We consider the quantum memory assisted quantum state verification task, where an adversary prepares independent multipartite entangled states and sends them to the local verifiers, who then store several copies in the quantum memory and measure them collectively to make a decision. We establish an exact analytic formula for optimizing two-copy state verification, where the verifiers store two copies, and give a globally optimal two-copy strategy for multiqubit graph states involving only Bell measurements. When the verifiers can store arbitrarily many copies, we present a dimension expansion technique that designs efficient verification strategies for this case, showcasing its application to efficiently verifying Greenberger-Horne-Zeilinger (GHZ)-like states. These strategies become increasingly advantageous with growing memory resources, ultimately approaching the theoretical limit of efficiency. Our findings demonstrate that quantum memories enhance state verification efficiency, shedding light on error-resistant strategies and practical applications of large-scale quantum memory assisted verification.

Fano resonances originate from the interaction of a discrete state with a continuum of propagation modes. They have been observed in various quantum and classical systems. Here, we provide experimental observation of the Fano resonance in two coupled micromechanical oscillator systems. The system consists of a pair of electrostatically coupled silicon micromechanical resonators. The coupling strength between the two resonators and their stiffness can be electrically tuned. The damping of one resonator can be regulated by an external proportional feedback control system. We have achieved the switching from the Lorentzian line shape to the Fano line shape, indicating that the quality factor of the Fano resonance is more than three times that of the Lorentzian resonance. This work supplies a flexible platform for exploring the physics of Fano resonances in a mechanical system and paves the way for developing a new generation of silicon resonant sensors with superior resolution.

We study the classical hardness of learning the output distribution from instantaneous quantum polynomial-time circuits with a varying density of two-qubit gates. We first investigate the complexity phases relevant to simulating the output distribution. In addition to a known parameter regime for anticoncentration, where the classical simulation is believed to be infeasible, we identify a novel parameter condition such that the classically simulability of the circuit can be proven rigorously. Next, we explore the classical learnability of the output distribution. We prove that a circuit instance whose output distribution is not classically learnable exists, assuming the hardness of learning parity with noise. We numerically study this phenomenon deeply using a classical energy-based model, where we find that the classical model fails to learn the output distribution even when the density of two-qubit gates is much smaller than that for the anticoncentration. We argue that this is mainly due to the fact that an accurate classical description of the output distribution requires exponentially large amount of resources.

Topological insulators are quantum materials involving time-reversal protected surface states making them appealing candidates for the design of the next generation of highly efficient spintronic devices. The very recent observation of large transient spin-charge conversion and subsequent powerful THz emission from $\mathrm{Co}\text{/}{\mathrm{Bi}}_{1-x}{\mathrm{Sb}}_x$ bilayers clearly demonstrates such potentiality and feasibility for the near future. Among the exotic properties appearing in and at the surface of such quantum materials, spin-momentum locking and Rashba-Edelstein effects remain as key ingredients to effectively convert the spin degree of freedom into a charge or a voltage signal. In this work, we extend our analyses to the quantification of orbital momentum-locking and related orbital charge conversion effects in ${\mathrm{Bi}}_{.85}{\mathrm{Sb}}_{.15}$ via orbital Rashba-Edelstein effects. In that sense, we will provide some clear theoretical and numerical insights implemented by multiorbital and slab tight-binding methods to clarify our recent experimental results obtained by THz spectroscopy in the time domain.

First-principles simulations of many-fermion systems are commonly limited by the computational requirements of processing large data objects. As a remedy, we propose the use of low-rank approximations of three-body interactions, which are the dominant such limitation in nuclear physics. We introduce a randomized decomposition technique to handle the excessively large matrix dimensions and study the sensitivity of low-rank properties to interaction details. The developed low-rank three-nucleon interactions are benchmarked in ab initio simulations of few- and many-body systems. Exploiting low-rank properties provides a promising route to extend the microscopic description of atomic nuclei to large systems where storage requirements exceed the computational capacities of the most advanced high-performance computing facilities.

Many biological molecular motors and machines are driven by chemical reactions that occur in specific catalytic sites. We study whether the arrival of molecules to such an active site can be accelerated by the presence of a nearby inactive site. Our approach is based on comparing the steady-state current in simple models to reference models without an inactive site. We identify two parameter regimes in which the reaction is accelerated. We then find the transition rates that maximize this acceleration and use them to determine the underlying mechanisms in each region. In the first regime, the inactive site stores a molecule to release it following a reaction, when the neighboring catalytic site is empty. In the second regime, the inactive site releases a molecule when the catalytic site is full, to impede the molecules from leaving the active site before they react. For the storage mechanism, which is more likely to be biologically relevant, the acceleration can reach up to 15%, depending on parameters.

