Papers

The distillability conjecture for two-copy four-by-four Werner states has been an open problem in quantum information for years. We investigate the conditions under which the conjectured inequality becomes an equality. For all known cases where the conjecture has been verified, we characterize the saturation conditions and show that equality forces the matrices $A$ and $B$ to be two-by-two block-diagonal. In particular, several previously obtained partial results, including the cases of one normal matrix, unitary similarity between $B$ and $-A$ or $-A^T$, and anti-diagonal block structures, are reduced to this common block-diagonal structure. We also employ a manifold optimization method, which provides numerical evidence that the two-by-two block-diagonal structure is essential for saturating the inequality. Furthermore, we prove that the identified saturation points are critical points of the objective function on the constraint manifold.

Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes. In this work, we introduce a training framework that reduces this cost to logarithmic in the number of qubits, making gradient-based QNN optimisation feasible on near-term hardware at increasing scales. Our framework combines three co-designed ingredients: (i) a structured, subspace-preserving Butterfly circuit architecture with $O(n \log n)$ parameters and logarithmic depth; (ii) a layer-wise training strategy that confines on-hardware optimisation to one small, well-structured layer at a time; and (iii) a parallelised parameter-shift rule that exploits the commuting structure within each Butterfly layer to extract all gradients in a constant number of circuit executions. Together these reduce the number of distinct circuit evaluations per optimisation step from $O(n^2)$ to $O(\log n)$. We validate the framework on clinical data imputation using the MIMIC-III electronic health record dataset, a demanding benchmark sensitive to optimisation instability and model variance. Hybrid classical-quantum models are trained directly on IonQ Forte Enterprise trapped-ion hardware at 16 qubits without performance degradation relative to ideal or noisy simulation and via tensor-network simulation at 32 qubits, with 32-qubit inference executed on hardware. The resulting models match or exceed strong classical neural baselines in downstream patient survival prediction while exhibiting reduced variance across runs, demonstrating that the proposed framework enables practical, scalable QNN training under realistic hardware constraints.

Quantum computing presents a promising method to overcome the efficiency and memory constraints in large-scale mechanical problems, with numerous successful applications demonstrated in fluid mechanics. However, solid mechanics problems usually require irregular grids for spatial discretization, due to the Lagrange formulations and complex boundaries, which makes the quantum simulation of the system matrix, e.g., the mass or stiffness matrix which is often referred to as the Hamiltonian in quantum computing, difficult to be effectively conducted. This study proposes a voxel-based quantum computing method (VBQC) for the quantum simulation of Hamiltonians in solid mechanics. VBQC applies voxel grids to discretize the spatial domain, thereby enabling the system matrix to exhibit the tridiagonal fractal property. Based on this property, the system matrix can be decomposed into three groups of fundamental matrices, $\mathbf{k}_{n}$, $\mathbf{c}_{n}$, and $\mathbf{q}_{n}$. This decomposition process is referred to as the KCQ decomposition. By integrating the KCQ decomposition with the quantum Fourier transform and the quantum multiplexer, VBQC enables efficient quantum simulation of Hamiltonians in solid mechanics. Three specific solid problems with different dimensions and numbers of variables are applied to preliminarily verify the correctness of the proposed VBQC for solid mechanics problems.

Quantum low-density parity-check (QLDPC) codes are promising candidates for practical low-overhead quantum memories. For large-scale fault-tolerant quantum computation, we further need logical processing methods for QLDPC codes. In this work, we construct full extractors—surgery systems capable of measuring arbitrary logical Pauli operators on a code block—for several hypergraph product (HGP) codes. These extractors enable logical processing via Pauli-based computation (PBC) without the compilation overhead observed in prior works. Moreover, our extractors have sizes between $47\%$ and $80\%$ of the base HGP codes, and the extractor-augmented codes can be supported on fixed-connectivity hardware with maximum qubit degree ten. Our approach involves assembling many partial extractors with verifiable fault tolerance into a single full extractor. For a distance $10$ HGP code, circuit-level noise simulations yield logical measurement error rates of approximately $10^{-6}$ at a physical error rate of $0.1\%$. These results demonstrate that extractor architectures, when designed in the fixed-connectivity setting, can achieve the space efficiency of QLDPC codes without introducing compilation overhead compared to surface-code PBC architectures.

