Papers

Numerical optimal control (GRAPE) can in principle discover pulse shapes that suppress all coherent gate error to machine precision. But when does that capability actually matter? We present a systematic comparison of Gaussian, DRAG, and GRAPE pulses for single-qubit gates on a three-level transmon model parameterized by IQM Garnet hardware ($T_1 = 37\,\mu$s, $T_2 = 9.6\,\mu$s,$\alpha/2\pi = -200$ MHz), with the explicit goal of identifying the regimes where numerical optimization provides genuine practical advantage over analytical methods. Our central finding is that properly calibrated DRAG already operates near the decoherence floor. At 20 ns gate time, GRAPE eliminates all coherent error ($1 - F < 10^{-15}$), but DRAG achieves $1 - F = 4.9 \times 10^{-4}$ in coherent error alone,and $8.4 \times 10^{-4}$ under full decoherence – only $1.2\times$ above GRAPE's decoherence-limited performance. More surprisingly,DRAG is more robust than GRAPE to qubit frequency detuning (minimum fidelity 0.990 vs.\ 0.931 over $\pm 5$ MHz), the dominant calibration uncertainty in charge-noise-limited transmons. GRAPE retains superior amplitude robustness (minimum fidelity 0.994 vs.\ 0.990) and provides the only route to guaranteed zero coherent error, which matters at short gate times ($\lesssim 15$ ns) where perturbative corrections break down. These results lead to concrete calibration guidance: (1) properly calibrated DRAG is sufficient for gate times $\gtrsim 20$ ns on hardware with $T_2/T \gtrsim 500$, (2) GRAPE is necessary at short gate times or when targeting error rates well below the decoherence floor, and (3) robust optimal control incorporating frequency uncertainty should be used when detuning is the dominant noise source. We decompose the full error budget (coherent, $T_1$, $T_2$, control noise) and provide the open-source QubitPulseOpt framework for reproducing all results.

Quantum computers have steadily improved over the last decade, but developing fault-tolerant quantum computing (FTQC) techniques, required for useful, universal computation remains an ongoing effort. Key elements of FTQC such as error-correcting codes and decoding are supported by a rich bed of stabilizer simulation software such as Stim and CHP, which are essential for numerically characterizing these protocols at realistic scales. Recently, experimental groups have built nascent high-dimensional quantum hardware, known as qudits, which have a myriad of attractive properties for algorithms and FTQC. Despite this, there are no widely available qudit stabilizer simulators. We introduce the first open-source realization of such a simulator for all dimensions. We demonstrate its correctness against existing state vector simulations and benchmark its performance in evaluating and sampling quantum circuits. This simulator is the essential computational infrastructure to explore novel qudit error correction as earlier stabilizer simulators have been for qubits.

Finite-size quantum spin systems can be magnetized by the application of a symmetry-breaking field, but in general their symmetry is expected to be restored once the field is turned off adiabatically. Recently (F. Caleca et al., arXiv:2412.15493) we have shown that systems of half-integer spins with an odd number of sites and a parity-preserving Hamiltonian can retain a finite magnetization, hence exhibiting spontaneous symmetry breaking (SSB) at finite size. Here we generalize this phenomenon to spin chains whose low-energy physics (in zero field) realizes a Luttinger-liquid phase. We observe that odd-sized chains can exhibit a phenomenon of finite-size quasi-SSB, in which a net sub-extensive magnetization, $M \sim N^{1-1/(4K)}$ is retained, where $N$ is the number of sites and $K$ the Luttinger exponent. Interestingly, the states prepared by turning off the symmetry-breaking field quasi-adiabatically display scalable spin squeezing – namely stronger the bigger the system – regardless of the parity of $N$. The scaling of the squeezing parameter is dictated again by the Luttinger exponent, $\xi_R^2 \sim N^{-1+1/(2K)}$. This result shows that scalable quantum correlations with metrological significance, associated typically with high-dimensional systems, can be found as well in gapless one-dimensional ones; and they are a direct consequence of the critical nature of Luttinger liquids.

