Papers

Exceptional points (EPs) can produce striking responses, but locating one does not reveal how much of its dynamics an excitation accesses, which responses a detector distinguishes, or whether a missing signal is absent or undetected. We introduce Krylov tomography, a preparation- and measurement-aware framework that uses time-resolved data and models with stated uncertainty limits to answer these questions, while also bounding offset-induced departures from exact-EP behavior. As an explicit illustration of the general framework, we present finite-precision simulations of a red-sideband optomechanical model, whose second-moment coherence sector contains a third-order EP, certifying preparation-sensitive access to two and three directions. Krylov tomography thus bridges EP structure and finite-precision measurements, providing a general framework for non-Hermitian systems.

Future quantum communication infrastructures will need to serve heterogeneous users on shared physical channels: short-range, high-throughput links favor Continuous-Variable Quantum Key Distribution (CV-QKD), while long-reach, high-loss links remain the domain of Discrete-Variable QKD (DV-QKD). Wavelength-division multiplexing (WDM) of the two protocols on a common channel would address both regimes simultaneously, but their markedly different noise sensitivities make coexistence non-trivial and, to date, experimentally untested. Here we report the first simultaneous operation of two independent CV- and DV-QKD systems on a common optical channel, using standard C-band DWDM filters at 1550.12 nm (CV) and 1545.32 nm (DV). We demonstrate joint operation on both optical fiber and a 620 m urban daylight free-space link. On fiber, the two systems exhibit the expected complementarity, crossing over at 7.56 dB of channel loss where both deliver $\sim$1.43 Mbit/s; in daylight free-space, both sustain Mbit/s key rates under time-varying atmospheric attenuation. Across all configurations we observe no measurable multiplexing-induced penalty in QBER or excess noise. These results establish hybrid CV-DV WDM as a practical building block for heterogeneous quantum communication networks, where metropolitan high-throughput users and long-reach backbone links can be served on a single physical infrastructure.

Quantum embedding methods offer a promising route to extend quantum chemical calculations to large multiscale systems by treating a chemically important subsystem at a high level of theory while describing its surrounding environment at an affordable level. The methods are also quite relevant for quantum computing approaches based on hardware with limited resources. Here, we present an iterative projection-based embedding framework combined with VQE, in which the environment density is allowed to respond self-consistently to the refined electronic structure of the embedded subsystem described by VQE. Unlike conventional one-shot approaches where the environment remains frozen after the initial orbital optimization, the proposed iterative scheme alternates between the VQE-level treatment of the subsystem and a mean-field-level refinement of the environment until mutual self-consistency is achieved. The convergence behavior of the scheme is first examined using several small test systems. Its practical applicability is then demonstrated with a composite system with a CH2NH molecule sandwiched by two benzene rings, with the C=N dihedral angle rotating from 0 to 90 deg. The iterative procedure consistently converges within ~10 iteration steps across all tested geometries, yielding energies below the conventional one-shot embedding results. The converged results well reproduce the fully correlated reference energy employing the same active space, and the resulting potential energy surface with respect to the dihedral rotation is also in good agreement with the reference one. These results demonstrate that our iterative embedding framework is numerically robust and physically sound, yielding a self-consistent and reliable treatment of inter-subsystem correlation. We expect that its formulation will be particularly compatible with the emerging paradigm of quantum-classical hybrid computing.

Generative AI creates an assessment challenge in quantum software education: a student can provide a homework notebook to ChatGPT and request a completed submission. This study examined whether introductory Qiskit homework could remain autogradable while requiring students to run, review, and discuss results rather than banning AI. Three packages were tested: seeded basis-state circuits with bit flips and customized measurement mappings; Quantum Fourier Transform followed by inverse-transform recovery; and seeded Deutsch-Jozsa with customized oracle masks. The designs used personalization, simulator execution, JSON submissions, hidden references, circuit metrics, reflections, and optional IBM Quantum execution. For each package, one student-visible instance was tested in 50 separate ChatGPT sessions, yielding 150 sessions overall. Every final artifact was executed and passed its grader. Nine sessions were fully archived; none required operator code changes or correction of quantum logic. Under the study's operational definition, each tested instance had zero observed ChatGPT-resiliency. Seeds changed parameters rather than task structure, expected results remained derivable from visible assignment logic, scaffolding exposed key solution steps, and hidden grading verified output consistency without establishing independent authorship or understanding. Because one instance was repeated for each package, the results do not establish solvability for every seed or possible Qiskit assessment. The tested personalized, execution-oriented take-home designs therefore did not prevent successful completion under a minimally engaged-student workflow. Correct artifacts should be complemented by direct assessment through supervised modification, oral defense, prediction, and transfer tasks.