Field-programmable gate arrays provide a high-performance solution for real-time signal processing in emerging quantum and photonic technologies. We present an FPGA-based fast feedforward system, that incorporates a high quantum efficiency fully fibre based homodyne detector, to enable low-latency signal processing critical for continuous variables (CV) measurement-based quantum information processing (MB-QIP) protocols. CV MB-QIP typically relies on adaptive measurements and/or displacements via feedforward to achieve scalability and universality, but existing implementations typically handle these operations in post-processing, limiting real-time applicability. Our system performs signal acquisition, conditioning, and logic operations in real-time, meeting the tight latency requirements of photonic quantum computing protocols. The detector exhibits a large clearance of 15 dB at 1 GHz with 4 mW linear oscillator and quantum efficiencies of >95% with a total system latency of 196 ns. This work highlights the role of FPGAs in bridging the gap between theoretical models and physical implementations in photonics-based technologies

We propose a scheme for piston control in a two-ion quantum device with motion confined to orthogonal axes. In this system, one ion plays the role of a ”classical” piston driven by the Coulomb interaction with the other ion, whose quantum motion is controlled through modulation of its trapping potential. The stationary state is determined self-consistently, taking quantum effects into account. We identify a narrow quantum regime of the ground state connecting two broad classical regimes. We further design inverse-engineering protocols to control the motion of the ”classical” ion. The proposed control scheme provides a useful route toward controlled piston dynamics in microscopic quantum devices.

Two-dimensional Chern insulators support topologically protected, chiral edge currents, and these can be detected in experiments with ultracold atoms in optical lattices. It has previously been shown that one can populate a selected group of edge states of a Chern insulator by transferring particles from a reservoir. Here, we numerically investigate the effect of performing an instantaneous, projective measurement on the reservoir before the reservoir is discarded. In this way, the final state of the system is pure and described by a wavefunction. We also show that quite likely measurement outcomes can help to increase the final number or percentage of particles in the chiral edge states through postselection. Without the measurement step, the physics can be described in terms of single-particle physics. The measurement significantly complicates the description. By appropriately rewriting the analytical expressions, we show that measurement probabilities, expectation values, averages of expectation values, and purity can nevertheless be computed from the state before the measurement in a way that scales only linearly with the number of lattice sites for a fixed number of particles. This enables us to investigate a setup with, for instance, 14 particles and 198 lattice sites numerically. The approach applies generally to noninteracting, fermionic models that conserve the number of particles.

Topological state transfer in Fock-state lattices has been demonstrated with high speed using sinusoidal profiles of coupling, yet the underlying reason has remained unclear. A global adiabatic criterion (GAC) is developed to bound the infidelity by the mean and variance of the nonadiabatic factor. The GAC reveals that the key to fast transfer is not a constant energy gap but the vanishing nonadiabaticity variance. For power-law coupling profiles, the variance vanishes only for the sinusoidal shape, which is thus globally optimal. Incorporating experimental decoherence parameters, it is predicted that the optimal transfer duration for a five-photon state is 161 ns, far shorter than 600 ns used in the experiment, reducing time by over 73% while increasing transferred photons by 29%. The optimal duration follow a simple linear scaling with photon number, providing a practical guideline. Through constructing an alternative constant-gap coupling family, it is confirmed that a constant gap alone is not sufficient for fast topological photon transfer. The essential condition is uniformity of nonadiabaticity. This work offers a rigorous explanation for the observed speed and a general framework for fast topological photonics engineering.