This work presents a study of continuous-variable quantum key distribution (CV-QKD) protocols over fast-fading channels, typically found in free-space communication links. Two eavesdropping models are considered to evaluate their security under collective attacks: Holevo bound average (HBA) and covariance matrix average (CMA). In the HBA approach, the Holevo bound is averaged over the channel transmittance. In contrast, the CMA method calculates the Holevo bound from the average covariance matrix. Analytical expressions are developed for both strategies. The two methods also differ in how they calculate the mutual information between the legitimate parties. The results demonstrate that the SKR is significantly influenced by how you treat channel fluctuations, highlighting the importance of choosing the model that better describes the actual implementation of the protocol.

Moments of ensembles of unitaries play a central role in quantum information theory as they capture the statistical properties of dynamics of systems with some form of randomness. Indeed, concepts such as approximate $t$-designs arise when comparing how close an associated moment operator of a given unitary ensemble is to that of another, reference ensemble. Despite the importance of moment operators, their properties have not been as explored for quantum channels. In this work we develop a theoretical framework to compute moment operators for ensembles of quantum channels, for all moment orders $t$, with a special focus on determining ensembles that can be used as points of reference. By deriving hierarchies between ensembles, via inequalities of their moment operator norms, we give them operational meaning, and define useful concepts such as that of channel $t$-designs. Finally, we perform theoretical and numerical studies which show that different types of noise can decrease the norm of the moment operators (e.g., depolarizing noise), as well as increase it (e.g., amplitude damping noise), and generalize noise-induced concentration phenomena to channel-design-induced phenomena. Along the way, we find a block-orthogonal basis for permutations, which greatly simplifies our analyses, and may be of independent interest in moment calculations.

Matter-wave optics is often viewed as a linear analogue of photonics, where noninteracting particles are coherently split, diffracted, and recombined, and interference arises from single-particle coherence. In ultracold quantum gases, however, interactions are intrinsic and can rival or exceed kinetic and optical energy scales. This drives matter-wave optics into a nonlinear regime: diffraction and momentum distributions become interaction-dependent, interference contrast degrades or collapses, and revival dynamics appear. In the mean time, interactions can generate squeezing and entanglement, enabling sensitivities beyond the standard quantum limit. We showcase representative examples - covering diffraction, splitting, and interferometry - that illustrate how interactions reshape the basic elements of matter-wave optics and open new opportunities for nonlinear quantum technologies.

We investigate ergotropy dynamics in a graphene-based quantum battery modeled as a four-level spin–valley system under different dissipative environments. The battery is charged via a Gaussian pulse and subsequently evolves under amplitude damping, dephasing, and both Markovian and non-Markovian reservoirs. We find that amplitude damping, while inducing energy loss, can stabilize non-passive steady states with finite ergotropy, whereas pure dephasing suppresses coherence and eliminates work extraction. On the other hand, non-Markovian memory slows ergotropy loss and enables partial recovery through information backflow. These results identify coherence and reservoir memory as essential resources for enhancing the long-time performance of graphene quantum batteries.

A significant challenge in quantum computing (QC) is developing learning models that truly align with quantum principles, as many current approaches are complex adaptations of classical frameworks. In this work, we introduce Quantum Hyperdimensional Computing (QHDC), a fundamentally new paradigm. We demonstrate that the core operations of its classical counterpart, Hyperdimensional Computing (HDC), a brain-inspired model, map with remarkable elegance and direct correspondence onto the native operations of a QC. This suggests HDC is exceptionally well-suited for a quantum-native implementation. We establish a direct, resource-efficient mapping: (i) hypervectors are mapped to quantum states, (ii) the bundling operation is implemented as a quantum-native averaging process using a Linear Combination of Unitaries (LCU) and Oblivious Amplitude Amplification (OAA), (iii) the binding operation is realized via quantum phase oracles, (iv) the permutation operation is implemented using the Quantum Fourier Transform (QFT), and (v) vector similarity is calculated using quantum state fidelity measurements based on the Hadamard Test. We present the first-ever implementation of this framework, validated through symbolic analogical reasoning and supervised classification tasks. The viability of QHDC is rigorously assessed via a comparative analysis of results from classical computation, ideal quantum simulation, and execution of a 156-qubit IBM Heron r3 quantum processor. Our results validate the proposed mappings and demonstrate the versatility of the framework, establishing QHDC as a physically realizable technology. This work lays the foundation for a new class of quantum neuromorphic algorithms and opens a promising avenue for tackling complex cognitive and biomedical problems intractable for classical systems.

