Acousto-optic modulators (AOMs) have been widely used in quantum optical technology, but the non-ideal diffraction efficiency limits its application in a quantum system. Here we experimentally demonstrate a bi-frequency interferometer scheme by using AOMs as the beam-splitter and the beam-combiner with a near prefect visibility of $(99.5\pm0.2)\%$. Chopped phase locking mode to arbitrary phase offset of the interferometer is achieved by directly introducing phase dithering to the splitting AOM. These features enable the interferometer functions as a high efficiency optical switch for quantum technology. Further discussion shows the interferometer can accomplish the coherent combination of quantum state with different optical frequency and is useful for the generation of entangled quantum state.

Black holes are considered among the most fascinating objects that exist in our universe, since in the classical formalism nothing, even no light, can escape from their vicinity. However, Hawking predicted that escape could still be possible for relativistic particles under certain conditions, known as Hawking radiation. Here we present a purely classical realization of this high energy phenomenon in a network of mechanical circuits, based on analogous condensed matter formalism of tunneling through the event horizon. The underlying network couplings turn out incompatible with classical dynamics and are implemented by embedded active feedback interactions. We demonstrate the tunneling by propagating mechanical wavepackets through the network, achieving an exceptional correspondence to the quantum system both in momentum properties, and in the energy transmission rate exhibiting mass loss within the black hole. Our platform is table-top experimental-ready and reprogrammable, which opens up further possibilities for realizing inaccessible high energy physical phenomena.

We propose a simple system hosting a novel quantum scar due to the interaction between particles, and analyze it theoretically. We consider three repulsively-interacting identical quantum particles in a circular trap. We identify a family of classically unstable periodic trajectories within an ergodic region of the classical phase space. We numerically calculate the quantum eigenstates. For each irreducible representation of the symmetry group of the system, we find a series of scarred quantum states matching the classically unstable trajectory. We explain this series of states using a semiclassical argument which well reproduces the energies of the scarred states. Ours is the first quantum scar in an interacting system to be fully analyzed in terms of the original prediction [Heller, Phys. Rev. Lett. 53, 1515, 1984]. It is also the first to affect the spatial motion, rather than the internal-state dynamics, of the interacting atoms.

We demonstrate the generation of a strong mechanical squeezing in a dissipative optomechanical system by introducing a periodic modulation in the amplitude of a single-tone laser driving the system. The mechanical oscillator is quadratically coupled to the optical mode, which contributes to a strong squeezing exceeding the 3-dB standard quantum limit. The Bogoliubov mode of the mechanical oscillator also cools down to its ground state due to sideband cooling. We further optimize this ratio of sideband strengths to introduce enhanced squeezing. We also compare our results with the analytical (under adiabatic approximation) and the exact numerical solution. Even for a thermal occupancy of 10^4 phonons, mechanical squeezing beyond 3 dB and a strong optomechanical entanglement is observed.

We investigate multiple electromagnetically induced transparency (EIT) in a waveguide quantum electrodynamics (wQED) system containing an atom array. By analyzing the effective Hamiltonian of the system, we find that in terms of the single-excitation collective states, a properly designed $N$-atom array can be mapped into a driven ($N+1$)-level system that can produce multiple EIT-type phenomenon. The corresponding scattering spectra of the atom-array wQED system are discussed both in the single-photon sector and beyond the single-photon limit. The most significant feather of this type of EIT scheme is control-field-free, which may provide an alternative way to produce EIT-like phenomenon in wQED system when external control fields are not available. The results given in our paper may provide good guidance for future experiments on multiple EIT without a control field in wQED system.

A simple scheme is presented for realizing robust optically controlled quantum gates for scalable atomic quantum processors by driving the qubits with optical standing waves. Atoms localized close to the antinodes of the standing wave can realize phase-controlled quantum operations that are potentially more than an order of magnitude less sensitive to the local optical phase and atomic motion than corresponding travelling wave configurations. The scheme is compatible with robust optimal control techniques and spatial qubit addressing in atomic arrays to realize phase controlled operations without the need for tight focusing and precise positioning of the control lasers. This will be particularly beneficial for quantum gates involving Doppler sensitive optical frequency transitions and provides an all optical route to scaling up atomic quantum processors.