We give detailed analysis and circuit design of structure-preserving quantum algorithms for second-order linear evolutionary PDEs, including parabolic equations and hyperbolic equations with mixed Dirichlet, Neumann, and periodic boundary conditions and source terms. While prior quantum algorithms usually neglect the stability problem from the PDE-to-ODE reduction, our method-of-lines approach investigates the boundary lifting via Coons interpolation and boundary-aware discretization, so that the resulting semi-discrete systems are stable and compatible with efficient quantum ODE primitives. For the parabolic problem, we use a diagonal similarity transform to ensure the semi-discrete generator must have a positive semi-definite Hermitian part, and then solve the resulting ODE system by the optimal linear combination of Hamiltonian simulation (LCHS). For the hyperbolic problem, we rewrite the semi-discrete equation as an equivalent first-order system and solve it by Hamiltonian simulation. We implement our quantum algorithms with explicit block-encoding constructions and circuit implementations, as well as demonstrating the end-to-end complexity bounds together with spatial and quadrature error estimates. We conduct classical numerical experiments on the convection-diffusion equation, inhomogeneous heat equation, and Klein-Gordon equation to validate our structure-preserving analysis and algorithmic constructions.

The non-Hermitian skin effect (NHSE) represents one of the most distinctive phenomena in non-Hermitian physics. Here, we uncover a new drag-induced NHSE mechanism in interacting Bose–Fermi mixtures where only bosons, but not fermions, experience asymmetric hopping.We show that strong Bose–Fermi interactions enable fermions to inherit boundary accumulation through correlated bound states.The interplay of interactions, quantum statistics, and non-Hermitian dynamics gives rise to an interaction-induced blockade mechanism, leading to highly asymmetric fermionic transport.We demonstrate that the drag-induced NHSE is dynamically stable and propose a feasible realization in ultracold Bose–Fermi mixtures with Floquet-engineered asymmetric tunneling.Our results establish a qualitatively distinct route to the NHSE, where non-Hermitian localization is not generated by direct nonreciprocal hopping of the localized particles themselves, but emerges through the interaction-mediated transfer of non-Hermitian dynamics between different quantum species. This mechanism further provides a potential route toward species-selective control of localization and transport in hybrid quantum matter.

We develop an exact spectral-response theory for the Grover-type iterate \(W_T=D_\xi\exp(-\mathrm{i} T H)\), in which the evolution generated by a diagonal Ising Hamiltonian is used directly as a continuous phase oracle. An energy-grouped recurrence and its generating-function solution show how the empirical characteristic function determines the position, width, height, and saturation time of an energy-selective resonance. For an annealed Gaussian density of states, a high-density-tail resonance containing \(M\) configurations is reached after \(\Theta(\sqrt{2^n/M})\) oracle calls with success probability \(\Theta(1)\), giving a quadratic query improvement over independent uniform sampling with classical energy evaluation. For correlated random Ising spectra, overlap-dependent covariances lead to a realization-dependent resonance shift with root-mean-square scale \(O(n^{-3})\), parametrically larger than the resonance width, and can also reduce the peak height. The shift is both an algorithmic detuning and a coherent probe of sample-specific spectral fluctuations whose ensemble statistics reflect the Ising overlap structure. Spectral symmetrization and iterative calibration can remove or compensate the resonance-center displacement for prescribed-energy targeting. We also clarify the relation to designed spectral filters and the precision and coherence requirements of this asymptotic primitive.

Entanglement purification is an essential component of quantum repeaters, as it can improve the fidelity of the distributed entangled states and mitigate the effects of the noisy channel. Successful purification yields entangled states with increased fidelity, whereas failed events can still retain residual entanglement that remains usable for further purification when the error rates of the two degrees of freedom (DOFs) are unbalanced. In this paper, we demonstrate a single-copy entanglement purification scheme based on hyperentanglement using silicon chips and experimentally observe the presence of residual entanglement. Leveraging the reconfigurability of integrated photonics, our scheme ensures that, under bit-flip noise acting on the two DOFs, residual entanglement suitable for further purification can always be obtained, regardless of which DOF has the higher error rate. Our results demonstrate the advantages of integrated photonics for quantum information processing and provide guidance for the optimized utilization of entanglement resources in on-chip entanglement purification and future quantum repeater systems.