We study the discrete nonlinear Schrodinger equation with competing powers (p,q) satisfying 2 <= p < q. The physically relevant cases are given by (p,q) = (2,3), (p,q) = (3,4), and (p,q) = (3,5). In the anticontinuum limit, all intrinsic localized modes are compact and can be classified by their codes, which record one of two nonzero (smaller and larger) states and their sign alternations. By using the spectral stability analysis, we prove that the codes for larger states of the same sign are spectrally and nonlinearly (orbitally) stable, whereas the codes for smaller states of the alternating signs are spectrally stable but have eigenvalues of negative Krein signature. We also identify numerically the spectrally stable codes which consist of stacked combinations of the sign-definite larger states and the sign-alternating smaller states.

We introduce QPU micro-kernels: shallow quantum circuits that perform a stencil node update and return a Monte Carlo estimate from repeated measurements. We show how to use them to solve Partial Differential Equations (PDEs) explicitly discretized on a computational stencil. From this point of view, the QPU serves as a sampling accelerator. Each micro-kernel consumes only stencil inputs (neighbor values and coefficients), runs a shallow parameterized circuit, and reports the sample mean of a readout rule. The resource footprint in qubits and depth is fixed and independent of the global grid. This makes micro-kernels easy to orchestrate from a classical host and to parallelize across grid points. We present two realizations. The Bernoulli micro-kernel targets convex-sum stencils by encoding values as single-qubit probabilities with shot allocation proportional to stencil weights. The branching micro-kernel prepares a selector over stencil branches and applies addressed rotations to a single readout qubit. In contrast to monolithic quantum PDE solvers that encode the full space-time problem in one deep circuit, our approach keeps the classical time loop and offloads only local updates. Batching and in-circuit fusion amortize submission and readout overheads. We test and validate the QPU micro-kernel method on two PDEs commonly arising in scientific computing: the Heat and viscous Burgers' equations. On noiseless quantum circuit simulators, accuracy improves as the number of samples increases. On the IBM Brisbane quantum computer, single-step diffusion tests show lower errors for the Bernoulli realization than for branching at equal shot budgets, with QPU micro-kernel execution dominating the wall time.

We introduce Quantum Orthogonal Separable Physics-Informed Neural Networks (QO-SPINNs), which replace selected dense hidden transformations in physics-informed PDE models with quantum-compatible orthogonal maps. A pyramidal reconfigurable-beamsplitter circuit realizes these maps with $\Theta(d)$ circuit depth. Under the stated signed-readout access model and fixed per-call failure probability, a width-$d$ map has sequential access-cost upper bound $\mathcal O(d\log d/\epsilon^2)$, which is asymptotically smaller than the $\Theta(d^2)$ cost of conventional direct dense multiplication when $\epsilon(d)^{-2}\log d=o(d)$. Because unary state preparation discards magnitude, we retain each hidden-state norm classically and restore it after readout, yielding the exact map $Wh$ under ideal readout. We study this construction in two complementary settings: forward PDE regression and a norm-controlled residual model for representation geometry and uncertainty quantification. In the norm-controlled model, structural orthogonality yields explicit bi-Lipschitz bounds, while finite-readout analysis relates sufficient reconstruction precision to a prescribed physical-coordinate resolution through trained-model constants. This makes the access-cost condition assessable for trained checkpoints, although favorable fixed-resolution scaling across widths is not established. A post-hoc Bayesian-linear covariance provides uncertainty from the same frozen predictor, and cross-PDE transfer shows that strong distance-awareness can persist even when absolute coverage calibration fails. All Quantum Orthogonal (QO) layers are evaluated by classical simulation: the results therefore characterize a quantum-compatible structured neural primitive rather than demonstrating a practical quantum-hardware advantage.

