Papers

The homogeneity of the microwave magnetic field is essential in controlling a large volume of ensemble spins, for example, in the case of sensitive magnetometry with nitrogen-vacancy (NV) centers in diamond. This is particularly important for pulsed measurement, where the fidelity of control pulses plays a crucial role in its sensitivity. So far, several magnetic field-forming systems have been proposed, but no detailed comparison has been made. Here, we numerically study the homogeneity of five different systems, including a planar antenna, a dielectric resonator, a cylindrical inductor, a barrel-shaped coil, and a nested barrel-shaped coil. The results of the simulation allowed us to optimize the design parameters of the barrel-shaped field-forming system, which led to significantly improved magnetic field uniformity. To measure this effect, we experimentally compared the homogeneity of a field-forming system having a barrel shape with that of a planar field-forming system by measuring Rabi oscillations of an ensemble of NV centers with them. Significant improvements in inhomogeneity were confirmed in the barrel-shaped coil.

Efficient, scalable, and magnetic-field-free superconducting diodes are essential for future superconducting electronics; yet, despite significant efforts, such practical devices remain unrealized. The main challenge lies in achieving broad-range in-situ tunability, both for optimization and for achieving transistor-like operation. Here, we study diodes based on four-terminal niobium planar Josephson junctions. We show that the multiterminal structure eliminates the need for an external magnetic field and enables essentially unrestricted in-situ tunability, along with reconfigurability of the diode polarity, leading to new functionality. For example, we demonstrate that such diodes can operate as Gauss neurons via reentrant superconductivity. By deliberately tuning the junction parameters, we obtain effectively infinite nonreciprocity (within experimental resolution) leading to threshold-free ac-current rectification. Such technologically simple, reconfigurable, and broadly tunable diodes could be instrumental for future digital and neuromorphic computing.

The traversable wormhole protocol in coupled Sachdev-Ye-Kitaev (SYK) systems produces a transmission signal C(t) widely interpreted as evidence of holographic dynamics. Recent work has questioned this interpretation, showing that similar signals arise in generic thermalizing systems. We address what the signal actually probes by systematically destroying quantum chaos in the SYK model via random coupling deletion, while monitoring the transmission signal across the chaos-to-integrable transition. Using exact diagonalization of the doubled SYK model at N=10 with 50 disorder realizations per sparsity, supplemented by Krylov-subspace extensions to N=20, we find that the ensemble-averaged peak height varies by less than 1.1% across a 50-fold sparsification range, even as the underlying spectrum transitions from Gaussian-unitary-ensemble to sub-Poisson statistics. A 1,200-instance sweep over the inter-system coupling mu confirms that the signal is controlled by mu alone, with no dependence on internal chaos. We further verify that the thermofield double state retains its thermal structure under sparsification despite substantial changes to the state vector, providing a structural explanation for the invariance. These results indicate that the transmission signal diagnoses inter-system coupling fidelity rather than holographic dynamics, and that future quantum-simulation experiments require independent chaos diagnostics to substantiate gravitational claims. As a practical consequence, the invariance implies that 98% of the Hamiltonian's coupling terms can be discarded (with variance rescaling of the survivors), reducing the gate count per Trotter step by approximately 50x at N=10 and bringing larger traversable-wormhole simulations within experimental reach.

Bell's theorem states that no description of a Bell experiment can be simultaneously local, realistic in the sense of counterfactual definiteness, and free of conspiracy between settings and hidden state. The recent generation of experiments has confirmed the predicted violation of the CHSH inequality, so one of the assumptions must be abandoned. Which one, and how one reconstructs a coherent worldview after doing so, is a question on which many authors disagree. This paper is written by three such authors. All three reject both counterfactual definiteness and conspiratorial violation of statistical independence of setting choices and state. After a joint exposition of the classical half of Bell's theorem in the language of Pearl-style causal graphs, a joint summary of the loophole-free experiments, and a joint survey of the recent literature, each author states where they have presently arrived. Gill accepts irreducible and non-local quantum randomness and finds the choice between locality and realism a false dichotomy. In his later works, Bell derives counterfactual definiteness from classical local causality, and that is what has to go. The metaphysical concepts "realism", "locality", "causality" need to be reconsidered. Helland reconstructs the Hilbert-space formalism from a theory of accessible variables, and from this theory he concludes that every observer must be limited in a specific sense. Jongejan proposes a geometric hidden-variable construction in which the degree of violation of the CHSH inequality depends on the number of dimensions of space, Tsirelson's bound corresponding to three dimensions. The authors conclude with a discussion.