Large-size cat states are especially meaningful and fundamental for exploring the quantum-to-classical transition, as well as promising resources for quantum metrology and fault-tolerant quantum computation. However, amplifying the magnitude of cat states remains challenging because of the growing fragility under decoherence. We propose to generate large cat states by using the dynamical invariant of hybrid qubit-bosonic systems under Hermitian or non-Hermitian time-dependent Hamiltonian. It is a study with the universal quantum control (UQC) theory, in which the system dynamics is analyzed in the ancillary picture via a unitary transformation conditional on the qubit state. The controllable dynamics that can be encoded in the evolution of the dynamical invariant is presented by the Heisenberg equation, which imposes constrains on the Hamiltonian. When the qubit is prepared in a balanced superposed state, the bosonic mode can evolve deterministically from the vacuum state to the cat state of a mean photon number over $120$. In the Hermitian case, the generation is perfect; and in the non-Hermitian case, the fidelity is over $0.962$. Our protocol can also be applied to the generation of the intrinsic cat states and the four-component cat states of large size. Through the preparation of macroscopic quantum states, our work essentially advances UQC to hybrid discrete-continuous variable systems.

Engineering proximity effects in twisted van der Waals heterostructures offers a powerful platform for designing electronic properties. While theoretical predictions of quantum interference in transition metal dichalcogenide-encapsulated graphene can selectively control the spin-orbit coupling component, experimental realizations have remained elusive. Here, we report pure valley-Zeeman spin-orbit coupling in monolayer graphene, achieved by encapsulation between two parallel twisted WSe$_2$ monolayers. We observed a symmetry-enforced reordering of Landau levels, which is driven by the competition between the fixed valley-Zeeman energy and the magnetic-field-dependent cyclotron energy. This reordering is characterized by a transition from symmetry-broken states in the quantum Hall effect to a restored fourfold degeneracy with integer or half-integer quantum Hall sequences. We also demonstrate the ability to completely quench the proximity spin-orbit coupling by tuning the encapsulated geometry.

A major open problem in quantum communication complexity is whether quantum protocols can be exponentially more efficient than classical protocols for computing total Boolean functions; the prevailing conjecture is that they cannot be so. In a seminal work, Razborov (2002) resolved this question for AND-functions of the form $$ F(x,y) = f(x_1 \land y_1, \ldots, x_n \land y_n), $$ when the outer function $f$ is symmetric, by proving that their bounded-error quantum and classical communication complexities are polynomially related. Since then, extending this result to all AND-functions has remained open and has been posed by several authors. In this work, we settle this problem in a strong way. We show that for every Boolean function $f$, the bounded-error quantum and classical deterministic communication complexities of the function $f \circ \mathrm{AND}_2$ are polynomially related, up to polylogarithmic factors in $n$. We prove this by showing that both are characterized–up to polynomial loss–by the logarithm of the De Morgan sparsity of $f$. Our results build on the recent work of Chattopadhyay, Dahiya, and Lovett (2025) on structural characterizations of non-sparse Boolean functions, which we extend to resolve the conjecture for general AND-functions.

The fractional Schrödinger equation is studied with a kinetic energy that slightly deviates from the usual nonrelativistic form. The harmonic oscillator and the Kepler problem are both treated in the context of small perturbations. The usual perturbation theory is used and compared with the envelope theory. The corrections to the unperturbed Hamiltonians are analytical. The bounds computed with the envelope theory are good for the harmonic oscillator but poorer for the Kepler problem.