Quantum mechanical control of the position of a particle by using a trapping potential well is an important problem for the manipulation of a quantum particle. We study the probability of successful conveyance of the particle trapped in a potential well for a given length within a given fixed time, i.e., survival probability after the motion. For the actual motion of conveyance, we need to accelerate the particle to move and then decelerate it to stop at the destination. Acceleration and deceleration cause a dropoff of a particle from the trapping potential well. The relaxation of the survival probability in a process with a constant acceleration rate is studied in detail. First, the process is studied as the relaxation of the survival probability of the trapped particle by direct numerical calculations. The survival probability obeys an exponential decay in a long time, which is analyzed from a viewpoint of eigenvalue problem. The value of survival probability is also estimated by the Wentzel-Kramers-Brillouin method with connection formulas using the Airy function and the Weber function. The value is further estimated by a method of the resonance states. We emphasize the fact that an important source of dropoff comes from a nonanalytic change of velocity at the starting point. When the rested particle begins to move, the ground state of the rest frame is redistributed to eigenstates of the moving frame, and then each eigenstate of the moving frame evolves in time. The dephasing of wave functions of the distributed populations reduces the probability of successful conveyance. In general, a smooth start gives a small initial disturbance but it requires a large acceleration during the process to reach the destination in the fixed time which causes a larger dropoff in the process. Considering these conflicting facts, we study the survival probability in concrete conveyance schemes, i.e., protocols with (1) a sudden change of velocity to a constant velocity, (2) a sudden change of acceleration rate to a constant acceleration, and (3) a smooth change of acceleration by studying the real-time change of populations of the adiabatic (instantaneous) eigenstates. We observe the time evolution of the trapped probability and the population distribution during the conveyance process. In cases that the potential well has several bound states, we propose a method to select the particle trapped at the ground state by making use of the difference of survival probabilities of bound states.

Observational entropy—a quantity that unifies Boltzmann's entropy, Gibbs' entropy, von Neumann's macroscopic entropy, and the diagonal entropy—was recently argued to play a key role in a modern formulation of statistical mechanics. Here, relying on algebraic techniques taken from Petz's theory of statistical sufficiency and on a Lévy-type concentration bound, we prove rigorous theorems showing how the observational entropy of a system undergoing a unitary evolution chosen at random tends to increase with overwhelming probability and to reach its maximum very quickly. More precisely, we show that for any observation that is sufficiently coarse with respect to the size of the system, regardless of the initial state of the system (be it pure or mixed), random evolution renders its state practically indistinguishable from the uniform (i.e., maximally mixed) distribution with a probability approaching 1 as the size of the system grows. The same conclusion holds not only for random evolutions sampled according to the unitarily invariant Haar distribution but also for approximate 2-designs, which are thought to provide a more physically and computationally reasonable model of random evolutions.

Adversarial robustness and generalization are both crucial properties of reliable machine learning models. In this paper, we study these properties in the context of quantum machine learning based on Lipschitz bounds. We derive parameter-dependent Lipschitz bounds for quantum models with trainable encoding, showing that the norm of the data encoding has a crucial impact on the robustness against data perturbations. Further, we derive a bound on the generalization error which explicitly involves the parameters of the data encoding. Based on these theoretical results, we propose a practical strategy for training robust and generalizable quantum models by regularizing the Lipschitz bound in the cost. Moreover, we show that, for fixed and nontrainable encodings, as those frequently employed in quantum machine learning, the Lipschitz bound cannot be influenced by tuning the parameters. Thus trainable encodings are crucial for systematically adapting robustness and generalization during training. The practical implications of our theoretical findings are illustrated with numerical results.

Local quantum annealing (LQA), an iterative algorithm, is designed to solve combinatorial optimization problems. It draws inspiration from QA, which utilizes adiabatic time evolution to determine the global minimum of a given objective function. In the original LQA, the classical Hamiltonian is minimized via gradient descent. The performance of LQA depends on the choice of the parameters. Owing to the nonconvex nature of the original cost function, LQA often becomes trapped in local minima, limiting its effectiveness. To address this challenge, we combine LQA with a deep unfolding scheme, which enables us to tune the parameters from the training data via back-propagation. As a demonstration, we apply the deep unfolded LQA to the Sherrington-Kirkpatrick model, which is a fundamental model in statistical physics. Our findings exhibit that deep unfolded LQA outperforms the original LQA, exhibiting remarkable convergence speed and performance improvement. As the trained parameters can be generalized to unknown instances and different system sizes, our results have significant practical implications and provide valuable insights for real-world applications.