We revisit entanglement wedge reconstruction in AdS/CFT using the Petz recovery channel. In the case of a spherical region on the boundary, we show that the Petz map reproduces the AdS-Rindler HKLL reconstruction. Moreover, for a generic subregion of the boundary, we could obtain the same boundary representation of a local bulk field lies in the entanglement wedge as the one proposed earlier in [1, 2] using properties of the modular flow

The Koopman-von Neumann (KvN) theory is one where the dynamical momentum is not canonically conjugate to position, i.e., position and momentum are deconjugated. From this point of view, we show that the KvN theory arises from quantum mechanics, extracting classical equations of motion from quantum ones. However, preserving the canonical structure of the theory requires introducing ``auxiliary'' canonical conjugates to position and momentum. We show that using the KvN formulation to study the interaction between quantum and classical systems forces the auxiliary variables to take on a physical role. While giving rise to classical behaviour, the KvN theory might be more than classical.

In Nature Machine Intelligence 4, 367 (2022), Schuetz et al provide a scheme to employ graph neural networks (GNN) as a heuristic to solve a variety of classical, NP-hard combinatorial optimization problems. It describes how the network is trained on sample instances and the resulting GNN heuristic is evaluated applying widely used techniques to determine its ability to succeed. Clearly, the idea of harnessing the powerful abilities of such networks to ``learn'' the intricacies of complex, multimodal energy landscapes in such a hands-off approach seems enticing. And based on the observed performance, the heuristic promises to be highly scalable, with a computational cost linear in the input size $n$, although there is likely a significant overhead in the pre-factor due to the GNN itself. However, closer inspection shows that the reported results for this GNN are only minutely better than those for gradient descent and get outperformed by a greedy algorithm, for example, for Max-Cut. The discussion also highlights what I believe are some common misconceptions in the evaluations of heuristics.

The quantum circuit model is the default for encoding an algorithm intended for a NISQ computer or a quantum computing simulator. A simple graph and through it, a graph state - quantum state physically manifesting an abstract graph structure - is syntactically expressive and tractable. A graph representation is well-suited for algorithms intended for a quantum computing facility founded on measurement-based quantum computing (MBQC) principles. Indeed, the process of creating an algorithm-specific graph can be efficiently realised through classical computing hardware. A graph state is a stabiliser state, which means a graph is a (quantum) intermediate representation at all points of the algorithm-specific graph process. We submit Q2Graph, a software package for designing and testing of simple graphs as algorithms for quantum computing facilities based on MQBC design principles. Q2Graph is a suitable modelling tool for NISQ computing facilities: the user is free to reason about structure or characteristics of its graph-as-algorithm without also having to account for (quantum) errors and their impact upon state.

The path dependence of adiabatic evolution in classical integrable systems with multiple degrees of freedom is examined. It is shown that different paths may adiabatically transport a normal mode of a mass-spring chain with clamps to different normal modes even when the paths share the endpoints. Accordingly, an adiabatic cycle works as a mode pump, which amounts to the breakdown of single-valuedness of action variables, i.e., the adiabatic invariants, in the adiabatic parameter space. Another topological pump for degenerate localized modes induced by clamps is also explained.

