster than classical boosting as a function of the VC-dimension of the weak learner’s hypothesis class, but worse as a function of the quality of the weak learner. In this paper we give a substantially faster and simpler quantum boosting algorithm, based on Servedio’s SmoothBoost algorithm [Servedio, 2003].