Polytypes of tetralayer graphene (TLG: Bernal, rhombohedral, and mixed stacking) are crystalline structures with different symmetries. Among those, mixed-stacking tetralayers lack inversion symmetry, which allows for intrinsic spontaneous out-of-plane electrical polarization, inverted in the mirror-image pair, ABCB and ABAC stackings. Here, we compare the intrinsic polarization of such TLGs with the symmetry-breaking effect of a substrate, which can also generate out-of-plane electric dipole moments with different sizes in all four polytypes, including ABCB and ABAC twins. We analyze their temperature and magnetic field dependence, in view of understanding the origin of the recently measured Kelvin probe force microscopy maps of tetralayer flakes [S. S. Atri et al.Adv. Phys. Res. 3, 2300095 (2023)], and notice that the intrinsic contribution could be singled out based on magnetic field dependence of polarization measured at low temperatures.

We report a two-dimensional pump-control-probe spectroscopic study of the dynamics of singly excited He Rydberg-state wave packets with a seeded extreme ultraviolet (XUV) free-electron laser (FEL) source. A pair of coherent XUV pulses, defined by their coarse time separation and relative phase, created and manipulated the wave packets. The He atoms were postionized by infrared (IR) pulses, and the ion yield was measured as a function of XUV phase and IR arrival time. We tagged and sorted the relative phase of the XUV pulse pair on a single-shot basis by fitting each FEL spectrum with a suitable function that accounts for nonidealities of the XUV pulse pairs, associated with the seeding process; more generally, the fit returns the time-dependent electric field of the FEL spectra. The experimental two-dimensional maps of ion yields, measured as a function of IR (probe) delay and of XUV (pump-control) phase, were compared with the solution of the first-order time-dependent Schrödinger equation for this field. Despite the fact that the experimental conditions imply strong excitation, beyond the approximations of first-order perturbation theory, the simulated map satisfactorily reproduces the experimental one for temporally well-separated pulses. We show that by selecting data at appropriate values of pump-control phase, we enhance or suppress the amplitude of chosen wave-packet components consisting of two or more Rydberg states. When the temporal overlap of the pulse pair cannot be neglected, the phase reconstruction is underdetermined, and we provide a simplified comparison between data and simulations.

Understanding critical phenomena is of central importance to condensed-matter and high-energy physics. Such an understanding is reflected in our ability to sort observables based on their degeneracy, symmetries, and power-law decays. Here, we study such critical properties of the Ising-Higgs gauge theory in $(2+1)D$ along the self-dual line which have recently been a subject of debate. Using machine learning techniques, we determine the low-energy operator content of the associated field theory. Our approach enables us to largely refute the existence of an emergent current operator and with it the standing conjecture that this transition is of the $X{Y}^{*}$ universality class. We contrast these results with the ones obtained for the $(2+1)D$ Ashkin-Teller transverse field Ising model where we find the expected current operator. Our numerical technique extends the recently proposed real-space mutual information allowing us to extract subleading nonlinear operators. This allows a controlled and computationally scalable approach to target the conformal field theory spectrum. and discern universality classes beyond $(1+1)D$ from Monte Carlo data.

We present an efficient algorithm for simulating open quantum systems dynamics described by the Lindblad master equation on quantum computers, addressing key challenges in the field. In contrast to existing approaches, our method achieves two significant advancements. First, we employ a repetition of unitary gates on a set of $n$ system qubits and, remarkably, only a single ancillary bath qubit representing the environment. It follows that, for the typical case of $m$ locality of the Lindblad operators, we reach an exponential improvement of the number of ancilla in terms of $m$ and up to a polynomial improvement in ancilla overhead for large $n$ with respect to other approaches. Although stochasticity is introduced, requiring multiple circuit realizations, the sampling overhead is independent of the system size. Second, we show that, under fixed accuracy conditions, our algorithm enables a reduction in the number of Trotter steps compared to other approaches, substantially decreasing circuit depth. These advancements hold particular significance for near-term quantum computers, where minimizing both width and depth is critical due to inherent noise in their dynamics.