End-to-end pumps utilizing edge-localized states offer a promising route to operate as connecting buses, supplying efficient and robust connectivity between non-neighboring elements in a network. Here, we investigate a finite quasiperiodic Fibonacci-type photonic chain and demonstrate its ability for end-to-end pumping, with only small and simple changes to the system. First, we use a tight-binding formalism to numerically show that a localized pumping state can be transferred between opposite ends of the system, with only a small structural change to the chain. Then, we experimentally implement this end-to-end pump in an array of coupled optical waveguides, where light propagation is effectively described by the tight-binding model under the paraxial approximation, enabling direct correspondence between theory and experiment. We numerically simulate and experimentally demonstrate pumping by injecting light into a single waveguide at one end of the setup, which activates a localized pumping state. As the light propagates along the wave guide array, it is also pumped to the other end. We further show that pumping remains robust against structural deformation, such as controlled defects in the waveguide array. Our results establish that quasiperiodic Fibonacci-type photonic lattices are a robust and experimentally viable platform for disorder-resilient state transfer.

Hybrid quantum–classical pipelines increasingly support applications such as drug discovery, fraud detection, and cloud quantum processing unit (QPU) auditing, yet existing integrity-verification methods remain largely classical and fail to capture quantum-stage behaviour. We propose QCIVET, a contract-based integrity-verification framework that models a hybrid pipeline as a sequence of stages with explicit specifications and audits it at both syntactic and semantic levels. Syntactic integrity is enforced through a hash-chained audit trail with optional external anchoring, while semantic integrity at quantum stages is verified using a calibrated observable-deviation test grounded in the behavioural-subtyping discipline of Liskov and Wing. We prove soundness under the diamond-norm distance between quantum channels, conditional completeness for informationally complete observable families, and compositionality under inheritance chains. We further identify a class of Z-only-sneaky overrides that evade weak single-Pauli contracts but are exposed by multi-Pauli contracts. The framework is evaluated under calibration-derived noise models from IBM Quantum Eagle r3 and Heron r2 processors, and the subtype-separation protocol is validated end-to-end on a real ibm_fez (Heron r2) processor. QCIVET is instantiated on three representative applications: variational quantum eigensolver (VQE) for drug discovery, quantum-assisted fraud detection, and customer-side auditing of cloud QPU services. The reference implementation, including a real-time verification engine with sub-millisecond per-stage commit latency, is released as open source.

Feedback-based quantum optimization is a quantum approach to combinatorial optimization. In this paper, we introduce the classical counterpart of feedback-based quantum optimization by using the quantum-classical correspondence of spin systems to discuss the possibility of quantum advantage. It also enables us to develop higher-order theory of a previously proposed classical approach to feedback-based quantum optimization. First, we compare the feedback-based algorithm for quantum optimization (FALQON) and its variant with their classical counterparts. Then, we perform benchmark tests of various quantum and classical algorithms with small-scale instances, and of classical algorithms with large-scale instances. Main findings are that (i) quantum algorithms can be advantageous to classical algorithms in terms of the quality of solutions, while classical algorithms tend to show faster convergence than quantum ones, and (ii) one of the classical algorithms discussed in this paper shows significant scalability for higher-order unconstrained binary optimization problems. These findings highlight the importance of quantumness and the usefulness of classical approaches.

The quantum approximate optimization algorithm (QAOA) holds promise for combinatorial optimization but is constrained by limited qubits. While divide-and-conquer frameworks like QAOA$^{2}$ address scalability by partitioning graphs into subgraphs, existing methods suffer from two fundamental limitations: i) misalignment between heuristic partitioning metrics and quantum optimization goals, and ii) topology-blind parameter initialization that leads to optimization cold starts. To bridge these gaps, we propose Neural QAOA$^{2}$, an end-to-end differentiable framework that jointly generates graph partitions and initial parameters. By integrating a generative evaluative network (GEN), our method utilizes a differentiable quantum evaluator as a high-fidelity performance surrogate to provide direct gradient guidance, enabling the joint generator to learn the intrinsic mapping from graph topology to high-quality partition and parameter configurations. Extensive experiments on 183 QUBO, Ising, and MaxCut instances (21 to 1000 variables) demonstrate that our gradient-driven approach broadly outperforms heuristic baselines, ranking first on 101 instances. It exhibits zero-shot generalization across out-of-distribution graph topologies and scales.