We study the application of Gaussian Boson Sampling (GBS) to the densest k-subgraph problem (DkSP). GBS with hard post-selection suffers from poor sampling efficiency due to strict cardinality constraints. To address this limitation, we introduce effective classical post-processing strategies that transform, otherwise discarded, near-k samples into feasible solutions. A comprehensive set of simulations is carried out, demonstrating that these approaches achieve near-optimal solution quality while improving sampling efficiency by approximately 4X compared to post-selection on community-structured graphs, and also post-selection often fails to reach the optimal solution on sparse random graphs even with large number of samples. Furthermore, the proposed methods perform on par with, and in some cases outperform, established classical approaches for graphs up to moderate size. Overall, the results indicate that while GBS with post-selection alone is insufficient, its combination with lightweight classical refinement can be highly effective. This underscores the potential of hybrid quantum-classical frameworks and positions GBS as a promising sampling primitive for combinatorial graph optimization.

We present a protocol which allows for arbitrary optical quantum states to simultaneously carry and transmit classical data, without sacrificing the integrity of either the quantum or classical information. Our scheme encodes classical information via displacements in the phase space prior to transmission and retrieves each classical symbol via a Gaussian continuous-variable teleportation. The original quantum state is then restored by guessing the the original displacement and performing the appropriate inverse operation. In the limit of sufficiently high classical signal and high squeezing, we show that our scheme is capable of perfectly reconstructing both the input classical signal and the input quantum state without loss of coherence. An example is given in terms of the transmission of a dual-rail Bell state.

Non-Gaussian quantum states are essential resources for continuous-variable quantum information processing and for metrology. Among these, multi-photon added coherent states bridge classical and non-classical behaviors; however, their generation typically relies on small photon numbers and probabilistic heralding schemes. Here, we propose a protocol for the post-selection free generation of high fidelity multi-photon added coherent states using the photon blockade effect in a driven Kerr nonlinear resonator, where such states emerge naturally during the dynamics. We demonstrate that high-fidelity states can be prepared by optimizing the external drive power and the interaction time. Furthermore, we show that the protocol is robust under realistic experimental conditions, achieving fidelities of $\approx 99\%$ with current state-of-the-art parameters. Our results unlock a deterministic route to complex non-classical states using well-established quantum optical platforms.

This perspective article analyzes the potential and critical challenges of employing quantum optimization algorithms to investigate phase transitions in quantum many-body systems during the Noisy Intermediate-Scale Quantum era. The simulation of strongly correlated systems is frequently intractable on classical computers due to the exponential growth of the Hilbert space and the fermionic sign problem. In this context, we review and compare the performance of traditional Variational Quantum Algorithms, such as the Variational Quantum Eigensolver and the Quantum Approximate Optimization Algorithm, against emerging heuristic approaches, specifically Feedback-based Quantum Algorithms, such as FALQON. We explore the applicability of these methods in the study of open phenomena in condensed matter physics, including Deconfined Quantum Criticality, strange metals, Many-Body Localization, topological phase transitions, and quantum spin liquids. We discuss how fundamental operational bottlenecks, notably expressibility- and noise-induced barren plateaus, severely compromise gradient-based optimization. We conclude that deterministic feedback-guided methods provide geometrically more robust trajectories for navigating the energy landscape of these systems, arguing that further advancement in the field will rely on deep hybridization and physics-informed circuit co-design towards fault tolerance.