The presence of interfaces in semiconductor devices substantially hinders thermal transport, contributing disproportionately to the overall thermal resistance. However, approaches to enhance interfacial thermal transport remain scarce without changing the interface structure, as the intrinsic electron and phonon properties of constituent materials set an upper limit. Here, we find a new thermal transport pathway, electronic heat tunneling, to enhance interfacial thermal conductance through metal-insulator-semiconductor junctions. By applying photoexcitation or bias voltage, we observe remarkable thermal conductance increases in operando, opening a new channel for efficient interfacial heat dissipation. The electron quantum tunneling pathway is parallel to conventional phonon-mediated interfacial thermal transport, and violates the Wiedemann-Franz law since this pathway deviates from the paradigm of diffusive transport. Moreover, we develop a tunneling mismatch model to describe the enhanced thermal conductance, originating from tunneling heat flux. Our Letter demonstrates a previously unexplored heat transport mechanism to enhance thermal conductance, bypassing the need for interface engineering. These findings emphasize the essential need to understand semiconductor thermal properties under realistic operating conditions.

This is a preliminary version. Improvements and additional analysis will be included in a revised manuscript. We investigate the charge-state stability of individual nitrogen-vacancy (NV) centers in weakly doped HPHT IIa diamond containing sub-ppm concentrations of boron and nitrogen. Using Ti/Al coplanar electrodes on an oxygen-terminated surface, we study how applied electric fields and optical excitation jointly govern NV charge conversion. By combining voltage-dependent photoluminescence, real-time charge-state monitoring, laser-power saturation with spectral decomposition, and time-resolved measurements, we reveal that electric fields several micrometers from the contacts significantly increase the NV- population and enhance spin readout. At low excitation powers, the NV- population evolves on minute timescales following compressed-exponential kinetics, consistent with slow space-charge rearrangement in ultra-insulating diamond. Under pulsed excitation, we observe hundreds-of-nanoseconds NV-/NV0 conversion driven by hole capture, which is strongly suppressed by applied bias. Our results demonstrate that residual boron acceptors play a key role in determining charge-state stability and show how electrical bias can reliably stabilize NV- in weakly doped bulk diamond.

The characterization of high-dimensional quantum entanglement is crucial for advanced quantum computing and quantum information algorithms. Traditional methods require extensive data acquisition and suffer from limited visibility due to experimental noise. Here, we introduce a sparsity-driven framework to enhance the detection and certification of high-dimensional entanglement in spatially entangled photon pairs. By applying $\ell_1$-regularized reconstruction to sample covariance matrices obtained from measurements on photons produced via spontaneous parametric down-conversion (SPDC) measurements, we enhance the visibility of the correlation signal while suppressing noise. We demonstrate, using a position-momentum Einstein-Podolsky-Rosen (EPR) entanglement criterion, that this approach enables certification of an entanglement dimensionality that cannot be achieved without regularization. Our method is scalable, simple to use and compatible with existing quantum-optics platforms, thus paves the way for efficient, real-time analysis of high-dimensional quantum states.

One of the major advances of quantum technology is the engineering of complex quantum channels in lattice systems that paves the way for a variety of novel non-equilibrium phenomena. For a boundary driven lattice with such engineered quantum channels, the analysis of the full counting statistics of current across boundaries has received limited attention. In this work, we consider a boundary driven free fermionic lattice with carefully engineered correlated gain and loss channels and obtain the cumulant generating function of the steady-state particle current. We also discuss the limit for simplifying the correlated gain-loss channel to a local gain-loss channel and obtain the average current and its fluctuation in such cases. Generally, in the presence of gain-loss, the current statistics are different at the two ends of the lattice. Hence, for both local and correlated gain-loss, we devise the conditions for which the statistics can coincide, giving rise to a $\mathcal{PT}$ symmetric balanced gain-loss scenario. A striking difference between the correlated gain-loss and their local counterpart is the emergence of nonreciprocity in the system and we observe that it has a dramatic impact in the current as well as fluctuations. Our work therefore provides interesting insights about the importance of engineered dissipators in boundary driven systems.

