The depth of quantum circuits is a critical factor when running them on state-of-the-art quantum devices owing to their limited coherence times. Reducing circuit depth decreases noise in near-term quantum computations and reduces overall computation time. This also benefits fault-tolerant quantum computations. Here, we show how to reduce the depth of quantum subroutines that typically scale linearly with the number of qubits, such as quantum fan-out and long-range CNOT gates, to a constant depth using mid-circuit measurements and feedforward operations, while only requiring a 1D line topology. We compare our protocols with existing ones to highlight their advantages. Additionally, we verify the feasibility by implementing the measurement-based quantum fan-out gate and long-range CNOT gate on real quantum hardware, demonstrating significant improvements over their unitary implementations.

A continuous one-dimensional map with period three includes all periods. This raises the following question: Can we obtain any types of periodic orbits solely by learning three data points? In this paper, we report the answer to be yes. Considering a random neural network in its thermodynamic limit, we first show that almost all learned periods are unstable, and each network has its own characteristic attractors (which can even be untrained ones). The latently acquired dynamics, which are unstable within the trained network, serve as a foundation for the diversity of characteristic attractors and may even lead to the emergence of attractors of all periods after learning. When the neural network interpolation is quadratic, a universal post-learning bifurcation scenario appears, which is consistent with a topological conjugacy between the trained network and the classical logistic map. In addition to universality, we explore specific properties of certain networks, including the singular behavior of the scale of weight at the infinite limit, the finite-size effects, and the symmetry in learning period three.

We demonstrate through high-fidelity particle-in-cell (PIC) simulations a simple approach for efficiently generating $20+$ GeV electron beams with the necessary charge, energy spread, and emittance for use as an injector in a future linear collider or a next generation XFEL. A high quality injected bunch is generated by self-focusing an unmatched electron driver in a nonlinear plasma wakefield. Over pump depletion distances, the drive beam dynamics and self-loading effects lead to high energy, low-energy spread output beams. For plasma densities of ${10}^{18}\mathrm{c}{\mathrm{m}}^{-3}$, PIC simulation results indicate that self-injected beams with $0.52\mathrm{n}\mathrm{C}$ charge can be accelerated to 20 GeV with projected core energy spreads of $\lesssim 1\%$, normalized slice emittances of $110\mathrm{n}\mathrm{m}$, peak normalized brightness of $\gtrsim {10}^{19}\mathrm{A}/{\mathrm{m}}^2/{\mathrm{rad}}^2$, and transfer efficiencies of $\gtrsim 44\%$.

We propose an optomechanical system utilizing luminescence-induced optical forces (LIOFs). Anisotropic dielectric structures enhance the recoil force from the luminescence. The optomechanical resonator consists of a composite film with a dielectric membrane, luminescent nanofilm, and a metallic substrate. The LIOF causes a mechanical frequency shift in the oscillator known as the optical spring effect. These results link the quantum properties of luminescent nanomaterials with those of other quantum-mechanical systems with vastly different frequency regimes via induced vibrational modes.

$$ We present a general method to compute canonical averages for physical models sampled via quantum or classical quadratic unconstrained binary optimization (QUBO). First, we introduce a histogram reweighting scheme applicable to QUBO-based sampling constrained to specific intervals of an order parameter, e.g., physical energy. Next, we demonstrate that the scheme can accurately recover the density of states, which in turn allows for calculating expectation values in the conjugate ensemble, e.g., at a fixed temperature. The method can thus be used to advance the state-of-the-art characterization of physical systems that admit a QUBO-based representation and that are otherwise intractable with real-space sampling methods. A case in point are space-filling melts of lattice ring polymers, recently mapped in QUBO form, for which our method reveals that the ring catenation probability is nonmonotonic with the bending rigidity.

