According to the subjective Bayesian interpretation of quantum theory (QBism), quantum mechanics is a tool that an agent would be wise to use when making bets about natural phenomena. In particular, the Born rule is understood to be a decision-making norm, an ideal which one should strive to meet even if usually falling short in practice. What is required for an agent to make decisions that conform to quantum mechanics? Here we investigate how a realistic (hence nonideal) agent might deviate from the Born rule in its decisions. To do so we simulate a simple agent as a reinforcement-learning algorithm that makes “bets” on the outputs of a symmetric informationally-complete measurement (SIC) and adjusts its decisions in order to maximize its expected return. We quantify how far the algorithm's decision-making behavior departs from the ideal form of the Born rule and investigate the limiting factors. We propose an experimental implementation of the scenario using heralded single photons.

We quantify how squeezed light can reduce quantum measurement noise to levels below the standard quantum limit in impulse measurements with mechanical detectors. The broadband nature of the signal implies that frequency-dependent squeezing performs better than frequency-independent squeezing. We calculate the optimal scaling of the impulse sensitivity with the squeezing strength and quantify degradations due to photodetection losses. Even for lossless measurement, we find there exists a fundamental limit to the benefit of squeezing that depends only on the system's mechanical properties.

We investigate the vibrational properties of polycrystalline monodisperse and disordered bidisperse granular packings during jamming and unjamming using discrete element method simulations. Both systems deviate from Debye scaling at low frequencies $\left(\omega \right)$, but only bidisperse packings exhibit a low-$\omega$ plateau. The low $\omega$ exponent ($\alpha$) in bidisperse packings evolves smoothly from zero (plateau) to near one (Debye scaling) with increasing packing fraction, whereas in polycrystalline packings, it changes discontinuously near jamming/unjamming, due to the nature of the contact network rearrangements. Despite structural modifications during the compression-decompression cycle, the exponent remains unchanged at the same distance from jamming density, regardless of the history. Nonaffine displacements and contact orientational order further confirm that structural features that impact low-$\omega$ vibrational states and, hence, mechanical properties are largely restored upon decompression, reinforcing vibrational similarities between jamming and unjamming states.

Assessing the stability of synchronization is a fundamental task when studying networks of dynamical systems. However, this becomes challenging when the coupled systems are not exactly identical, as is always the case in practical settings. Here we introduce an extension of the Master Stability Function to determine near-synchronization stability within simplicial complexes of nearly identical systems coupled by synchronization-noninvasive functions. We validate our method on a simplicial complex of Lorenz oscillators, finding a good correspondence between the predicted regions of stability and those observed via direct simulation. This confirms the correctness of our approach, making it a valuable tool for the evaluation of real-world systems, in which differences between the constitutive elements are unavoidable.

The dc conductivity tensor of two-dimensional one-band metals with weak pointlike disorder and magnetic field is studied in the self-consistent Born approximation, with special emphasis on the regime of low carrier density. In this theory, the Kubo conductivity is a functional of the electron dispersion and local (momentum-independent) electron self-energy, which is itself a causal functional of the dispersion and disorder strength. We obtain closed expressions for the asymptotic low-density conductivities at zero temperature in the form of power laws of the density and disorder strength with universal exponents. The crossover to the semiclassical regime of high density is studied numerically, as well as the temperature dependence. Our model and results may be relevant to interpret linear magnetotransport experiments performed in the metallic regime of gated two-dimensional semiconductors.

Cavity quantum electrodynamics (QED) investigates the interaction between resonator-confined radiation and natural atoms. Similar phenomena can be explored using superconducting artificial atoms coupled to microwave resonators. Here, we examine incoherent and coherent spectra in a circuit QED system consisting of a flux qubit coupled to an LC resonator, considering different coupling configurations to the output transmission line. Although such systems can be effectively described by the quantum Rabi model, we demonstrate that the choice of the coupling between the resonator and the input-output channel can significantly affect the emission properties, particularly in the ultrastrong coupling regime. Specifically, we show that when the resonator is capacitively coupled with the input-output transmission line, the spectral features at zero flux offset can be mapped onto those of the cavity QED model. However, this correspondence breaks down for the mutual inductive coupling case. Extending our analysis to the parity-symmetry broken regime of the flux qubit, we further highlight how capacitive and inductive coupling mechanisms lead to distinct spectral features, emphasizing the necessity of a careful input-output treatment for an accurate description of the emission processes.