Convolutional neural networks (CNNs) have been employed along with Variational Monte Carlo methods for finding the ground state of quantum many-body spin systems with great success. In order to do so, however, a CNN with only linearly many variational parameters has to circumvent the ``curse of dimensionality'' and successfully approximate a wavefunction on an exponentially large Hilbert space. In our work, we provide a theoretical and experimental analysis of how the CNN optimizes learning for spin systems, and investigate the CNN's low dimensional approximation. We first quantify the role played by physical symmetries of the underlying spin system during training. We incorporate our insights into a new training algorithm and demonstrate its improved efficiency, accuracy and robustness. We then further investigate the CNN's ability to approximate wavefunctions by looking at the entanglement spectrum captured by the size of the convolutional filter. Our insights reveal the CNN to be an ansatz fundamentally centered around the occurrence statistics of $K$-motifs of the input strings. We use this motivation to provide the shallow CNN ansatz with a unifying theoretical interpretation in terms of other well-known statistical and physical ansatzes such as the maximum entropy (MaxEnt) and entangled plaquette correlator product states (EP-CPS). Using regression analysis, we find further relationships between the CNN's approximations of the different motifs' expectation values. Our results allow us to gain a comprehensive, improved understanding of how CNNs successfully approximate quantum spin Hamiltonians and to use that understanding to improve CNN performance.

One-way quantum steering is of importance for quantum technologies, such as secure quantum teleportation. In this paper, we study the generation of one-way quantum steering between two distant yttrium iron garnet (YIG) microspheres in chiral waveguide electromagonics. We consider that the magnon mode with the Kerr nonlinearity in each YIG sphere is chirally coupled to left- and right-propagating guided photons in the waveguide. We find that quantum steering between the magnon modes is absent with non-chirality but is present merely in the form of one way (i.e., one-way steering) when the chirality occurs. The maximal achievable steering is obviously improved as the chirality degree increases. We further find that when the waveguide's outputs are subjected to continuous homodyne detection, the steering can be considerably enhanced and asymmetric steering with strong entanglement can also be achieved by tuning the chirality. Our study shows that chirality can be explored to effectively realize one-way quantum steering. Compared to other studies on achieving asymmetric steering via controlling intrinsic dissipation, e.g. cavity loss rates, our scheme merely depends on the chirality enabled via positioning the micromagnets in the waveguide and is continuously adjustable and experimentally more feasible.

Perturbation theory is an important technique for reducing computational cost and providing physical insights in simulating quantum systems with classical computers. Here, we provide a quantum algorithm to obtain perturbative energies on quantum computers. The benefit of using quantum computers is that we can start the perturbation from a Hamiltonian that is classically hard to solve. The proposed algorithm uses quantum signal processing (QSP) to achieve this goal. Along with the perturbation theory, we construct a technique for ground state preparation with detailed computational cost analysis, which can be of independent interest. We also estimate a rough computational cost of the algorithm for simple chemical systems such as water clusters and polyacene molecules. To the best of our knowledge, this is the first of such estimates for practical applications of QSP. Unfortunately, we find that the proposed algorithm, at least in its current form, does not exhibit practical numbers despite of the efficiency of QSP compared to conventional quantum algorithms. However, perturbation theory itself is an attractive direction to explore because of its physical interpretability; it provides us insights about what interaction gives an important contribution to the properties of systems. This is in sharp contrast to the conventional approaches based on the quantum phase estimation algorithm, where we can only obtain values of energy. From this aspect, this work is a first step towards ``explainable'' quantum simulation on fault-tolerant quantum computers.

Ab initio method based on a complex-scaling approach and aimed at a rigorous QED description of autoionizing states is worked out. The autoionizing-state binding energies are treated nonperturbatively in $\alpha Z$ and include all the many-electron QED contributions up to the second order. The higher-order electron correlation, nuclear recoil, and nuclear polarization effects are taken into account as well. The developed formalism is demonstrated on the $LL$ resonances in heliumlike argon and uranium. The most accurate theoretical predictions for the binding energies are obtained.

Quantum reservoir computers (QRC) and quantum extreme learning machines (QELM) aim to efficiently post-process the outcome of fixed -- generally uncalibrated -- quantum devices to solve tasks such as the estimation of the properties of quantum states. The characterisation of their potential and limitations, which is currently lacking, will enable the full deployment of such approaches to problems of system identification, device performance optimization, and state or process reconstruction. We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements, and provide an explicit characterisation of the information exactly retrievable with such protocols. We furthermore find a close analogy between the training process of QELMs and that of reconstructing the effective measurement characterising the given device. Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs, and has the potential to become a powerful measurement paradigm for quantum state estimation that is more resilient to noise and imperfections.

