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| Open AccessReversibility of quantum resources through probabilistic protocols
The problem of reversibility within general quantum resource theories is still an open one. Here, the authors prove that a reversible entanglement manipulation framework (and, consequently, the concept of entanglement entropy) can be formally established by adjusting the setting to allow for probabilistic operations
- Bartosz Regula
- & Ludovico Lami
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Article
| Open AccessHigher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction
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
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Article
| Open AccessA programmable hybrid digital chemical information processor based on the Belousov-Zhabotinsky reaction
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
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| Open AccessReconfigurable perovskite X-ray detector for intelligent imaging
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
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Article
| Open AccessTwo-edge-resolved three-dimensional non-line-of-sight imaging with an ordinary camera
Ordinary cameras cannot directly see objects hidden around corners. Here, from an ordinary indirect 2D photograph, the authors compute a 3D image of a scene hidden behind a doorway by exploiting two perpendicular edges of the doorway.
- Robinson Czajkowski
- & John Murray-Bruce
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| Open AccessHarnessing synthetic active particles for physical reservoir computing
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
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Article
| Open AccessDistinguishing examples while building concepts in hippocampal and artificial networks
While the hippocampus is well-known to store specific memories, it can also learn common features that are shared across individual memories. Here, the authors show how this ability arises from dual input pathways and how it can inspire better machine learning methods.
- Louis Kang
- & Taro Toyoizumi
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Article
| Open AccessPhotonic Stochastic Emergent Storage for deep classification by scattering-intrinsic patterns
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
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| Open AccessSynergistic pretraining of parametrized quantum circuits via tensor networks
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
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Article
| Open AccessGravitationally induced decoherence vs space-time diffusion: testing the quantum nature of gravity
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
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| Open AccessMulti-client distributed blind quantum computation with the Qline architecture
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
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Article
| Open AccessOnline dynamical learning and sequence memory with neuromorphic nanowire networks
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
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Article
| Open AccessEfficient optimization with higher-order Ising machines
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
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| Open AccessThe complexity of NISQ
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
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| Open AccessBenchmarking universal quantum gates via channel spectrum
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
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| Open AccessSecurity of quantum key distribution from generalised entropy accumulation
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
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| Open AccessShadow estimation of gate-set properties from random sequences
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
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Article
| Open AccessExperimental validation of the free-energy principle with in vitro neural networks
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
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Article
| Open AccessDiffusion capacity of single and interconnected networks
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
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| Open AccessCertification of non-classicality in all links of a photonic star network without assuming quantum mechanics
Full network nonlocality, which certifies nonclassical behaviour in all sources of quantum networks, has so far only been demonstrated in the simplest scenarios. Here, the authors reach a complete experimental demonstration in a complex network involving three-qubit joint measurements.
- Ning-Ning Wang
- , Alejandro Pozas-Kerstjens
- & Armin Tavakoli
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| Open AccessCombining data and theory for derivable scientific discovery with AI-Descartes
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
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| Open AccessSeparation of scales and a thermodynamic description of feature learning in some CNNs
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
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| Open AccessFundamental energy cost of finite-time parallelizable computing
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
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| Open AccessThe learnability of Pauli noise
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
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Article
| Open AccessUncomputably complex renormalisation group flows
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
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| Open AccessGeometrical congruence, greedy navigability and myopic transfer in complex networks and brain connectomes
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
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Article
| Open AccessMultidimensional hyperspin machine
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
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Article
| Open AccessQuantum capacities of transducers
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
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| Open AccessFlexible learning of quantum states with generative query neural networks
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
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| Open AccessNoise-injected analog Ising machines enable ultrafast statistical sampling and machine learning
Ising machines are accelerators for computing difficult optimization problems. In this work, Böhm et al. demonstrate a method that extends their use to perform statistical sampling and machine learning orders-of-magnitudes faster than digital computers.
- Fabian Böhm
- , Diego Alonso-Urquijo
- & Guy Van der Sande
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| Open AccessThree learning stages and accuracy–efficiency tradeoff of restricted Boltzmann machines
Restricted Boltzmann Machines are unsupervised machine learning model that have been applied for various tasks from image analysis to many-body physics. The authors elaborate the interplay of accuracy and efficiency of this model and define possible balance regimes for applications.
- Lennart Dabelow
- & Masahito Ueda
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| Open AccessSmall-world complex network generation on a digital quantum processor
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
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Article
| Open AccessAnalytical solution for nonadiabatic quantum annealing to arbitrary Ising spin Hamiltonian
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
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Article
| Open AccessContrasting social and non-social sources of predictability in human mobility
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
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Article
| Open AccessKeyhole fluctuation and pore formation mechanisms during laser powder bed fusion additive manufacturing
Understanding the keyhole porosity formation is important in laser powder bed fusion. Here the authors reveal the dynamics of keyhole fluctuation, and collapse that induces bubble formation with three main stages of evolution; growth, shrinkage, and being captured by the solidification front.
- Yuze Huang
- , Tristan G. Fleming
- & Peter D. Lee
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Article
| Open AccessForecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations
Deep learning has an increasing impact to assist research. Here, authors show that a dynamical neural network, trained on a minimal amount of data, can predict the behaviour of spintronic devices with high accuracy and an extremely efficient simulation time.
- Xing Chen
- , Flavio Abreu Araujo
- & Damien Querlioz
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| Open AccessQuantum algorithmic measurement
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
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| Open AccessNoise-induced barren plateaus in variational quantum algorithms
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
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| Open AccessEntropic singularities give rise to quantum transmission
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
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| Open AccessFundamental limitations on distillation of quantum channel resources
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
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Article
| Open AccessEvent generation and statistical sampling for physics with deep generative models and a density information buffer
Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.
- Sydney Otten
- , Sascha Caron
- & Rob Verheyen
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| Open AccessCost function dependent barren plateaus in shallow parametrized quantum circuits
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
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Article
| Open AccessMachine learning in spectral domain
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
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Article
| Open AccessA unifying framework for mean-field theories of asymmetric kinetic Ising systems
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
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Article
| Open AccessUncomputability of phase diagrams
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
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Article
| Open AccessValley interference and spin exchange at the atomic scale in silicon
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
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Article
| Open AccessFrequency-domain ultrafast passive logic: NOT and XNOR gates
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
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| Open AccessDetecting and tracking drift in quantum information processors
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
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Article
| Open AccessLearning molecular dynamics with simple language model built upon long short-term memory neural network
Artificial neural networks have been successfully used for language recognition. Tsai et al. use the same techniques to link between language processing and prediction of molecular trajectories and show capability to predict complex thermodynamics and kinetics arising in chemical or biological physics.
- Sun-Ting Tsai
- , En-Jui Kuo
- & Pratyush Tiwary