We study quantum transport in a periodically driven (Floquet) topological system coupled to static fermionic reservoirs. Using the Floquet nonequilibrium Green's-function (NEGF) formalism we show, from exact numerics for a strip geometry, that the two-terminal (longitudinal) conductance is quantized as $|W_{\varepsilon}|\,e^2/h$, while the Hall (transverse) conductance is quantized as $W_{\varepsilon}\,e^2/h$, where $W_{\varepsilon}$ is the Floquet winding invariant associated with the quasienergy gap at $\varepsilon = 0$ or $\varepsilon = \Omega/2$. Quantization is achieved only after summing over the contribution of all Floquet sidebands. We provide an analytic understanding of this Floquet conductance sum rule, by considering the Hall conductance in the weak coupling limit. In that limit, we show that the Floquet Hall conductance gets contributions from the Floquet sidebands, which includes the signs of the velocities of the edge modes. Their sum yields exact quantization, as predicted by the Floquet sum rule. We find that in a wide range of parameter regime, the convergence is fast, making observation of the sum rule and Floquet winding numbers accessible to experiments.

We consider the multi-qubit fixed-excitation state transfer along the spin chain with dipole-dipole interaction subjected to the interaction with environment governed by the Lindblad equation preserving the excitation number during spin-evolution. The state transfer algorithm includes the state restoring via Kraus operators and ancilla measurement. As a result, the transferred state appears in superposition with completely mixed state, the latter disappears with vanishing interaction with environment. In that case we deal with probabilistic perfect state transfer. Example of an arbitrary multi-qubit one-excitation state transfer is present and its robustness with respect to perturbation of the Kraus operators is studied.

Quantum information platforms enable analog quantum simulations, such as quantum annealing, offering a promising route to solving complex combinatorial optimization problems. Here, we propose a quantum information architecture based on networks of interacting parity-time (PT)-symmetric non-Hermitian qubits. While the dynamics of individual PT-symmetric qubits have been experimentally demonstrated across multiple platforms including NV centers, superconducting circuits, and trapped-ion systems yet coherent dynamics in interacting systems remain largely unexplored. To address this issue we theoretically investigate stationary and time-dependent Hamiltonians relevant to quantum annealing using a minimal model of two interacting XX-coupled PT-symmetric non-Hermitian qubits. We analyze both symmetry-preserving and symmetry-broken regimes and demonstrate that adding even tiny PT-symmetric non-Hermitian terms in the qubits Hamiltonian allows to greatly enhance the probability of reaching the ground state after annealing.

Quantum key distribution (QKD) promises information-theoretic security based on quantum mechanics and idealized device models. Practical implementations, however, deviate from these models due to unavoidable device imperfections, and existing security proofs fall short of capturing the complexity of real-world systems. Here we introduce a versatile numerical finite-key security framework valid against general coherent attacks and applicable to a broad class of practical QKD setups. It accommodates most relevant imperfections at both transmitter and receiver, including non-independent-and-identically-distributed (non-IID) signals arising in high-speed QKD systems due to the limited bandwidth of optical modulators, while requiring only partial characterization of the apparatuses. We demonstrate the power of our framework by proving the security of a realistic decoy-state QKD implementation with laser sources, providing a practical route towards rigorous security certification of real-world QKD setups.

Some collapse models proposed that gravitational effects cause the instability of mass distribution superpositions, leading to wave function collapse. In this paper, we utilize the quantum Boltzmann equation (QBE) to analyze the behavior of a fermion in a spatial superposition under graviton emission. We introduce a quantitative measure that links the stability of the superposition to the spatial separation, particle mass, and gravitational coupling. By examining the collision term in the QBE, we derive the decoherence rate and show how it depends on these parameters. Our results provide a detailed framework for understanding gravity induced decoherence, bridging the gap between quantum field theory and collapse models. We also discuss the implications of these findings for experimental tests of gravitationally induced wave function collapse and the broader class of collapse models known as dissipative continuous spontaneous localization (CSL) model.