Small clusters of $^4$He atoms are benchmark systems for universal few-body physics near the unitary limit. We study these systems using a finite-cutoff effective field theory calibrated to low-energy observables from the realistic LM2M2 potential. The chosen two-body cutoff reproduces both the atom–atom scattering length and effective range, while a regulated three-body interaction is adjusted to the trimer and tetramer ground-state energies. We distinguish total binding energies from the one-atom separation energies $S_A^*=B_A^*-B_{A-1}$ of the shallow excited states. Ground-state energies through $A=8$ are reproduced at the few-percent level, but threshold-sensitive observables are less accurate. In particular, the calculated tetramer separation energy is $S_4^*=2.85$~mK, compared with $0.92$–$0.96$~mK in converged LM2M2 calculations, and the atom–trimer scattering length differs substantially from modern LM2M2 benchmarks. For $A\ge5$, the available excited-state benchmarks are sparse and strongly method dependent. Comparison with another soft-core interaction suggests that the discrepancies primarily reflect short-distance physics and higher-order operators omitted from the finite-cutoff Hamiltonian rather than numerical convergence of the stochastic variational calculation. Finite-cutoff EFT therefore provides an efficient description of ground-state systematics, whereas shallow excited states and atom–cluster scattering require a controlled higher-order and cutoff-dependence analysis.

Long-lived storage of single photons is a fundamental requirement for enabling quantum communication and foundational tests of quantum physics over extended distances. While the implementation of a global-scale quantum network requires quantum storage times on the order of seconds to minutes, existing photonic quantum memories have so far been limited to subsecond lifetimes. Although $^{151}$Eu$^{3+}$:Y$_2$SiO$_5$ crystals exhibit substantially extended spin coherence times at the `magic' magnetic field, the concomitant weak optical absorption has until now prevented single-photon storage. Here, we overcome this challenge by integrating a noiseless photon echo protocol–which makes full use of the crystal's natural absorption for photonic storage–with a universally robust dynamical decoupling sequence incorporating adiabatic pulses to efficiently protect delocalized spin-wave excitation, enabling long-lived quantum storage at the `magic' magnetic field. At a storage time of 5.6 s, we achieve a time-bin qubit storage fidelity of 88.0 $\pm$ 2.1%, surpassing the maximum fidelity attainable via classical strategies. Our device reaches a $1/e$ storage lifetime of 27.6 $\pm$ 0.5 s, enabling single-photon-level storage for 42 s with a signal-to-noise ratio greater than unity. This work establishes photonic quantum memory in the minute-scale regime, laying a solid foundation for global-scale quantum network and deep-space quantum experiments.

Quantum computers based on cold-atom arrays offer long-lived qubits with programmable connectivity, yet their progress toward fault-tolerant operation is limited by the relatively low fidelity of site-selective local control. We introduce an artificial-intelligence (AI) framework that overcomes this limitation. Trained on atom-laser dynamics, a deep neural network autonomously designs composite pulses that improve local control fidelities tenfold while remaining compatible with existing control hardware. We further demonstrate the robustness of these pulses against optical aberrations and beam misalignment. This approach establishes AI-trained pulse compilation for high-fidelity qubit control and can be readily extended to other atom-like platforms, such as trapped ions and solid-state color centers.