How typical elements that shape organisms, such as protein secondary structures, have evolved, or how evolutionarily susceptible/resistant they are to environmental changes, are significant issues in evolutionary biology, structural biology, and biophysics. According to Darwinian evolution, natural selection and genetic mutations are the primary drivers of biological evolution. However, the concept of “robustness of the phenotype to environmental perturbations across successive generations,” which seems crucial from the perspective of natural selection, has not been formalized or analyzed. In this study, through Bayesian learning and statistical mechanics we formalize the stability of the free energy in the space of amino acid sequences that can design particular protein structure against perturbations of the chemical potential of water surrounding a protein as such robustness. This evolutionary stability is defined as a decreasing function of a quantity analogous to the susceptibility in the statistical mechanics of magnetic bodies specific to the amino acid sequence of a protein. Consequently, in a two-dimensional square lattice protein model composed of 36 residues, we found that as we increase the stability of the free energy against perturbations in environmental conditions, the structural space shows a steep steplike reduction. Furthermore, lattice protein structures with higher stability against perturbations in environmental conditions tend to have a higher proportion of $\alpha$ helices and a lower proportion of $\beta$ sheets. This result is qualitatively confirmed by comparing the histograms of the percentage of secondary structures of evolutionarily robust proteins and randomly selected proteins through an empirical validation using a protein database. The result shows that protein structures rich in $\alpha$ helices are more robust to environmental perturbations through successive generations than those rich in $\beta$ sheets.

Electron-hole pairs in semiconductors are essential for solar cells and fast electronic circuitry, but the competition between carrier transport and relaxation into heat limits the efficiency and speed. Here we use ultrafast electron diffraction with terahertz pulse compression to measure the electron-phonon decay rate in single-crystal silicon as a function of laser excitation strength. We find that the excited electrons relax slower into phonons for higher carrier densities. The electron-phonon scattering rate changes in a nonlinear way from 400 fs at $\sim 2\times {10}^{20}/\mathrm{c}{\mathrm{m}}^3$ to 1.2 ps at $\sim 4\times {10}^{20}/\mathrm{c}{\mathrm{m}}^3$. These results indicate that a hot electron gas quenches the scattering into phonons in a temperature-dependent way. Ultrafast electronic circuitry of silicon should therefore work faster and provide higher bandwidths at lower carrier densities.

Density functional theory (DFT) offers a desirable balance between quantitative accuracy and computational efficiency in practical many-electron calculations. Its central component, the exchange-correlation energy functional, has been approximated with increasing levels of complexity ranging from strictly local approximations to nonlocal and orbital dependent expressions with many tuned parameters. In this paper, we formulate a general way of rewriting complex density functionals using deep neural networks in a way that allows for simplified computation of Kohn-Sham potentials as well as higher functional derivatives through automatic differentiation, enabling access to highly nonlinear response functions and forces. These goals are achieved by using a recently developed class of robust neural network models capable of modeling functionals, as opposed to functions, with explicitly enforced spatial symmetries. Functionals treated in this way are then called global density approximations and can be seamlessly integrated with existing DFT workflows. Tests are performed for a dataset featuring a large variety of molecular structures and popular meta–generalized gradient approximation density functionals, where we successfully eliminate orbital dependencies coming from the kinetic energy density, and discover a high degree of transferability to a variety of physical systems. The presented framework is general and could be extended to more complex orbital and energy dependent functionals as well as refined with specialized datasets.

We explore the behavior of the Hall response of a Bose-Hubbard triangular ladder in a magnetic field as a function of the repulsive on-site atomic interactions. We consider a wide range of interaction strengths, from the weakly interacting limit to the hard-core regime. This is realized by computing the Hall polarization following the quench of a weak linear potential, which induces the flow of a current through the system, using time-dependent matrix product state numerical simulations. We complement our understanding in the regime of small magnetic fields by analytical calculations of the equilibrium value of the Hall polarization for noninteracting bosonic atoms and under a mean-field assumption. The Bose-Hubbard triangular flux ladder exhibits a rich phase diagram, containing Meissner, vortex, and biased-chiral, superfluid phases. We show that the Hall response can be employed to fingerprint the various chiral states, the frustration effects occurring in the limit of strong interactions, and the phase boundaries of the equilibrium phase diagram.