Graph-structured data provide an integrated description of complex systems, encompassing not only the interactions among nodes, but also the intrinsic features that characterize these nodes. These features play a fundamental role in the formation of links within the network, making them valuable for extracting meaningful information. In this paper, we present a comprehensive framework that treats features as tangible entities and establishes a bipartite graph connecting nodes and features. By assuming that nodes sharing similarities should also share features, we introduce a hyperbolic geometric space where both nodes and features coexist, shaping the structure of both the node network and the bipartite network of nodes and features. Through this framework, we can identify correlations between nodes and features in real data and generate synthetic datasets that mimic the topological properties of their connectivity patterns. Notably, node features are at the core of deep learning techniques, such as graph convolutional neural networks (GCNs), offering great utility in downstream tasks. Therefore, our approach provides insights into the inner workings of GCNs by revealing the intricate structure of the data.

Predicting the measurable statistical properties of density fluctuations in a supersonic compressible turbulent flow is a major challenge in physics. In 1951, Chandrasekhar derived an invariant under the assumption of the statistical homogeneity and isotropy of the turbulent density field and stationarity of the background density. Recently, Jaupart and Chabrier [Astrophys. J. Lett. 922, L36 (2021)] extended this invariant to nonisotropic flows in a time-evolving background and showed that it has the dimension of a mass. This invariant $M_{\mathrm{inv}}$ is defined by $M_{\mathrm{inv}}=\mathbb{E}\left(\rho \right)\text{Var}\left(\frac{\rho }{\mathbb{E}\left(\rho \right)}\right){\left(l_{\mathrm{c}}^{\rho }\right)}^3$, where $\rho$ is the density field and $l_{\mathrm{c}}^{\rho }$ is the correlation length. In this article, we perform numerical simulations of homogeneous and isotropic compressible turbulence to test the validity of this invariant in a medium subject to isotropic decaying turbulence. We study several input configurations, namely different Mach numbers, injection lengths of turbulence, and equations of state. We confirm that $M_{\mathrm{inv}}$ remains constant during the decaying phase of turbulence. Furthermore, we develop a theoretical model of the density field statistics which predicts without any free parameter the evolution of the correlation length with the variance of the log-density field beyond the assumption of the Gaussian field for the log density. Noting that $M_{\mathrm{inv}}$ is independent of the Mach number, we show that this invariant can be used to relate the non-Gaussian evolution of the log-density probability distribution function to its variance with no free parameters.

We show that a composite quantum system described by the tensor product of multiple systems each with a leading-order exceptional point (a non-Hermitian degeneracy at which not only eigenvalues but also eigenstates coalesce) exhibits a single leading-order exceptional point, whose order surpasses the order of any constituent exceptional point. The formation of such higher-order exceptional points does not require coupling among the subsystems. We determine explicitly the order and the spectral response strength of this exceptional point. Moreover, we observe that the energy eigenstates that do not merge are entangled. Finally, we demonstrate that general initial states disentangle during time evolution due to the presence of the higher-order exceptional point of the composite system.

We present a systematic framework to quantify the interplay between coherence and wave-particle duality in generic two-path interference systems. Our analysis reveals a closed-form duality ellipse (DE) equality, that rigorously unifies visibility (a traditional waveness measure) and predictability (a particleness measure) with degree of coherence, providing a complete mathematical embodiment of Bohr's complementarity principle. Extending this framework to quantum imaging with undetected photons (QIUP), where both path information and photon interference are inherently linked to spatial object reconstruction, we establish an imaging duality ellipse (IDE) that directly connects wave-particle duality to the object's transmittance profile. This relation enables object characterization through duality measurements alone and remains robust against experimental imperfections such as decoherence and misalignment. Our results advance the fundamental understanding of quantum duality while offering a practical toolkit for optimizing coherence-driven quantum technologies, from imaging to sensing.