Quantum error correction is essential for fault-tolerant quantum computing. However, standard methods relying on active measurements may introduce additional errors. Autonomous quantum error correction (AQEC) circumvents this by utilizing engineered dissipation and drives in bosonic systems, but identifying practical encoding remains challenging due to stringent Knill-Laflamme conditions. In this work, we utilize curriculum learning enabled deep reinforcement learning to discover Bosonic codes under approximate AQEC framework to resist both single-photon and double-photon losses. We present an analytical solution of solving the master equation under approximation conditions, which can significantly accelerate the training process of reinforcement learning. The agent first identifies an encoded subspace surpassing the breakeven point through rapid exploration within a constrained evolutionary time-frame, then strategically fine-tunes its policy to sustain this performance advantage over extended temporal horizons. We find that the two-phase trained agent can discover the optimal set of codewords, i.e., the Fock states $\ket{4}$ and $\ket{7}$ considering the effect of both single-photon and double-photon loss. We identify that the discovered code surpasses the breakeven threshold over a longer evolution time and achieve the state-of-art performance. We also analyze the robustness of the code against the phase damping and amplitude damping noise. Our work highlights the potential of curriculum learning enabled deep reinforcement learning in discovering the optimal quantum error correct code especially in early fault-tolerant quantum systems.

We investigate the multiphoton pair production in circularly polarized field via two level model. There appears obvious discrete ring structures in the momentum distribution of the created particles, in which the ring radius is mainly controlled by the number of the photons absorbed in the creation with the energy conservation and could also be modulated by the spin of the created pair. These multiphoton rings become narrower when both of the pair particles' spin are aligned with the direction of the field rotation, and become broader if both spin are antiparallel to that direction. This spin-modulation can be simply understood with the angular momentum conservation, as less orbital angular momentum from the absorbed photons would be transferred to the created particles if their spins are aligned with the field rotation. The orbital angular momentum of the created particles is manifested as the vortex structure in the phase of the momentum distribution, and valued as the topological charge of this phase vortex. We also study the spin entanglement between the created particles, and reveal that the entanglement becomes stronger with the increase of the particles' transverse momentum, and gets sharp peak in the transition between different multiphoton rings, where the topological charge of the phase vortex is changed.

The quantum Wasserstein distance (W-distance) is a fundamental metric for quantifying the distinguishability of quantum operations, with critical applications in quantum error correction. However, computing the W-distance remains computationally challenging for multiqubit systems due to exponential scaling. We present a machine learning framework that efficiently predicts the quantum W-distance by extracting physically meaningful features from quantum state pairs, including Pauli measurements, statistical moments, quantum fidelity, and entanglement measures. Our approach employs both classical neural networks and traditional machine learning models. On three-qubit systems, the best-performing Random Forest model achieves near-perfect accuracy ($R^2 = 0.9999$) with mean absolute errors on the order of $10^{-5}$. We further validate the framework's practical utility by successfully verifying two fundamental theoretical propositions in quantum information theory: the bound on measurement probability differences between unitary operations and the $W_1$ gate error rate bound. The results establish machine learning as a viable and scalable alternative to traditional numerical methods for W-distance computation, with particular promise for real-time quantum circuit assessment and error correction protocol design in NISQ devices.

We present and open source Extraferm, a quantum circuit simulator tailored to chemistry applications. More specifically, our simulator can compute the Born-rule probabilities of samples obtained from circuits containing particle number-conserving matchgates and controlled-phase gates. We support both approximate and exact calculation of probabilities, and for approximate probability calculation, our simulator's runtime is exponential only in the magnitudes of the circuit's controlled-phase gate angles. This makes our simulator useful for simulating certain systems that are beyond the reach of conventional state vector methods. We demonstrate our simulator's utility by simulating the local cluster unitary Jastrow (LUCJ) ansatz and integrating it with sample-based quantum diagonalization (SQD) to improve the accuracy of molecular ground-state energy estimates with negligible computational overhead. More generally, we highlight a regime in which our simulator achieves substantially superior latency scaling and exponentially superior memory scaling over a tensor network simulator and a state vector simulator. As an efficient and flexible tool for simulating quantum chemistry circuits, our simulator enables new opportunities for enhancing near-term quantum algorithms in chemistry and related domains.