Magnon condensation, which emerges in pumped bosonic systems at room temperature, continues to garner great interest for its long-lived coherence. While traditionally formulated in terms of Bose-Einstein condensation, which typically occurs at ultralow temperatures, it could potentially also be explained by Fröhlich condensation, a hypothesis of Bose-Einstein-like condensation in living systems at ambient temperatures. This prompts general questions relating to fundamental differences between coherence phenomena in open and isolated quantum systems. To that end, we introduce a simple model of bosonic condensation in an open quantum system (OQS) formulation, wherein bosons dissipatively interact with an oscillator (phonon) bath. Our derived equations of motion for expected boson occupations turn out to be similar in form to the rate equations governing Fröhlich condensation. Provided that specific system parameters result in correlations that amplify or diminish the condensation effects, we thereby posit that our treatment offers a better description of high-temperature condensation compared to traditional formulations obtained using equilibrium thermodynamics. By comparing our OQS derivation with the original uncorrelated and previous semiclassical rate equations, we furthermore highlight how both classical anticorrelations and quantum correlations alter the bosonic occupation distribution.

We report a novel configuration of bulk ${\chi }^{\left(2\right)}$ optical parametric oscillator (OPO) capable of delivering coherent broadband phase-locked spectrum when driven by a continuous-wave (cw) pump laser. By deploying an electrooptic modulator (EOM) internal to a degenerate cw OPO based on MgO:sPPLT or MgO:PPLN and implementing intracavity dispersion control, we show that output spectra extending over 9 nm (119 nm) with well-defined phase coherence can be generated. Pumping the cw OPO at 532 nm (1550 nm) for degenerate operation in the normal (anomalous) dispersion regime at 1064 nm (3100 nm), and using the coupled-wave equations to simulate the OPO in the presence of intracavity dispersion compensation, we demonstrate that this device offers a new alternative for ${\chi }^{\left(2\right)}$-based frequency comb generation. We also show that in the time domain the output of such a device corresponds to femtosecond quadratic solitons in both dispersion regimes. The described concept is generic, paving the way for the realization of a new class of coherent broadband frequency comb sources in different spectral regions based on ${\chi }^{\left(2\right)}$ OPOs pumped by cw lasers.

Unexpected and often irreversible shifts in the state or the dynamics of a complex system often accumulate in extreme events with likely disastrous impact on the system and its environment. Detection, understanding, and possible prediction of such critical transitions are thus of paramount importance across a variety of scientific fields. The rather modest improvement achieved so far may be due previous research mostly concentrating on either particular subsystems, considered to be of vital importance for the generating mechanism of a critical transition, or on the system as a whole. These approaches only rarely take into account the intricate, time-dependent interrelatedness of subsystems that can essentially determine emerging behaviors underlying critical transitions. We uncover subsystems, network vertices, and the interrelatedness of certain subsystems, network edges, as tipping elements in a networked dynamical system, forming a time-evolving tipping subnetwork. We demonstrate the existence of tipping subnetworks in excitable complex networks and in human epileptic brains. These systems can repeatedly undergo critical transitions that result in extreme events. Our findings reveal that tipping subnetworks encapsulate key properties of mechanisms involved in critical transitions.

Optical networks are the leading platform for the transfer of information due to their low loss and ability to scale to many information channels using optical frequency modes. To fully leverage the quantum properties of light in this platform, it is desired to manipulate higher-dimensional superpositions by orchestrating linear, beamsplitter-type interactions between several channels simultaneously. We propose a method of achieving simultaneous, all-to-all coupling between $N$ optical frequency modes via $N$-way Bragg-scattering four-wave mixing. By exploiting the frequency degree of freedom, additional modes can be multiplexed in an interaction medium of fixed volume and loss while avoiding the introduction of excess noise. We generalize the theory of the frequency-encoded two-mode interaction to $N$ modes under this four-wave-mixing approach, finding that arbitrary unitary transformations are possible through successive four-wave-mixing pump configurations. We experimentally verify the quantum nature of this scheme by demonstrating three-way multiphoton interference. Two input photons are shared among three frequency modes and display interference differing from that of two classical (coherent-state) inputs. These results show the potential of our approach for the scalability of photonic quantum information processing to general $N$-mode systems in the frequency domain.

