Information theory and computation articles within Nature Communications

Featured

  • Article
    | Open Access

    For reservoir computing, improving prediction accuracy while maintaining low computing complexity remains a challenge. Inspired by the Granger causality, Li et al. design a data-driven and model-free framework by integrating the inference process and the inferred results on high-order structures.

    • Xin Li
    • , Qunxi Zhu
    •  & Wei Lin

  • Article
    | Open Access

    Computing platforms based on chemical processes can be an alternative to digital computers in some scenarios but have limited programmability. Here the authors demonstrate a hybrid computing platform combining digital electronics and an oscillatory chemical reaction and demonstrate its computational capabilities.

    • Abhishek Sharma
    • , Marcus Tze-Kiat Ng
    •  & Leroy Cronin

  • Article
    | Open Access

    In-sensor computing requires detectors with polarity reconfigurability and linear responsivity. Pang et al. report a CsPbBr3 perovskite single crystal X-ray detector for edge extraction imaging with a data compression ratio of 46.4% and classification task with an accuracy of 100%.

    • Jincong Pang
    • , Haodi Wu
    •  & Guangda Niu

  • Article
    | Open Access

    The ability of living systems to process signals and information is of vital importance. Inspired by nature, Wang and Cichos show an experimental realization of a physical reservoir computer using self-propelled active microparticles to predict chaotic time series such as the Mackey–Glass and Lorenz series.

    • Xiangzun Wang
    •  & Frank Cichos

  • Article
    | Open Access

    Photonic Stochastic Emergent Storage is a neuromorphic photonic device for image storage and classification based on scattering-intrinsic patterns. Here, the authors show emergent storage employs stochastic prototype scattering-induced light patterns to generate categories corresponding to emergent archetypes.

    • Marco Leonetti
    • , Giorgio Gosti
    •  & Giancarlo Ruocco

  • Article
    | Open Access

    Scalable training of parametrised quantum circuit approaches is usually hindered by the barren plateau issue. Here, the authors show how initializing parametrised quantum circuits starting from scalable tensor-network based algorithms could ameliorate the problem.

    • Manuel S. Rudolph
    • , Jacob Miller
    •  & Alejandro Perdomo-Ortiz

  • Article
    | Open Access

    Consistent theories have been proposed in which spacetime is treated classically while matter remains quantum. Here, the authors prove that such theories are constrained by a trade-off between the decoherence induced in the quantum system, and stochasticity in the classical one, providing a way to experimentally test the quantum nature of gravity.

    • Jonathan Oppenheim
    • , Carlo Sparaciari
    •  & Zachary Weller-Davies

  • Article
    | Open Access

    Multi-client demonstrations of blind quantum computation are still missing, due to their high resource overhead. Here, the authors fill this gap, by proposing a more scalable solution based on a recently introduced linear quantum network structure with high modularity, and demonstrating it in the two-client case.

    • Beatrice Polacchi
    • , Dominik Leichtle
    •  & Elham Kashefi

  • Article
    | Open Access

    Designing efficient neuromorphic systems based on nanowire networks remains a challenge. Here, Zhu et al. demonstrate brain-inspired learning and memory of spatiotemporal features using nanowire networks capable of MNIST handwritten digit classification and a novel sequence memory task performed in an online manner.

    • Ruomin Zhu
    • , Sam Lilak
    •  & Zdenka Kuncic

  • Article
    | Open Access

    Combinatorial optimization problems can be solved on parallel hardware called Ising machines. Most studies have focused on the use of second-order Ising machines. Compared to second-order Ising machines, the authors show that higher-order Ising machines realized with coupled-oscillator networks can be more resource-efficient and provide superior solutions for constraint satisfaction problems.

    • Connor Bybee
    • , Denis Kleyko
    •  & Friedrich T. Sommer

  • Article
    | Open Access

    Our current understanding of the computational abilities of near-intermediate scale quantum (NISQ) computing devices is limited, in part due to the absence of a precise definition for this regime. Here, the authors formally define the NISQ realm and provide rigorous evidence that its capabilities are situated between the complexity classes BPP and BQP.

    • Sitan Chen
    • , Jordan Cotler
    •  & Jerry Li

  • Article
    | Open Access

    Performing quantum computing in the NISQ era requires reliable information on the gate noise characteristics and their performance benchmarks. Here, the authors show how to estimate the individual noise properties of any quantum process from the noisy eigenvalues of its corresponding quantum channel.

    • Yanwu Gu
    • , Wei-Feng Zhuang
    •  & Dong E. Liu

  • Article
    | Open Access

    Security proofs against general attacks are the ultimate goal of QKD. Here, the authors show how the Generalised Entropy Accumulation Theorem can be used, for some classes of QKD scenarios, to translate security proofs against collective attacks in the asymptotic regime into proofs against general attacks in the finite-size regime.