Quantum batteries, which use quantum systems to store and deliver energy, are promising for next-generation energy storage. However, optimizing charging strategies and understanding the interplay between dissipation and quantum coherence remain open challenges. Here, we investigate steady-state charging in an open quantum battery and demonstrate that the charging timescale depends on the spectral gap of the Liouvillian operator governing dissipative dynamics. As a minimal example, we examine a three-level quantum battery realized in a single trapped ${}^{40}\mathrm{Ca}^{+}$ ion, where energy from an engineered thermal photon reservoir is coherently transferred to a long-lived metastable storage state. We find that long-term dynamics are confined to a low-dimensional manifold of slow Liouvillian modes, with their spectral structure determining the relaxation rate to the charged steady state. By adjusting experimentally accessible parameters, such as reservoir occupation and coherent coupling strength, the non-Hermitian Liouvillian spectrum can approach an exceptional point. This increases the spectral gap and accelerates the approach to steady state. As a result, this mechanism significantly enhances asymptotic charging power without relying on many-body collectivity or steady coherence. Our findings offer fundamental insights into open quantum thermodynamics and provide a path to efficient energy storage and fast-charging solutions in emerging quantum technologies.

We investigate the quantum computing of the vibrational dynamics of CO$_2$ and H$_2$O by constructing the vibrational third-order anharmonic Hamiltonian in qubit and qudit form by two types of qubit encodings (binary and direct) and a qudit encoding. We simulate the time-dependent vibrational population transfer using the three different encodings, including the effect of noise and find that the qudit encoding leads to the most accurate results both for CO$_2$ and H$_2$O because of the small number of terms in the qudit Hamiltonian as long as the same values of the entangling gate error rates are adopted. Although the entangling gate errors of current qudit quantum computers are approximately one order of magnitude larger than the entangling gate errors of current qubit quantum computers, our results suggest that the simulation of vibrational dynamics is a promising application for future qudit quantum computers.

Variational Quantum Algorithms (VQAs) are a leading approach to exploiting near-term quantum hardware, leveraging parameterized quantum circuits and classical optimization to achieve advantage. Despite their promise, the practical deployment of VQAs is challenged by the difficulty of designing quantum circuit architectures that balance expressivity, trainability, and hardware constraints. Existing evolutionary-based quantum neural architecture search methods address these challenges but suffer from high computational costs due to repeated training of candidate circuits. In this work, we identify a setting in which the Gram matrix of the Quantum Neural Tangent Kernel converges. Building on this observation, we design a zero-shot surrogate model to estimate candidate performance without full training, significantly accelerating the architecture search process. Using this surrogate, we propose MZeQAS, a Monte Carlo Tree Search (MCTS)-based Zero-Shot Quantum Neural Architecture Search framework for VQAs. By integrating proxy-based performance estimation with MCTS exploration, MZeQAS efficiently discovers high-performing architectures. Experimental results demonstrate that MZeQAS outperforms existing approaches in terms of both search efficiency and solution quality, providing a scalable and effective framework for advancing VQA deployment on noisy intermediate-scale quantum devices.

Partial differential equations (PDEs) are central to modeling physical and engineering systems, but repeatedly solving parametric PDEs remains computationally expensive. Operator learning enables fast surrogate inference, yet typically requires large input-output paired datasets generated by costly high-fidelity PDE solvers. Unsupervised operator learning frameworks alleviate data dependency but remain hindered by computational bottlenecks. To address this, we propose Neural Variational Quantum Linear Solver (NVQLS), the first hybrid quantum-classical operator learning framework leveraging the Legendre–Galerkin weak formulation. We critically resolve the sign ambiguity in VQLS energy minimization, preventing erroneous solution representations. Additionally, we introduce a neural embedding, a novel encoding scheme to map varying forcings and PDE coefficients into parameterized quantum circuit representations. These structural innovations provide theoretical computational complexity advantages under efficient state preparation schemes, while achieving superior accuracy compared to a representative classical baseline. Validations on 1D and 2D parametric PDEs under diverse boundary conditions demonstrate NVQLS's capability to simultaneously process varying inputs, offering a scalable unsupervised approach to quantum-enhanced operator learning.