Strategic decision-making among many agents under incomplete information is central to economics, security, and multi-agent artificial intelligence (AI). Computing equilibria in such settings is challenging because the joint type-action space grows exponentially with the number of players. In binary-type, binary-action Bayesian games with $n$ players, an explicit representation over type-action profiles requires $O(2^{2n})$ entries, making direct linear-programming (LP) formulations increasingly costly as $n$ grows. We propose a hybrid quantum-classical framework for approximating Bayes correlated equilibrium (BCE) using a parameterized quantum circuit (PQC). The PQC represents the conditional distribution over joint actions using $O(nL)$ trainable parameters, where $L$ denotes the circuit depth; for the largest trained setting, $n=8$ and $L=2$, this corresponds to $48$ trainable angles. Each player count is trained independently by maximizing expected social welfare with a penalty on positive aggregated BCE obedience violations. On a strategically coupled Bayesian congestion game with $n=2,4,6,8$ players, feasible PQC solutions attain higher welfare than MCCFR and DCFR product-strategy baselines while satisfying $\epsilon_{\max}\leq10^{-3}$, where $\epsilon_{\max}$ denotes the maximum positive aggregated BCE obedience violation. Across five independent runs per setting, all runs are feasible for $n=2,4,6$, while four of five are feasible for $n=8$. PQC welfare remains below the exact LP optimum, with the absolute gap increasing with $n$, while classical state-vector simulation prevents PQC training beyond eight players. These results demonstrate the use of a compact PQC parameterization for approximate equilibrium computation and quantify its welfare, feasibility, and classical simulation scaling on the studied benchmark.

We study a kinematical consequence of the Hopf-algebraic momentum composition law in $\kappa$-Minkowski spacetime. The same curved momentum space can be described in different coordinates. In the bicrossproduct basis the ordered-plane-wave labels are the translation-generator eigenvalues, so the relevant map is one-to-one. In the classical basis, instead, the translation eigenvalues $P_\mu$ are nonlinearly related to the ordered-plane-wave labels $p_\mu$. This relation can fail to be globally one-to-one in a high-momentum region. When a given classical-basis four-momentum admits more than one real auxiliary preimage, the branch-sensitive quantity $P_+\equiv P_0+P_4=\kappa e^{p_0/\kappa}$ enters the coproduct and resolves the branches in two-particle states. Imposing the vanishing total-momentum constraint therefore gives branch-dependent $\kappa$-deformed back-to-back momentum correlations. In a single-branch regime this is just a deformed correlated product, while in a multibranch regime a state specified only by $P_\mu$ can be expanded into distinct auxiliary branches. If $P_\mu$ are taken as the directly meaningful momenta, the physical content is the resulting deformed correlation pattern. If the auxiliary variables $p_\mu$ are assigned operational meaning, the same constrained state can be interpreted as a superposition over different auxiliary branches. We also compare this structure with standard regular self-adjoint nonrelativistic minimal-length models and find no analogous smooth local two-real-branch inversion on their physical domains.

Noise remains the primary obstacle to realizing quantum advantage, continuously degrading the resources that enable quantum technologies. Purification aims to reverse this degradation by extracting high-fidelity resources from noisy ensembles, yet its conventional formulation is intrinsically static, acting only after noise has taken effect. Here we instead recast purification as a dynamical task, introducing a spatiotemporal framework that distributes interventions across the noise process. This formulation reveals operational capabilities inaccessible to existing approaches and gives rise to forward-assisted purifications that extend achievable performance. In certain regimes, a single-copy protocol already exceeds what can be achieved with up to 50 copies under conventional purification, demonstrating a significant overhead in required resources. Beyond these gains, our framework circumvents no-purification theorems within conventional protocols, including for Bell-state ensembles, thereby enabling purification previously considered impossible and pointing toward an efficient route to mitigating noise in quantum systems.

We solve the two-body problem of ultracold heteronuclear atoms in a quasi-two-dimensional (quasi-2D) geometry. The quasi-2D confinement is realized by a harmonic trap along the longitudinal ($z$-) direction, with different trap frequencies for the two atoms, as in many current experiments on ultracold heteronuclear gases. As a consequence, the longitudinal center-of-mass (CoM) motion is coupled to the relative motion, which significantly complicates the two-body problem. We solve this problem exactly and derive the 2D scattering length $a_{\rm 2D}$, the 2D effective range parameter $R_{\rm 2D}$, and the bound-state energies, as functions of the $s$-wave scattering length and effective range of the two atoms in free three-dimensional (3D) space. We show that multiple 2D scattering resonances can be induced by the coupling between the longitudinal CoM and relative motion. Around these resonances, $a_{\rm 2D}$ varies rapidly with the 3D scattering parameters, while $R_{\rm 2D}$ is strongly enhanced. Since the effective pairwise interaction in quasi-2D ultracold gases is determined by i.e., the two-body scattering amplitudes and bound-state energies, our results can be used for manipulating the effective 2D interatomic interaction in quasi-2D ultracold heteronuclear gases by tuning the confinement frequencies and the 3D scattering parameters.

