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Volume 4 Issue 7, July 2022

Quantum error mitigation with neural networks

The development of quantum hardware has reached a stage where meaningful quantum computing tasks are within reach, provided that the effects of noise can be mitigated. Most error mitigation methods require specific information about the noise channels that affect a quantum computation, the hardware implementation or the quantum algorithms themselves. Machine learning provides an alternative route to error mitigation, and Bennewitz et al. demonstrate a new technique that uses neural networks to mitigate errors in finding the quantum ground states of molecular Hamiltonians. The method is highlighted by the experimental preparation of the ground states of LiH at different bond lengths using IBM’s five-qubit chip, IBMQ-Rome.

See Elizabeth R. Bennewitz et al.

Image: Image courtesy of Dr. Derek Noon. Cover Design: Lauren Heslop


  • As with last summer, COVID-19 is still with us, but there is a semblance of what life was like before the pandemic. Here, we recommend AI podcasts from the past year that may inform, inspire or entertain, as we get an opportunity to travel or take time away from regular activities.



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Comment & Opinion

  • China is pushing ahead of the European Union and the United States with its new synthetic content regulations. New draft provisions would place more responsibility on platforms to preserve social stability, with potential costs for online freedoms. They show that the Chinese Communist Party is prepared to protect itself against the unique threats of emerging technologies.

    • Emmie Hine
    • Luciano Floridi
  • Artificial intelligence (AI) can support managers by effectively delegating management decisions to AI. There are, however, many organizational and technical hurdles that need to be overcome, and we offer a first step on this journey by unpacking the core factors that may hinder or foster effective decision delegation to AI.

    • Stefan Feuerriegel
    • Yash Raj Shrestha
    • Ce Zhang
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News & Views

  • Neural networks can be implemented by using purified DNA molecules that interact in a test tube. Convolutional neural networks to classify high-dimensional data have now been realized in vitro, in one of the most complex demonstrations of molecular programming so far.

    • William Poole
    News & Views
  • Microscopy-based drug screens with fluorescent markers can shed light on how drugs affect biological processes. Without adding markers and imaging channels, which is cumbersome and costly, a new generative deep-learning method extracts new fluorescence channels from images, potentially improving the drug-discovery pipeline.

    • Florian Heigwer
    News & Views
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  • So-called noisy intermediate-scale quantum devices will be capable of a range of quantum simulation tasks, provided that the effects of noise can be sufficiently reduced. A neural error mitigation approach is developed that uses neural networks to improve the estimates of ground states and ground-state observables of molecules and quantum systems obtained using quantum simulations on near-term devices.

    • Elizabeth R. Bennewitz
    • Florian Hopfmueller
    • Pooya Ronagh
  • Artificial DNA circuits that can perform neural network-like computations have been developed, but scaling up these circuits to recognize a large number of patterns is a challenging task. Xiong, Zhu and colleagues experimentally demonstrate a convolutional neural network algorithm using a synthetic DNA-based regulatory circuit in vitro and develop a freeze–thaw approach to reduce the computation time from hours to minutes, paving the way towards more powerful biomolecular classifiers.

    • Xiewei Xiong
    • Tong Zhu
    • Hao Pei
  • An end-to-end machine learning approach that can learn which mechanisms determine cell fate and competition from a large time-lapse microscopy dataset is developed. The approach makes use of a probabilistic autoencoder to learn an interpretable representation of the organization of cells, and provides cell fate predictions that can be tested in drug screening experiments.

    • Christopher J. Soelistyo
    • Giulia Vallardi
    • Alan R. Lowe
  • Deep learning methods can provide useful predictions for drug design, but their hyperparameters need to be carefully tweaked to give good performance on a specific problem or dataset. Li et al. present here a method that finds appropriate architectures and hyperparameters for a wide range of drug design tasks and can achieve good performance without human intervention.

    • Yuquan Li
    • Chang-Yu Hsieh
    • Xiaojun Yao
  • Using the natural dynamics of a legged robot for locomotion is challenging and can be computationally complex. A newly designed quadruped robot called Morti uses a central pattern generator inside two feedback loops as an adaptive method so that it efficiently uses the passive elasticity of its legs and can learn to walk within 1 h.

    • Felix Ruppert
    • Alexander Badri-Spröwitz
    Article Open Access
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