Currently, the primary applications of fluorescent nanodiamonds (FNDs) are in the area of biosensing, by using photoluminescence or spin properties of colour centres, mainly represented by the Nitrogen Vacancy (NV) point defect. The sensitivity of NV-FNDs to external fields is, however, limited by crystallographic defects, which influence their key quantum state characteristics - the spin longitudinal (\textit{T$_1$}) and spin transversal (\textit{T$_2$}) relaxation and coherence times, respectively. We report on utilising an advanced FND growth technique consisting of heterogeneous nucleation on pre-engineered sites to create FNDs averaging around 60 nm in size, with mean longitudinal coherence times of 800 $\mu$s and a maximum over 1.8 ms, close to bulk theoretical values. This is a major, nearly ten-fold improvement over commercially available nanodiamonds for the same size range of 50 to 150 nm. Heavy-N doped nanodiamond shells, important for sensing events in nm proximity to the diamond surface, are fabricated and discussed in terms of re-nucleation and twinning on \{111\} crystal facets. We also discuss scalability issues in order to enable the production of FND volumes matching the needs of sensing applications.

The steady incompressible Navier–Stokes equations pose significant computational challenges due to their nonlinear convective terms and pressure–velocity coupling. Physics-informed neural networks (PINNs) provide a mesh-free framework for approximating such systems, but classical PINNs can experience optimization difficulties in nonlinear flow regimes. In this work, we propose a quantum physics-informed neural network (QPINN) framework with a quantum neural network (QNN)-based trainable embedding for the lid-driven cavity problem. The proposed approach uses a QNN to learn data-adaptive quantum feature maps that encode spatial coordinates before they are processed by a variational quantum circuit within a physics-informed loss formulation. Numerical experiments show that the proposed QNN-TE-QPINN exhibits stable training behavior and competitive solution accuracy compared with classical PINNs and hybrid quantum models using classical embeddings, while requiring significantly fewer trainable parameters. Rather than claiming computational speedup, these results highlight the potential of trainable quantum embeddings for parameter-efficient physics-informed learning. The findings suggest that embedding design plays an important role in quantum-assisted PDE solvers and support further investigation of QNN-based trainable embeddings for nonlinear fluid dynamics benchmarks.

Quantum measurements can exhibit contextuality: their outcomes cannot always be explained by assigning pre-existing values that are independent of which compatible measurements are performed together. The Klyachko-Can-Binicioğlu-Shumovsky (KCBS) inequality provides the canonical minimal test of this effect for a single three-level quantum system, or qutrit, using five measurement events arranged as a pentagon. Here we ask whether a larger but still compact set of measurement events can produce a stronger separation between quantum predictions and the corresponding noncontextual limit. We perform an exhaustive search over all 11,117 connected non-isomorphic graphs with eight vertices, where each vertex represents a measurement event and edges connect pairs of events that cannot occur together. We identify a sparse ten-edge graph, which we call Quad-$C_5$, as the unique maximizer of this separation at the reported numerical precision. The graph can be understood as four overlapping KCBS pentagons, with every edge shared by two pentagons. Quad-$C_5$ already demonstrates contextuality in a qutrit, for which we obtain an exact analytical result and find the same violation above the noncontextual bound as in the original KCBS test. The larger quantum-noncontextual separation allowed by the graph, however, becomes accessible in a four-level quantum system, where numerical optimization reaches the full graph-theoretic quantum bound and yields a larger contextuality gap than the standard eight-vertex Wagner-graph benchmark while requiring fewer pairwise constraints. Quad-$C_5$ therefore provides a compact connection between minimal qutrit contextuality and stronger contextuality tests available in higher-dimensional quantum systems.

We introduce a new family of CSS codes obtained from rate-1 precoded polar codes, which harnesses the precoding benefits obtained for classical short blocklength polar codes. We optimize the rate profile and precoder of these codes with a genetic algorithm, and present codes of dimension $ [\![256, 2 ]\!] $ and $ [\![512, 2]\!] $ that have logical error rates similar to the $ [\![1201, 1, 25 ]\!] $ surface code over the depolarizing channel.