Quantum Fisher information (QFI) is a fundamental quantifier in quantum metrology, determining the ultimate precision achievable in parameter-estimation protocols through the quantum Cramér-Rao bound. However, direct evaluation of the QFI generally requires detailed knowledge of the density matrix, making it increasingly demanding as the Hilbert-space dimension grows. In this work, we investigate the extent to which the QFI of multipartite quantum systems can be predicted from a limited set of experimentally accessible quantities using support vector regression (SVR). By comparing different physically motivated features, we identify a dominant feature set governing QFI and show that the predictive power of collective spin moments alone decreases as system size and consequently Hilbert-space dimension grows. We demonstrate that QFI is governed primarily by the interplay between collective covariance and low-order spectral moments of the density matrix. Our results identify the physically relevant information sectors governing the QFI and demonstrate that accurate estimation of metrological sensitivity can be achieved from a restricted set of experimentally accessible quantities without requiring full quantum-state tomography.

This software update extends Omnisolver with omnisolver-bruteforce, a first-class plugin for exact exhaustive search of QUBO and Ising instances on CUDA-enabled GPUs. The update contributes three components: packaging of the single-GPU brute-force kernel of [Computer Physics Communications 260, 107728, 2021] as a first-class Omnisolver plugin with a uniform Python and CLI interface, a Ray-based distributed framework that splits the search into $2^{k}$ fixed-variable subproblems and dispatches them across multiple GPUs and hosts, with a controller that gathers and merges partial results, and numerical stabilization of the fast float32 ground-state path through compensated incremental updates, periodic exact energy re-anchoring, and best-buffer refresh. Stabilization activates automatically for $N \geq 40$ leaving the public sampler API is unchanged. On dense random Ising instances we measure exhaustive solve times up to $N=60$ on $8\times$ NVIDIA H100 (96 GB) GPUs, reaching $\approx\!3.15$ days at $N=60$ in close agreement with the empirical $t_{N+1}=2\,t_N$ doubling rule, with the distributed sampler reaching the full $\approx\!8\times$ speedup over a single H100 from $N\!\gtrsim\!44$ onwards. The plugin thereby serves as a practical ground-truth oracle for state-of-the-art classical heuristics such as the Simulated Bifurcation Machine.

We regularize the Aichelburg-Sexl shock-wave geometry for massless particles by smearing the point-like source over a string-inspired length scale $l_0$. A radial extension of the transverse geometry admits a zero-throat Einstein-Rosen interpretation. For a photon wave packet of longitudinal extent $L$, the regularized gravitational self-energy of a transversely separated pair is $E_{\rm GSE}\sim \frac{4G(\hbar\omega)^2}{c^4L} \ln\!\left(\frac{d^2}{l_0^2}\right)$, where $d$ is the transverse separation. The factor $1/L$ strongly suppresses the interaction, giving gravitational stability times greater than $10^{30}$ years for optical photons. We also examine an effective two-dimensional entanglement-entropy description of the shock-wave geometry. The entropy construction reproduces the same dependence on the scales $d$, $L$, and $l_0$, and can be calibrated to reproduce the normalization of the direct self-energy calculation. However, we find that the gravitationally induced excess entropy, and hence the self-energy inferred from this prescription, is the same for Bell-entangled and unentangled photon pairs having identical energy-momentum distributions. This agrees with the direct geometrical calculation, since the classical two-shock metric depends on the stress-energy tensor but not on the polarization entanglement. The present construction should therefore be understood as a semiclassical ER-like geometry motivated by ER=EPR, rather than as a complete realization of the conjecture. A geometry whose connectivity tracks the amount of quantum entanglement would require a quantum-gravitational description sensitive to state-dependent correlations beyond the one-point stress-energy tensor.

