The computation of thermal properties of quantum many-body systems is a central challenge in our understanding of quantum mechanics. We introduce the quantum finite-temperature Lanczos method (QFTLM), which extends the finite-temperature Lanczos method to quantum computers by combining real-time quantum Krylov methods with efficient preparation of typical states for trace estimation. This approach enables the computation of thermal expectation values while avoiding the exponential scaling inherent to classical exact simulation techniques. Numerical experiments on the transverse-field Ising model show that QFTLM can reproduce thermal observables over a wide temperature range. We further analyze the influence of Krylov dimension, number of trace-estimator states, and Trotter error, and show that suitable regularization is essential for robustness in noisy settings. These results establish QFTLM as a promising framework for finite-temperature quantum simulation.

In both classical and quantum thermodynamics, physical quantities are typically assigned objective values defined independently of our observations. We then refer to the “work performed by a gas,” or the “entropy of the gas,” regardless of how they are evaluated. Here, we question this conception in the context of quantum thermodynamics, estimating how the definition of pivotal thermodynamic quantities is affected by experimental instruments of limited precision. We find that the coarse-grained thermodynamic quantities frequently lead to different conclusions from those drawn in fine-grained scenarios. For instance, the irreversibility of a process, or its work payoff, can significantly vary with the instrument precision. We show nonetheless that coarse-grained thermodynamic quantities satisfy the same relations (i.e., the second law inequality, the relation between dissipation and distinguishability of a process from its time reverse, and the quantum work fluctuation theorems) as their fine-grained counterparts. These results highlight the observation independence of relations linking thermodynamic quantities, which are themselves observation dependent.

Microfibers contaminate the global ocean, but their impact depends on length-dependent residence at the air-sea interface. In the submillimeter range, the sea surface exhibits a surface-enrichment signature: The long-to-short fiber ratio is higher at the interface than a few meters below (about 5 m), meaning that longer fibers are relatively more represented at the surface, despite an approximately length-flat fragment production expected below the fiber breakage cutoff. We show that this contrast is consistent with length-selective retention in the sea-surface microlayer via capillary pinning and attachment to sparse anchoring sites. A minimal two-state mixed-layer column model yields an approximately linear surface enrichment, reproduces the observed surface-subsurface contrast, and identifies the microlayer as a dynamic reservoir shaping residence times, exposure, and air-sea exchange of fiberlike pollutants.

Computing spectral functions in large, nonperiodic super-moiré systems remains an open problem due to the exceptionally large system size that must be considered. Here, we establish a tensor network methodology that allows computing momentum-resolved spectral functions of noninteracting and interacting super-moiré systems at an atomistic level. Our methodology relies on encoding an exponentially large tight-binding problem as an auxiliary quantum many-body problem, solved with a many-body kernel polynomial tensor network algorithm combined with a quantum Fourier transform tensor network. We demonstrate the method for one- and two-dimensional super-moiré systems, including super-moiré with nonuniform strain, interactions treated at the mean-field level, and quasicrystalline super-moiré patterns. Furthermore, we demonstrate that our methodology allows us to compute momentum-resolved spectral functions restricted to selected regions of a super-moiré, enabling direct imaging of position-dependent electronic structure and minigaps in super-moiré systems with nonuniform strain. Our results establish a powerful methodology to compute momentum-resolved spectral functions in exceptionally large super-moiré systems, providing a tool to directly model quantum twisting microscope experiments in twisted van der Waals heterostructures.

We investigate recurrent neural networks with asymmetric interactions and demonstrate that the inclusion of self-couplings or sparse excitatory intermodule connections leads to the emergence of a densely connected manifold of dynamically accessible stable configurations. This representation manifold is exponentially large in system size and is reachable through simple local dynamics, despite constituting a subdominant subset of the global configuration space. We further show that learning can be implemented directly on this structure via a fully local, gradient-free mechanism that selectively stabilizes a single task-relevant network configuration. Unlike error-driven or contrastive learning schemes, this approach does not require explicit comparisons between network states obtained with and without output supervision. Instead, transient supervisory signals bias the dynamics toward the representation manifold, after which local plasticity consolidates the attained configuration, effectively shaping the latent representation space. Numerical evaluations on standard image classification benchmarks indicate performance comparable to that of multilayer perceptrons trained using backpropagation. More generally, these results suggest that the dynamical accessibility of fixed points and the stabilization of internal network dynamics constitute viable alternative principles for learning in recurrent systems, with conceptual links to statistical physics and potential implications for biologically motivated and neuromorphic computing architectures.