We investigate the purification dynamics of a single qubit under continuous in time monitoring. By employing a collisional model framework where the system interacts sequentially with ancillary qubits, we describe the conditioned evolution of the density matrix through a stochastic master equation. We show that for initial mixed states, the dynamics reduce to a multiplicative Langevin equation for a single scalar parameter representing the state's purity. This stochastic process is solved exactly using the Onsager-Machlup path integral formalism, allowing us to derive the full probability distribution for the qubit's trajectories. Our analytical results reveal that purification is characterized by a dynamical crossover from a diffusion dominated regime to a measurement dominated regime, visible in the emergence of a bimodal state distribution. The analytical solutions are in strong agreement with numerical simulations, providing a robust theoretical benchmark for the study of information extraction in monitored quantum systems.

Closed-form expressions for all matrix elements required for variational calculation of the electronic structure of periodic solids have been derived using a basis of explicitly correlated Gaussians (ECGs). Periodic basis functions are constructed by summing shifted correlated Gaussians over all composite lattice translations, where a generalized unfolding theorem reduces the resulting double lattice sum to a single sum through a unified computational framework for overlap, kinetic energy, and Coulomb potential operators. The formalism has been validated through application to an infinite one-dimensional hydrogen chain, where the ground-state energy per atom computed in the thermodynamic limit is shown to agree with finite-chain results extrapolated by other many-body methods.

In the field of quantum reservoir computing (QRC), many different computational models and architectures have been proposed. From these models, we identify feedback-based models – which use a feedback mechanism to re-embed classical measurements from the QRC – and recurrent models – which use a multi-register approach with memory and readout qubits – as the two major competing architectures that have been discussed and validated on hardware. In this paper, we advance upon the recurrent architectures, which employ a two register approach to endow the QRC with a fading memory. While these approaches have been validated on hardware and have demonstrated great real-world performance on noisy-intermediate-scale-quantum (NISQ) quantum processing units (QPUs), the exact mechanism through which the memory capacity arises is not completely understood or fully controllable. With this, we augment the recurrent approaches and present a hardware-realizable mechanism, which we call a tunable partial-SWAP, that allows for the direct control of the rate of memory dissipation from a QRN implemented on a gate-based QPU. The theory behind this mechanism is discussed in terms of a controlled amplitude-damping channel and validation experiments using a randomized short-term memory capacity (STMC) recall benchmark and the NARMA-5 dataset are conducted using simulation and IBM QPUs, respectively.

Nonreciprocal quantum batteries offer superior charging performance compared to reciprocal quantum batteries. We consider a charger-battery system comprising two optical cavities that interact independently with a third auxiliary cavity. We show that the nonzero dissipation of the auxiliary cavity induces a nonreciprocal exchange of excitations among the charger-battery system. Therefore, by engineering the loss in the auxiliary cavity, we induce a directional energy flow that enhances the charging efficiency. Using numerical and analytical calculations, we show that the steady-state energy stored in the battery significantly exceeds that in the charger. We compare our results with those of the reciprocal cases and demonstrate that our nonreciprocal quantum battery model exhibits a significant charging advantage. We believe that our proposed scheme represents a step forward in cavity-loss engineering, making it a viable approach for nonreciprocal quantum batteries with existing experimental techniques.

An answer partition specifies which oracle instances share an output, while query complexity also depends on how those instances are accessed. We formulate exact one-query answer-partition resolution as block discrimination of a unitary oracle family. Applied to Deutsch's problem with a controlled response unitary V, the criterion shows that one query suffices if and only if -1 is an eigenvalue of V; cyclic addition in odd response dimension therefore raises the exact quantum cost to two queries, matching the classical cost while preserving the same computational-basis point values. Holding the standard Boolean point oracle fixed, we further show that balanced answer partitions with equal-sized cells can have different exact classical and quantum query complexities. A complete exact classification is given for all 35 balanced three-bit partitions, while the unresolved four-bit cases are reported explicitly as numerical candidates. These results separate the combinatorial specification of an answer from the oracle-dependent distinguishability that determines query complexity.