The interplay of nuclear and electronic dynamics characterizes the multi-dimensional electronic spectra of various molecular and solid-state systems. Theoretically, the observable effect of such interplay can be accounted for by response functions. Here, we report analytical expressions for the response functions corresponding to a class of model systems. These are characterized by the coupling between the diabatic electronic states and the vibrational degrees of freedom resulting in linear displacements of the corresponding harmonic oscillators, and by nonadiabatic couplings between pairs of diabatic states. In order to derive the linear response functions, we first perform the Dyson expansion of the relevant propagators with respect to the nonadiabatic component of the Hamiltonian, then derive and expand with respect to the displacements the propagators at given interaction times, and finally provide analytical expressions for the time integrals that lead to the different contributions to the linear response function. The approach is then applied to the derivation of third-order response functions describing different physical processes: ground state bleaching, stimulated emission, excited state absorption and double quantum coherence. Comparisons between the results obtained up to sixth order in the Dyson expansion and independent numerical calculation of the response functions provide an evidence of the series convergence in a few representative cases.

Instruction scheduling is a key transformation in backend compilers that take an untimed description of an algorithm and assigns time slots to the algorithm's instructions so that they can be executed as efficiently as possible while taking into account the target processor limitations, such as the amount of computational units available. For example, for a superconducting quantum processor these restrictions include the amount of analogue instruments available to play the waveforms to drive the qubit rotations or on-chip connectivity between qubits. Current small-scale quantum processors contain only a few qubits; therefore, it is feasible to drive qubits individually albeit not scalable. Consequently, for NISQ and beyond NISQ devices, it is expected that classical instrument sharing to be designed in the future quantum control architectures where several qubits are connected to an instrument and multiplexing is used to activate only the qubits performing the same quantum operation at a time. Existing quantum scheduling algorithms either rely on ILP formulations, which do not scale well, or use heuristic based algorithms such as list scheduling which are not versatile enough to deal with quantum requirements such as scheduling with exact relative timing constraints between instructions, situation that might occur when decomposing complex instructions into native ones and requiring to keep a fixed timing between the primitive ones to guarantee correctness. In this paper, we propose a novel resource constrained scheduling algorithm that is based on the SDC formulation, which is the state-of-the-art algorithm used in the reconfigurable computing. We evaluate it against a list scheduler and describe the benefits of the proposed approach. We find that the SDC-based scheduling is not only able to find better schedules but also model flexible relative timing constraints.

Portfolio optimization is an important process in finance that consists in finding the optimal asset allocation that maximizes expected returns while minimizing risk. When assets are allocated in discrete units, this is a combinatorial optimization problem that can be addressed by quantum and quantum-inspired algorithms. In this work we present an integer simulated annealing method to find optimal portfolios in the presence of discretized convex and non-convex cost functions. Our algorithm can deal with large size portfolios with hundreds of assets. We introduce a performance metric, the time to target, based on a lower bound to the cost function obtained with the continuous relaxation of the combinatorial optimization problem. This metric allows us to quantify the time required to achieve a solution with a given quality. We carry out numerical experiments and we benchmark the algorithm in two situations: (i) Monte Carlo instances are started at random, and (ii) the algorithm is warm-started with an initial instance close to the continuous relaxation of the problem. We find that in the case of warm-starting with convex cost functions, the time to target does not grow with the size of the optimization problem, so discretized versions of convex portfolio optimization problems are not hard to solve using classical resources. We have applied our method to the problem of re-balancing in the presence of non-convex transaction costs, and we have found that our algorithm can efficiently minimize those terms.

The spectrum of a local random Hamiltonian can be represented generically by the so-called $\epsilon$-free convolution of its local terms' probability distributions. We establish an isomorphism between the set of $\epsilon$-noncrossing partitions and permutations to study its spectrum. Moreover, we derive some lower and upper bounds for the largest eigenvalue of the Hamiltonian.