Cellular decision-making based on information received from the external environment is frequently initiated by transmembrane receptors. These receptors are known to propagate such information by triggering a series of irreversible, energy-consuming reactions. While this active mechanism ensures switchlike responses, here we show how spontaneous self-assembly of native three-dimensional subunits on a two-dimensional substrate can similarly act as a tunable and robust switch for detecting receptors at physiological concentrations. This mechanism is much more sensitive than other passive mechanisms for receptor detection. We derive analytical expressions for the critical receptor density driving stable subunit assembly, in close agreement with stochastic reaction-diffusion simulations. The theory provides testable predictions for how lipids, subunits, and receptors each can control decision boundaries and magnitude of response.

Using a linear hydrodynamic model, we theoretically investigate the interaction between penetrating electron beams and sub-5-nm metallic spherical nanoparticles. We derive an analytical expression of the electron energy loss probability that includes nonlocal effects in the response of the confined electron gas. Our study focuses on the longitudinal plasmon excitations—also known as confined bulk plasmons (CBPs)—which are inaccessible within local dielectric frameworks, and it shows that their excitation is highly sensitive to the impact parameter, the kinetic energy of the incident electron, and the nanoparticle size. In contrast to local approaches, our model predicts the emergence of several peaks associated with CBPs above the plasma frequency and a blueshift of their spectral envelope as the nanoparticle size decreases and the impact parameter of the electron beam increases from its center toward the surface. Moreover, it predicts a threshold impact parameter, corresponding to the minimum length of the electron's trajectory inside the nanoparticle required for efficient excitation of specific CBP modes. By exploiting a multipolar description of the CBPs, we identify the underlying selection rules governing their excitation by electron beams and correlate the observed blueshift at larger impact parameters with the preferential excitation of higher-order CBPs. The size-dependent dispersion of the CBPs further enhances this impact-parameter-dependent blueshift and also explains the decrease in the impact parameter threshold for smaller nanoparticles. Compared with the local response approximation, the hydrodynamic model captures these trends without ad hoc momentum cutoffs and with rapid multipolar convergence. Applied to sodium nanoparticles as a canonical free-electron system, the theory reproduces qualitative behaviors observed in experiments and ab initio calculations, providing practical guidance for the design and interpretation of CBP-sensitive electron energy loss spectroscopy measurements.

We introduce a Hopfield-type associative memory in which effective connectivity is multiplicatively modulated by astrocytic gains evolving under an entropy-regularized replicator equation. The coupled neuron-astrocyte dynamics admit a Lyapunov function, ensuring global convergence. At fixed points, astrocytic gains implement a softmax-normalized allocation over pattern similarity scores, yielding a mechanistic realization of self-attention as emergent routing on the gain simplex. In regimes of high memory load and interference, the model significantly improves retrieval accuracy relative to classical Hopfield dynamics and recent neuron-astrocyte baselines. These results establish a dynamical systems framework linking glial modulation, competitive resource allocation, and attentionlike computation.

Circular dichroism in the angular distribution (CDAD) is the effect that the angular intensity distribution of photoemitted electrons depends on the handedness of the incident circularly polarized light. The origin of CDAD can be manifold, including intrinsic properties of the system under study, such as chirality, spin-orbit interaction, or quantum-geometrical properties, but CDAD can also originate from final-state effects influenced by the experimental geometry. For example, CDAD has been reported for achiral organic molecules at the interface to metallic substrates. For this latter case, we investigate two prototypical $\pi$-conjugated molecules, namely, tetracene and pentacene, whose frontier orbitals have a similar shape but exhibit distinctly different symmetries. By comparing experimental CDAD momentum maps with simulations within time-dependent density functional theory, we show how the final state of the photoelectron must be regarded as the source of the CDAD in such otherwise achiral and quantum-geometrically trivial systems. We gain additional insight into the mechanism by employing a simple scattering model for the final state, which allows us to decompose the CDAD signal into partial wave contributions.