In kagome metals, flat electronic bands induced by frustrated hopping are a platform for strong electron correlations. Here, we investigate the superconductivity in the kagome system ${\mathrm{LaRu}}_3{\left({\mathrm{Si}}_{1-x}{\mathrm{Ge}}_x\right)}_2$ by chemical pressure tuning while preserving the Ru-$4d$ states that constitute the kagome flat bands. We observe a sizable enhancement in the density of states up to $x=0.07$, as determined by the specific heat, with a concomitant increase in the superconducting transition temperature $T_{\mathrm{c}}$. Ge dopants induce a uniaxial lattice expansion along the $c$ axis. Our first-principles calculations suggest that this mitigates the detrimental effect of hybridization between kagome layers and reduces the dispersion of the Ru-$d_{x^2-y^2}$ flat band. The calculated chemical potential moves closer to the maximum in the energy-dependent density of states. Our result is consistent with a theoretical prediction of tunable flat-band superconductivity in ${\mathrm{LaRu}}_3{\mathrm{Si}}_2$ by mode-selective coupling between specific kagome phonons and the Ru-$d_{x^2-y^2}$ orbitals.

Chaos presents complex dynamics arising from nonlinearity and a sensitivity to initial states. These characteristics suggest a depth of expressivity that underscores their potential for advanced computational applications. However, strategies to effectively exploit chaotic dynamics for information processing have largely remained elusive. In this study, we reveal that the essence of chaos can be found in various state-of-the-art deep neural networks. Drawing inspiration from this revelation, we propose a novel method that directly leverages chaotic dynamics for deep learning architectures. Our approach is systematically evaluated across distinct chaotic systems. In all instances, our framework presents superior results to conventional deep neural networks in terms of accuracy, convergence speed, and efficiency. Furthermore, we find an active role of transient chaos formation in our framework. Collectively, this study opens up a new path for the integration of chaos, which has long been overlooked in information processing, and provides insights into the potential fusion of chaotic dynamics within the domains of machine learning and neuromorphic computation.

Small—but finite—fluid inertia can be leveraged to generate steady flows out of liquid vibrations around an immersed interface. In engineering, external high-frequency drivers $\left({10}^2\text{–}{10}^5\mathrm{Hz}\right)$ allow this inertial rectification phenomenon, known as viscous streaming, to be employed in micron-scale devices for precise flow control, particle manipulation, and spatially controlled chemistry. However, beyond artificial settings, streaming has been hypothesized to be accessible by larger-scale biological systems pertaining to lower frequencies. Then millimeter-size organisms that oscillate or pulsate cilia and appendages in the 1 to $10\mathrm{Hz}$ range may be able to endogenously rectify surrounding flows, for feeding or locomotion, removing the need for external actuators, tethers, or tubing. Motivated by this potential for bio-hybrid robotic applications and biophysical exploration, here we demonstrate an in vitro living system able to produce streaming flows endogenously, autonomously, and unassisted. Computationally informed, our biological device generates oscillatory flows through the cyclic contractions of an engineered muscle tissue, shaped in the form of a torus and suspended in fluid within a microparticle image velocimetry setup. Flow patterns consistent with streaming simulations are observed for low-frequency muscle contractions $(2–4\mathrm{Hz})$, either spontaneous or light-induced, illustrating system autonomy and controllability, respectively. Thus, by connecting tissue engineering with hydrodynamics, this work provides experimental evidence of biologically powered streaming in untethered, millimeter-scale living systems, endowing bio-hybrid technology with inertial microfluidic capabilities. It also illustrates the potential of combining bio-hybrid platforms and simulations to advance both biophysical understanding and fluid mechanics.

We present a robust error accumulation suppression (REAS) technique to manage errors in quantum computers. Our method reduces the accumulation of errors in any quantum circuit composed of single- or two-qubit gates expressed as $e^{-i\sigma \theta }$ for Pauli operators $\sigma$ and $\theta \in [0,\pi )$, which forms a universal gate set. For coherent errors, which include gate overrotation and crosstalk, we demonstrate a reduction of the error scaling in an $L$-depth circuit from $O\left(L\right)$ to $O\left(\sqrt{L}\right)$. This asymptotic error suppression behavior can be proven in a regime where all gates, including those constituting the error-suppressing protocol itself, are noisy. Going beyond coherent errors, we derive the general form of decoherence noise that can be suppressed by REAS. Lastly, we experimentally demonstrate the effectiveness of our approach regarding realistic errors using 100-qubit circuits with up to 64 two-qubit gate layers on IBM Quantum processors.