According to a common view, Everettian quantum mechanics (EQM) is a local theory because it avoids nonlocal action at a distance, and this is an important point in EQM's favor. Unlike collapse theories, EQM does not allow an action on one system to change the reduced density matrix (RDM) of a remote entangled system - a clear case of nonlocal action. However, EQM does allow an action on one system to change the global state of the system and its remote entangled partners. We argue that such changes should also count as nonlocal actions, meaning EQM is not local after all. First, we consider an argument to the contrary, which deems such global changes to be mere extrinsic changes, whereas nonlocal action requires intrinsic changes to the remote system. We respond that the intrinsic-extrinsic distinction is problematic and cannot hold the weight of this argument. We then try to clarify when actions that change global states count as nonlocal actions. We argue that it is when the global states are essential explanatory mechanisms of the theory. In EQM, the global state is needed to explain why, in an anti-correlated Bell state, Alice's measuring spin-up ensures that she encounters only the branch where Bob measures spin-down.

Amplitude damping fundamentally limits qubit lifetimes by irreversibly leaking energy and information into the environment. Standard Wiseman–Milburn feedback offers only modest improvement because it acts on a single measured quadrature and its corrective drive is degraded by loop delay. We introduce a compact hybrid upgrade with two components: (i) a coherently coupled ancilla qubit that receives the homodyne current and feeds back quantum-coherently on the system, recovering information from both field quadratures and intentionally engineered to decay much faster than the system; and (ii) a lightweight supervised predictor that forecasts the near-future homodyne current, phase-aligning the correction to overcome hardware latency. A Lindblad treatment yields closed-form effective decay rates: the ancilla suppresses the emission channel by a cooperativity factor, while the predictor further suppresses the residual decay in proportion to forecast quality. Using IBM-scale parameters (baseline \(T_1 = 50~\mu\mathrm{s}\)), numerical simulations surpass the W–M limit, achieving \(\sim 3\!-\!4\times\) longer \(T_1\) together with improved population retention and integrated energy. The method is modular and hardware-compatible: ancilla coupling and supervised prediction can be added to existing W–M loops to convert leaked information into a precise, time-advanced corrective drive. We also include a detailed, student-friendly derivation of the effective rates for both ancilla-assisted and prediction-enhanced feedback, making the impact of each design element analytically transparent.

Quantum optimization allows for up to exponential quantum speedups for specific, possibly industrially relevant problems. As the key algorithm in this field, we motivate and discuss the Quantum Approximate Optimization Algorithm (QAOA), which can be understood as a slightly generalized version of Quantum Annealing for gate-based quantum computers. We delve into the quantum circuit implementation of the QAOA, including Hamiltonian simulation techniques for higher-order Ising models, and discuss parameter training using the parameter shift rule. An example implementation with Pennylane source code demonstrates practical application for the Maximum Cut problem. Further, we show how constraints can be incorporated into the QAOA using Grover mixers, allowing to restrict the search space to strictly valid solutions for specific problems. Finally, we outline the Variational Quantum Eigensolver (VQE) as a generalization of the QAOA, highlighting its potential in the NISQ era and addressing challenges such as barren plateaus and ansatz design.

QuaRT is a Python library for quantum simulation of radiative transfer in astrophysical and cosmological problems. It features a novel angular redistribution methodology for lattice Boltzmann methods which improves the isotropy of simulations of objects such as stars in non-scattering media.