We present a quantum information-inspired ansatz for the variational quantum eigensolver and demonstrate its efficacy in calculating ground-state energies of atomic systems. Instead of adopting a heuristic approach, we start with an approximate multiqubit target state and utilize two quantum information-theoretic quantities, i.e., von Neumann entropy and quantum mutual information, to construct our ansatz. The quantum information encoded in the target state helps us design unique blocks and identify qubit pairs that exhibit maximal quantum correlations within the multi-qubit system, thereby enabling us to deterministically place two-qubit entanglers in the suitably constructed parametrized quantum circuit. We find that our approach offers the advantage of reduced circuit depth compared to the physics-inspired unitary coupled-cluster (UCC) ansatz, while yielding accurate results. To test the performance of our ansatz, we apply it to compute ground-state energies of atomic systems in the relativistic regime. Our results show that for up to 12-qubits (12-spinorbitals) noiseless calculations, our ansatz yields energies within chemical accuracy ($\sim 1.6$ mhartree) relative to the complete active space configuration interaction values, while utilizing only a few blocks that contain in the best case $98\%$ fewer two-qubit gates when compared with the UCC ansatz in the singles and doubles approximation.

Open quantum systems far from thermal equilibrium can exhibit remarkable physical phenomena including topological properties without a direct equilibrium counterpart. Along these lines, in periodically driven-dissipative systems within the effective non-Hermitian (NH) Hamiltonian approximation spectral winding numbers have been linked to intriguing nonreciprocal transport properties. Here, going beyond an NH Hamiltonian description, we introduce and study a microscopic lattice model of a driven open quantum system described by a Markovian quantum master equation, which exhibits the mentioned spectral winding within an NH approximation. By encompassing quantum jump processes in the topological analysis, we uncover a distinct jump-induced topological phase, which qualitatively corresponds to the richer nonreciprocal transport properties of the fully quantum model. In addition, we find that the NH skin effect, i.e., the accumulation of a macroscopic number of eigenstates at one end of the system, is already visible in the transient dynamics even for systems with periodic boundary conditions. Our results exemplify the subtle correspondence between NH topological properties and physical manifestations of Liouvillian topological properties in open quantum systems, thus providing a theoretical framework toward understanding unidirectional transport in quantum dissipative Floquet dynamics.

Asymptotically close to critical end points of first-order transitions, maxima in thermodynamic quantities occur along a line called the Widom line, a concept first introduced in classical fluids. This concept has been extended to strongly correlated electronic fluids in the context of the Mott transition. Namely, upon increasing interaction strength in the Hubbard model at half filling, one finds a first-order Mott metal-insulator transition with a critical end point at high temperature, above which several crossover lines are observable. Using the dynamical cluster approximation for the triangular-lattice Hubbard model, we compute the Frenkel line, a concept borrowed from classical fluids, which is used to define a sharp crossover between the pseudogap and the correlated Fermi liquid. The Frenkel line in the electron fluid is defined by the appearance of backscattering upon entering the pseudogap. The signature of backscattering is the existence of a negative value in the time-domain optical conductivity. The Frenkel line extends to high temperatures.

Laser-plasma accelerators offer a compact means of producing high-energy electron beams, but their performance is fundamentally limited by dephasing between the accelerated electrons and the plasma wave. To overcome this limitation, we investigate the combination of plasma density tapering and optical guiding to extend the effective acceleration length. Using a Joule-class femtosecond laser coupled into an optical-field-ionized plasma waveguide with a controlled density gradient, we experimentally achieve electron beam energies exceeding 1.6 GeV, a 40% increase compared to the constant-density case. Particle-in-cell simulations reproduce the main experimental features and reveal the central roles of delayed injection, nonlinear laser evolution, and self-focusing in enhancing energy gain.

The excitonic insulator candidate ${\mathrm{Ta}}_2{\mathrm{Pd}}_3{\mathrm{Te}}_5$ has emerged as a promising platform to explore macroscopic quantum condensation and its collective excitations. Using polarization-resolved Raman spectroscopy, we identify a symmetry-selective electronic mode that softens and undergoes linewidth renormalization across the characteristic temperature $T^{*}\sim 100\mathrm{K}$. This mode, most prominent in the $B_{3g}$ channel, is assigned to vestigial collective excitations associated with excitonic correlations, as evidenced by its order-parameter-like temperature evolution and symmetry dependence. In parallel, a damped Drude-like response exhibits a suppressed scattering rate and maximum intensity at $T^{*}$, while phonon intensities display symmetry-dependent anomalies, signaling anisotropic band reconstructions and the spontaneous opening of a collective condensation gap. These observations provide compelling spectroscopic evidence for collective amplitude excitations in ${\mathrm{Ta}}_2{\mathrm{Pd}}_3{\mathrm{Te}}_5$, establishing this material as a bulk excitonic insulator.