The influence-matrix formalism provides an alternative route to the classical simulation of quantum dynamics. Because influence matrices retain information only about the effective bath seen by local observables, they are expected to be easier to simulate than the full wavefunction. Recent work, however, has shown that they carry strong temporal correlations even in maximally chaotic systems, making them difficult to represent efficiently. Here we show that the reduced transition matrix, a suitable combination of influence matrices that directly determines local expectation values, can nevertheless be efficiently approximated. We first show that the truncation error is controlled by its singular-value spectrum, which naturally motivates a low-rank approximation. We then prove that, for chaotic dual-unitary circuits, the associated entropy grows at most logarithmically in time. Our conclusions follow from exact results for random dual-unitary circuits and are further supported by numerical results for fixed instances of both dual-unitary and random circuits.

Quantum emitters in hexagonal boron nitride (hBN) have emerged as a promising solid-state platform for quantum technology applications. However, a persistent challenge in the field is the unclear origin of many observed emission lines, particularly in the visible range, which can be difficult to distinguish from signals arising from organic or process-induced contamination during sample preparations and handling. This ambiguity limits both the reproducibility of emitter generation and the reliable identification of truly intrinsic quantum defects. This work provides a step-by-step framework to assess whether quantum emitters in electron-irradiated hBN are associated with organic contaminants introduced during sample preparation. We employ hyperspectral imaging, thermal annealing, and oxygen plasma etching to investigate the origin of the green-yellow emitters in electron-irradiated hBN. The combined results not only rule out organic contamination as the source of emission but also provide insight into the spectral variability, thermal stability, and vertical localization of the emitters generated in electron-irradiated hBN that was created without any pre- or post-processing. In addition, our experiments demonstrate the feasibility of creating stable emitters in hBN with thicknesses below 10 nm. These findings provide practical guidance for the identification and controlled implementation of hBN-based single-photon emitters in quantum photonic devices.

Multivariate quantum signal processing (M-QSP) has recently been shown to be applicable for non-Hermitian Hamiltonian simulation, opening several problems regarding the optimization landscape, angle-finding, and constant-factor analysis. We resolve several of these problems here. We find the anti-Hermitian query complexity $d_I = \Theta(\betaI T + \log(1/\varepsilon)/\log\log(1/\varepsilon))$ to be tight, established via Chebyshev coefficient bounds, modified Bessel function asymptotics, and Lambert~$W$ inversion. Fast-forwarding to $d_I = \mathcal{O}(\sqrt{\betaI T})$ is impossible in the bivariate polynomial model, though a linear state-dependent improvement to $d_I = \mathcal{O} \beta_{\mathrm{eff}} T + \log(1/\varepsilon)/\log\log(1/\varepsilon))$ is achievable. The optimization landscape of M-QSP admits spurious local minima, but a warm-start basin guarantee ensures the two-stage algorithm converges. CRC-exploiting block peeling reduces angle-finding from $\mathcal{O}(d^3)$ to $\mathcal{O}(d^2)$ classical operations, and optimized error allocation yields a leading constant of approximately~$2$ relative to the information-theoretic lower bound. A constant-ratio condition extends to non-identical signal operators, enabling time-dependent non-Hermitian simulation with query complexity $\mathcal{O}(\int_0^T(\alphaR(s) + \betaI(s))\,ds + \log(1/\varepsilon)/\log\log(1/\varepsilon))$. Block-encoding overhead $e^{-2\betaI T}$ holds across all function classes within the walk-operator oracle model, and dilational methods (Schrödingerization) achieve the walk-operator barrier. A precisely characterized direct-access construction achieves the intrinsic barrier $e^{-2\omega T}$ (with $\omega < \betaI$ for non-commuting Hamiltonians) on a restricted domain, though extension to the full bitorus remains open.

We investigate the dissipative dynamics of linear and nonlinear waves in harmonic traps by means of engineered complex non-Hermitian potentials. By combining an analytical mapping between real and complex Schrödinger equations with direct numerical simulations, we show that while in the linear case the damped motion leads to the formation of a stationary state at the trap center, in the nonlinear case a static potential design alone is insufficient to ensure long-term stability. Instead, the system relaxes toward a long-lived metastable configuration that eventually undergoes decay or collapse. To overcome this limitation, we introduce a time-dependent modulation of the nonlinearity that effectively converts these metastable states into robust non-equilibrium stationary states. This approach establishes a general strategy for controlling nonlinear waves in non-Hermitian systems, with potential applications in photonics and Bose–Einstein condensates.