Point processes have broad applications in science and engineering. In physics, their use ranges from quantum chaos to statistical mechanics of many-particle systems. We introduce a spatial form factor (SFF) for the characterization of spatial patterns associated with point processes. Specifically, the SFF is defined in terms of the averaged even Fourier transform of the distance between any pair of points. We focus on homogeneous Poisson point processes and derive the explicit expression for the SFF in $d$-spatial dimensions. The SFF can then be found in terms of the even Fourier transform of the probability distribution for the distance between two independent and uniformly distributed random points on a $d$-dimensional ball, arising in the ball line picking problem. The relation between the SFF and the set of $n$-order spacing distributions is further established. The SFF is analyzed in detail for $d=1,2,3$ and in the infinite-dimensional case, as well as for the $d$-dimensional Coulomb gas, as an interacting point process. As a physical application, we describe the spontaneous vortex formation during Bose-Einstein condensation in finite time recently studied in ultracold atom experiments and use the SFF to reveal the stochastic geometry of the resulting vortex patterns. In closing, we also introduce a generalization of the SFF applicable to arbitrary sets in a metric space.

We report a comprehensive study of spin dynamics in the ${\left({\mathrm{Mo}}_{2/3}{\mathrm{Tb}}_{1/3}\right)}_2\mathrm{AlC} i$-MAX compound using ac susceptibility measurements across a range of magnetic fields. Unique behaviors were observed, including spin dynamics in the kHz range between ${\mu }_{0}H\approx 0.2T-6T$, indicating a nontrivial superparamagnetic state, suggesting that the compound acts as a transitional system within the $i$-MAX family, bridging stable spin-dynamic materials and fluctuation-dominated ones. Field- and frequency-dependent magnetic phase transitions, coupled with relaxation behaviors, reveal complex interactions between spin density waves and superparamagnetic components. These findings, corroborated by $\mu \mathrm{SR}$ studies, deepen our understanding of magnetic phase diagrams and field-induced phenomena in $i$-MAX systems, laying the groundwork for further exploration of their unique properties and applications.

We investigate the emission characteristics of a measurement-driven quantum emitter in a continuously monitored optical environment. The quantum emitter is stimulated by observing the Pauli spin along its transition dipole that maximally noncommutes with the Hamiltonian of the emitter. It also exchanges energy resonantly with the optical environment, observable as quantum jumps corresponding to the absorption or emission of a photon and the null events where the quantum emitter did not make a jump. We characterize the finite-time statistics of quantum jumps and estimate their covariance and precision using the large deviation principle. While the statistics of absorption and emission events are generically sub-Poissonian with an improved precision by at most a factor of two compared to Poissonian jumps, our analysis also reveals a spin-measurement-induced transition from super-Poissonian to sub-Poissonian in their sum. We conclude by describing generalized quantum measurement strategies using mode-entangled optical beams to access the predicted counting statistics in experiments, with implications extending to optimal quantum clocks.

In communication complexity-like problems, previous studies have shown either an exponential quantum advantage or an unbounded quantum advantage with an exponentially large input set $\mathrm{\Theta }\left(2^n\right)$ bit with respect to classical communication $\mathrm{\Theta }\left(n\right)$ bit. In the former, the quantum and classical separation grows exponentially in input while the latter's quantum communication resource is a constant. Remarkably, it was still open whether an unbounded quantum advantage exists while the inputs do not scale exponentially. Here we answer this question affirmatively using an input size of optimal order. Considering two variants as tasks: (1) distributed computation of relation and (2) relation reconstruction, we study the one-way zero-error communication complexity of a relation induced by a distributed clique labeling problem for orthogonality graphs. While we prove no quantum advantage in the first task, we show an unbounded quantum advantage in relation reconstruction without public coins. Specifically, for a class of graphs with order $m$, the quantum complexity is $\mathrm{\Theta }\left(1\right)$ while the classical complexity is $\mathrm{\Theta }\left({log}_{2}m\right)$. Remarkably, the input size is $\mathrm{\Theta }\left({log}_{2}m\right)$ bit and the order of its scaling with respect to classical communication is minimal. This is exponentially better compared to previous works. Additionally, we prove a lower bound (linear in the number of maximum cliques) on the amount of classical public coin necessary to overcome the separation in the scenario of restricted communication and connect this to the existence of orthogonal arrays. Finally, we highlight some applications of this task to semi-device-independent dimension witnessing as well as to the detection of mutually unbiased bases.