Hermiticity has long been a cornerstone of quantum theory, ensuring the conservation of probability and real energy spectra. Recent advances in non-Hermitian physics have revealed that this requirement can be relaxed, giving rise to richer dynamics where the Hilbert space is endowed with a time-dependent inner product defined by a metric operator $\rho \left(t\right)$. This dynamical metric fundamentally reshapes quantum predictions, yet direct access to it on physical devices has remained elusive. Here, we bridge this gap by presenting general, platform-agnostic operator-dilation schemes that embed the metric formalism into closed Hermitian systems. Our proposed framework converts any time-dependent non-Hermitian Hamiltonian into a larger unitary evolution, bringing the theory from a purely mathematical curiosity into the experimentally testable realm. This permits the first controlled study of $\mathcal{PT}$-symmetric and symmetry-broken regimes at arbitrarily long timescales. We demonstrate the power of this approach on a digital quantum simulator, showing that cutting-edge questions about non-Hermiticity are already within reach of today's noisy intermediate-scale quantum processors. Our results provide the first direct evidence of a dynamically evolving metric in a single qubit, establishing a new paradigm for simulating non-Hermitian quantum mechanics and opening the door to its realization in the laboratory.

Information-processing algorithms inspired by survival strategies of slime molds have been intensively studied to realize combinatorial optimization machines with high computational efficiency and low power consumption. However, previously proposed mathematical models are not suitable for physical implementation as they involved constraints due to the volume-conservation law of slime molds, multiple conditional branches, and numerous complex functions such as sigmoid functions. Here, we propose a mathematical model, the Amoeba TSP (traveling salesman problem) recurrence formula model, which simplifies the information-processing procedure and enables physical implementation without sacrificing optimization performance. Specifically, we demonstrate using numerical simulations that (1) the conservation-law constraints assumed in previous models can be removed, (2) several sigmoid functions can be eliminated, and (3) Gaussian-distribution random numbers that can be generated from thermal fluctuations can be employed as random fluctuations. These improvements broaden the range of candidate materials and phenomena for physical implementation and reduce the number of device components required. Furthermore, we discover that our mathematical model integrating these modifications is mathematically equivalent to a recurrent neural network with fixed weights that exploits nonlinear dynamics. Remarkably, despite its significant simplification, the proposed model exhibits superior solution-search performance compared with the previous models in terms of solution quality, convergence speed, and scalability. Our results open a practical pathway toward realization of highly efficient and high-performance combinatorial optimization machines based on non-von Neumann-type architectures and provide insights into bioinspired information processing by uncovering a connection between the computational principles of slime molds and recurrent neural networks.

We propose quantum-selected configuration interaction, a class of hybrid quantum-classical algorithms for calculating the ground- and excited-state energies of many-electron Hamiltonians on noisy quantum devices. Suppose that an approximate ground state can be prepared on a quantum computer either by variational quantum eigensolver or by some other method. Then, by sampling the state in the computational basis, which is hard for classical computation in general, one can identify the electron configurations that are important for reproducing the ground state. The Hamiltonian in the subspace spanned by those important configurations is diagonalized on classical computers to output the ground-state energy and the corresponding eigenvector. The excited-state energies can be obtained similarly. The result is robust against statistical and physical errors because the noisy quantum devices are used only to define the subspace, and the resulting ground-state energy strictly satisfies the variational principle even in the presence of such errors. The expectation values of various other operators can also be estimated for obtained eigenstates with no additional quantum cost, since the explicit eigenvectors in the subspaces are known. We verified our proposal by numerical simulations and demonstrated it on a quantum device for an eight-qubit molecular Hamiltonian. The proposed algorithms are potentially feasible to tackle some challenging molecules by exploiting quantum devices with several tens of qubits, assisted by high-performance classical computing resources for diagonalization.