In this work, we introduce an experimentally viable scheme to enhance the simultaneous estimation precision of the couplings $G_{mc}$ and $G_{mb}$, with a particular focus on the performance of heterodyne detection. By comparing simultaneous and individual estimation strategies, we demonstrate that the simultaneous approach offers a notable advantage in our system. To support this, we compute the quantum Fisher information matrices (QFIMs) based on the symmetric logarithmic derivative (SLD) and the right logarithmic derivative (RLD). Our results show that the quantum Cramér Rao bound (QCRB) associated with the RLD is consistently lower than that of the SLD, indicating superior estimation precision. From a physical standpoint, this improvement reflects the system's enhanced capacity to encode, transfer, and extract quantum information while allowing optimal control of fundamental interactions. We show that increasing the Rabi frequency, cavity loss rate, and the average number of photons and phonons, combined with reduced mechanical damping and temperature, enhances the system's sensitivity to the coupling parameters. These mechanisms act on the available quantum resources, such as entanglement, squeezing, and state purity, leading to more precise estimations. Furthermore, our analysis reveals that under certain conditions, heterodyne detection can closely approach the ultimate precision set by the QFIM. This suggests that a measurement strategy based on heterodyne detection can offer an efficient and practical route for estimating the couplings $G_{mc}$ and $G_{mb}$, paving the way for high precision hybrid quantum sensors.

The Hawking effect amplifies fluctuations in the vicinity of horizons, both in black holes and in analogue platforms. Here, we consider a polariton simulator and numerically examine the stimulated Hawking effect using a coherent probe incident on the horizon from the exterior. We implement an experimentally realistic effective spacetime that supports a quasinormal mode (QNM) in the vicinity of the horizon. We find that the stimulated Hawking effect manifests as transmission into a negative-energy Bogoliubov channel inside the horizon, consistent with pseudo-unitary Bogoliubov scattering. Moreover, transmission across the horizon peaks at the QNM frequency. The computed spectral signatures provide a practical guide for future experimental investigations of the Hawking effect and its interplay with QNMs, an open question in quantum field theory in curved spacetime.

The study of spins and particles on graphs has broad applications, from the dynamics of interacting systems on networks to combinatorial problems. Here, we study the large-$n$ limit of the $O(n)$ model on graphs, which is considerably more challenging than on regular lattices, as the loss of translational invariance gives rise to an infinite set of saddle point constraints in the thermodynamic limit. We show that the free energy at low and high temperature $T$ is determined by the spectrum of two fundamental graph-theoretic objects: the Laplacian matrix at low $T$ and the Adjacency matrix at high $T$. Their interplay is studied across several classes of graphs. For regular lattices the two coincide. We obtain an exact solution on trees, where the Lagrange multipliers interestingly depend solely on the number of nearest neighbors. We further contrast these classical results with those for a quantum spin model on an exemplary tree. For decorated lattices, the singular part of the free energy is governed by the Laplacian spectrum, whereas this is true for the full free energy only in the zero temperature limit. Finally, we discuss a bipartite fully connected graph to highlight the importance of a finite coordination number in these results.

Sixth-Generation (6G) wireless networks will interconnect billions of resource-constrained devices and time-critical services, where classical, fixed, and heavy cryptography strains latency and energy budgets and struggles against large-scale, pre-computation attacks. Physical-Layer Security (PLS) is therefore pivotal to deliver lightweight, information-theoretic protection, but still requires strong, reconfigurable confusion components that can be diversified per slice, session, or device to blunt large-scale precomputation and side-channel attacks. In order to address the above requirement, we introduce the first-ever chaos-lifted substitution box (S-box) for PLS that couples a $\beta$-transformation-driven dynamical system with dyadic conditional sampling to generate time-varying, seedable 8-bit permutations on demand. This construction preserves uniformity via ergodicity, yields full 8-bit bijections, and supports on-the-fly diversification across sessions. The resulting S-box attains optimal algebraic degree 7 on every output bit and high average nonlinearity 102.5 (85% of the 8-bit bound), strengthening resistance to algebraic and linear cryptanalysis. Differential and linear profiling report max DDT entry 10 (probability 0.039) and max linear probability 0.648, motivating deployment within a multi-round cipher with a strong diffusion layer, where the security-to-efficiency trade-off is compelling. Our proposed reconfigurable, lightweight S-box directly fulfills key PLS requirements of 6G networks by delivering fast, hardware-amenable confusion components with built-in agility against evolving threats.