Discrete-time quantum walks are powerful platforms for simulating quantum transport and information processing. Here we introduce a walker on a one-dimensional lattice whose motion is controlled by three entangled coins, each initialized with the Hadamard gate, aiming to maximize information flow. The walker moves only when all three coins yield the same outcome (HHH or TTT), thus coupling the 8-dimensional coin Hilbert space to the position degree of freedom. By analyzing fully separable, fully entangled (GHZ-type) and intermediate initial states, and using the von Neumann entropy of reduced subsystems, we compute the mutual information $I(C;P;t)$ between coin and position. The results show that initial three-partite entanglement accelerates the growth of mutual information by up to 18% after ten steps (when compared to the lower of the two separable states), although it exhibits short-term non-monotonic dynamics due to quantum interference. For the first time, we introduce a tunable parameter $\alpha$ (amplitude of non-displacement states) and show that the GHZ state reaches a maximum of mutual information at $\alpha \approx 0.71$ - a key finding for optimal control of information flow. Finally, an integrated photonic implementation using polarization, spatial modes and time bins is proposed, where $\alpha$ can be tuned with nonlinear or electro-optic elements. A scalable numerical framework (Python code) for simulations up to $t = 5$ steps is provided. Our findings establish three-partite entanglement as a dynamical resource for maximizing information flow and spatial spreading, with direct applications in quantum state transfer, entanglement-assisted sensing and programmable photonic quantum processors.

We present a new method for the efficient imaginary time evolution of quantum many-body wavefunctions represented by matrix product states (MPS). We first show that logarithmic time grids are sufficient to resolve long imaginary time dynamics, yielding an exponential reduction in the number of time steps compared with standard approaches. We then show that A-stable implicit time-stepping methods for ordinary differential equations allow stable propagation for any time step size. The resulting scheme requires only matrix-vector products and linear solves, standard operations in the MPS toolbox. We validate our approach with two examples: a Heisenberg spin chain, which we use to demonstrate a speedup of several orders of magnitude over the standard time-dependent variational principle method with uniform time steps, and a single-site Anderson impurity model with a metallic bath, for which propagation to large imaginary times allows one to observe the exponential dependence of the Kondo temperature on the interaction strength.

Photonic graph states with advanced topologies can enable measurement-based quantum computing, distributed quantum sensing, and quantum interconnects. However, the efficient generation of photonic graph states is limited by the probabilistic nature of photonic entangling operations and the exponential dependence of generation rate on resource cost. In this work, we study photonic graph state synthesis as a cost-aware decomposition problem, exploiting local Clifford (LC) equivalence to identify more synthesis-friendly representations of the target graph state before decomposition. Specifically, we propose Cost-aware Fusion-based Decomposition (CFD), a three-stage heuristic framework that decomposes a target graph state into ring, star, and linear motifs, and assembles them via Type-I fusion operations to minimize fusion overhead and physical-qubit consumption. We further show that selecting the LC-equivalent graph state with the minimum number of edges provides a highly effective proxy for near-optimal synthesis: in many cases it matches the best generation rate observed within the LC equivalence class under CFD, and in most remaining cases it remains close to it. Numerical evaluations on graph state orbit data and 2D and 3D lattice graph states demonstrate that CFD achieves up to 84.6% reduction in resource overhead compared to baseline constructions, and yields improvements in photonic generation rate spanning multiple orders of magnitude. These results suggest that combining structure-aware motif decomposition with LC equivalence is a practical and scalable strategy for photonic graph state synthesis.