    • Tony Metger
    •  & Renato Renner

  • Article
    | Open Access

    In order to be practical, schemes for characterizing quantum operations should require the simplest possible gate sequences and measurements. Here, the authors show how random gate sequences and native measurements (followed by classical post-processing) are sufficient for estimating several gate set properties.

    • J. Helsen
    • , M. Ioannou
    •  & I. Roth

  • Article
    | Open Access

    Empirical applications of the free-energy principle entail a commitment to a particular process theory. Here, the authors reverse engineered generative models from neural responses of in vitro networks and demonstrated that the free-energy principle could predict how neural networks reorganized in response to external stimulation.

    • Takuya Isomura
    • , Kiyoshi Kotani
    •  & Karl J. Friston

  • Article
    | Open Access

    Understanding of diffusive and spreading processes in networks remains challenging when dynamics of the network is complex. The authors propose a quantity to reflect the potential of a network node to diffuse information, that may serve to develop interventions for improved network efficiency.

    • Tiago A. Schieber
    • , Laura C. Carpi
    •  & Martín G. Ravetti

  • Article
    | Open Access

    Automatic extraction of consistent governing laws from data is a challenging problem. The authors propose a method that takes as input experimental data and background theory and combines symbolic regression with logical reasoning to obtain scientifically meaningful symbolic formulas.

    • Cristina Cornelio
    • , Sanjeeb Dash
    •  & Lior Horesh

  • Article
    | Open Access

    In the quest to understand how deep neural networks work, identification of slow and fast variables is a desirable step. Inspired by tools from theoretical physics, the authors propose a simplified description of finite deep neural networks based on two matrix variables per layer and provide analytic predictions for feature learning effects.

    • Inbar Seroussi
    • , Gadi Naveh
    •  & Zohar Ringel

  • Article
    | Open Access

    Based on fundamental thermodynamics, traditional electronic computers, which operate serially, require more energy per computation the faster they operate. Here, the authors show that the energy cost per operation of a parallel computer can be kept very small.

    • Michael Konopik
    • , Till Korten
    •  & Heiner Linke

  • Article
    | Open Access

    Characterisation of quantum hardware requires clear indications on what can and cannot be learned about quantum noise. Here, the authors show how to characterise learnable degrees of freedom of a Clifford gate using tools from algebraic graph theory.

    • Senrui Chen
    • , Yunchao Liu
    •  & Liang Jiang

  • Article
    | Open Access

    Renormalisation group methods serve for finding analytic solutions, critical points and computing phase diagrams of many-body systems. Here the authors demonstrate that renormalisation group schemes can be constructed for undecidable many-body systems, giving rise to the types of renormalisation group flow which are strictly more unpredictable than chaotic flows.

    • James D. Watson
    • , Emilio Onorati
    •  & Toby S. Cubitt

  • Article
    | Open Access

    The manifold’s geometry underlying the connectivity of a complex network determines its navigation ruled by the nodes distances in the geometrical space. In this work, the authors propose an algorithm which allows to uncover the relation between the measures of geometrical congruency and efficient greedy navigability in complex networks.

    • Carlo Vittorio Cannistraci
    •  & Alessandro Muscoloni

  • Article
    | Open Access

    Spin simulators can solve many combinatorial optimization problems that can be represented by spin models, but they are limited to low-dimensional spins. Here the authors propose a simulator of multidimensional spins in arbitrary dimension, using a system of coupled parametric oscillators with a common pump.

    • Marcello Calvanese Strinati
    •  & Claudio Conti

  • Article
    | Open Access

    A unified metric to assess the performances of quantum transducers, i.e., converters of quantum information between different physical systems - is still lacking. Here the authors propose quantum capacity as such metric, and use it to investigate the optimal designs of generic quantum transduction schemes.

    • Chiao-Hsuan Wang
    • , Fangxin Li
    •  & Liang Jiang

  • Article
    | Open Access

    The use of machine learning to characterise quantum states has been demonstrated, but usually training the algorithm using data from the same state one wants to characterise. Here, the authors show an algorithm that can learn all states that share structural similarities with the ones used for the training.

    • Yan Zhu
    • , Ya-Dong Wu
    •  & Giulio Chiribella

  • Article
    | Open Access

    The study of complexity in quantum systems is a fascinating topic, which however is still in its infancy, especially at the experimental level. Here, the authors report on the observation of “small-world” characteristics in the network of quantum correlations within chains of up to 23 superconducting qubits long.

    • Eric B. Jones
    • , Logan E. Hillberry
    •  & Lincoln D. Carr

  • Article
    | Open Access

    The computational capabilities of quantum annealing in the accessible regimes of operation are still subject to debate. Here, the authors study a model admitting an analytical solution far from the adiabatic regime, and show evidences of better convergence and energy relaxation rates over classical annealing.

    • Bin Yan
    •  & Nikolai A. Sinitsyn

  • Article
    | Open Access

    Information about an individual’s mobility can leave traces embedded in the social network. The authors show that such traces are also present beyond the social network. Simple colocation contains predictive information about one’s mobility patterns even when the colocators have no social links. In the aggregate, non-social information can sometimes meet or exceed social information.

    • Zexun Chen
    • , Sean Kelty
    •  & Gourab Ghoshal

  • Article
    | Open Access

    Applying the language of computational complexity to study real-world experiments requires a rigorous framework. Here, the authors provide such a framework and establish that there can be an exponential savings in resources if an experimentalist can entangle apparatuses with experimental samples.

    • Dorit Aharonov
    • , Jordan Cotler
    •  & Xiao-Liang Qi

  • Article
    | Open Access

    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.

    • Samson Wang
    • , Enrico Fontana
    •  & Patrick J. Coles

  • Article
    | Open Access

    Non-additivity of the quantum channel coherent information is known to occur in some very noisy channels, but its fundamental origin is unclear. Here, the author explains its link with log singularity of quantum entropy, and shows that it can also come up for low-noise channels.

    • Vikesh Siddhu

  • Article
    | Open Access

    Several key tasks in quantum information processing can be regarded as channel manipulation. Here, focusing on the class of distillation protocols, the authors derive general bounds on resource overhead and incurred errors, showing application to magic state distillation and quantum channel capacities.

    • Bartosz Regula
    •  & Ryuji Takagi

  • Article
    | Open Access

    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.

    • M. Cerezo
    • , Akira Sone
    •  & Patrick J. Coles

  • Article
    | Open Access

    Theoretical aspects of automated learning from data involving deep neural networks have open questions. Here Giambagli et al. show that training the neural networks in the spectral domain of the network coupling matrices can reduce the amount of learning parameters and improve the pre-training process.

    • Lorenzo Giambagli
    • , Lorenzo Buffoni
    •  & Duccio Fanelli

  • Article
    | Open Access

    Many mean-field theories are proposed for studying the non-equilibrium dynamics of complex systems, each based on specific assumptions about the system’s temporal evolution. Here, Aguilera et al. propose a unified framework for mean-field theories of asymmetric kinetic Ising systems to study non-equilibrium dynamics.

    • Miguel Aguilera
    • , S. Amin Moosavi
    •  & Hideaki Shimazaki

  • Article
    | Open Access

    Phase diagrams describe how a system changes phenomenologically as an external parameter, such as a magnetic field strength, is varied. Here, the authors prove that in general such a phase diagram is uncomputable, by explicitly constructing a one-parameter Hamiltonian for which this is the case.

    • Johannes Bausch
    • , Toby S. Cubitt
    •  & James D. Watson

  • Article
    | Open Access

    Coupled donor wavefunctions in silicon are spatially resolved to evidence valley interference processes. An atomic-scale understanding of the interplay between interference, envelope anisotropy and crystal symmetries unveils a placement strategy compatible with existing technology where the exchange is insensitive to interference.

    • B. Voisin
    • , J. Bocquel
    •  & S. Rogge

  • Article
    | Open Access

    Typically, Boolean logic gates have to compromise between high speed and low energy consumption which can become limiting at scale. Here, the authors demonstrate architectures for NOT and XNOR gates that enable simultaneous low power and fast operation.

    • Reza Maram
    • , James van Howe
    •  & José Azaña

  • Article
    | Open Access

    Time-dependent errors are one of the main obstacles to fully-fledged quantum information processing. Here, the authors develop a general methodology to monitor time-dependent errors, which could be used to make other characterisation protocols time-resolved, and demonstrate it on a trapped-ion qubit.

    • Timothy Proctor
    • , Melissa Revelle
    •  & Kevin Young

  • Article
    | Open Access

    According to Zipf’s law, the population size of a city is inversely proportional to its size rank in any urban system. The authors show how demography explains this law as a time average of balanced migration between cities and how deviations express information about people’s net preferences.

    • Luís M. A. Bettencourt
    •  & Daniel Zünd

  • Article
    | Open Access

    The no-signaling principle constrains which multipartite correlations are allowed, but network scenarios considered so far were limited to specific cases. Here, the authors apply inflation technique to the no-input/binary-output triangle network, and show that it admits non-trilocal distributions.

    • Nicolas Gisin
    • , Jean-Daniel Bancal
    •  & Nicolas Brunner

Browse broader subjects

Browse Information theory and computation across other nature.com journals