Hole spins in silicon quantum dots are a promising platform for quantum computing due to their strong intrinsic spin-orbit coupling (SOC), which enables fast, all-electrical control. However, this coupling also increases their susceptibility to charge noise, thereby limiting coherence times. Moreover, holes in silicon are also affected by hyperfine interactions with residual nuclear spins in the silicon substrate, introducing a non-negligible source of low-frequency noise. Here, we implement a phase-modulated concatenated continuous driving (CCD) technique for hole spin qubits to suppress low-frequency noise through microwave phase modulation. This approach stabilizes Rabi oscillations and extends the oscillation decay time compared to the conventional method. Furthermore, by defining a qubit in the CCD frame, we achieve coherent control while simultaneously protecting the qubit from noise, confirming coherence protection during gate operations. These results demonstrate a viable route toward noise-robust hole spin qubits.

Dissipation is usually regarded as a source of decoherence that suppresses quantum interference and localization. Here we show that suitably engineered dissipation can instead be used to select localized or extended states in a strictly non-disordered one-dimensional lattice. The underlying clean lattice has spatially inhomogeneous hopping and supports both extended bulk states and localized boundary states, including an algebraically localized bound state in the continuum. We introduce a nonlocal bond jump operator with a tunable relative phase and show that this phase selectively favors eigenstates with different spatial phase correlations. As a result, the long-time density matrix can be steered toward sectors dominated by localized or extended Hamiltonian eigenstates without changing any Hamiltonian parameter. The microscopic origin of the selection is quantified by the fraction of site pairs separated by a distance $l$ that are phase matched with the dissipative channel. We further characterize the dissipative quench through the quantum fidelity and show that the selected character of the steady state can persist after the dissipation is removed. Our results establish phase-selective bond dissipation as a route to controllable state preparation and transport manipulation in non-disordered lattices.

In this paper we employ the open quantum system framework with the influence functional formalism to non-perturbatively analyze the response of an Unruh-DeWitt detector modeled as a harmonic oscillator which interacts with a massless scalar field in impulsive plane wave spacetimes. Subtracting the Minkowski results, we obtain the expectation values for $\langle Q^2 \rangle$, $\langle P^2 \rangle$, and $\langle \{Q,P\} \rangle$, along with transition probabilities $P_{0\rightarrow 1}$ from ground state to the first excited state of the detector due to the influence of the wave. Explicit calculations are performed for both a pure gravitational wave (vanishing Ricci tensor) and a null electromagnetic wave (vanishing Weyl tensor). In both scenarios, the wave suppresses excitation transitions. Since our approach is non-perturbative, we are able to consider cases with both weak and strong coupling constants.

We establish upper and lower bounds for relative entropy contraction of generalized quantum depolarizing channels and semigroups. Our bound provides tight first order asymptotic of the contraction rate in terms of the dimension constant. One side estimate are based on sharp reverse ratio and convexity of relative entropy of two states, which can be derived from the recently introduced Hockey-Stick quantum $f$-divergence, and also independently, Bogoliubov–Kubo–Mori quantum Fisher information metric. The other side follows from the existence of index achieving pure state with respect to a general conditional expectation. Our results extend to the complete entropy contraction rate, tensor stable estimates for product dynamics. Examples include quantum depolarization, dephasing, and compact group symmetrization, with consequences for the decay rate of coherence and asymmetry.

When the control field of a qubit, a polarization state, or a spin-$\tfrac12$ system is swept through a level degeneracy, its direction traces an open curve on the Bloch sphere whose endpoints are antipodal, and the geodesic rule for the open-path geometric phase becomes ambiguous: infinitely many geodesics close the path, and different closures enclose different solid angles. We resolve this ambiguity in closed form. A coordinate-free monopole connection defines the open-path solid angle $\Omega[C]$ intrinsically, and displacing the degeneracy by $\epsilon\uhat$ closes the path with enclosed solid angle $\Omega(\epsilon\uhat)=\Omega[C]+2\alpha+O(\epsilon)$, where $\alpha$ is the azimuth of the transverse part of $\uhat$ measured from the principal normal of the control curve at the crossing. The identity between geometric phase and enclosed solid angle therefore holds for exactly one closing geodesic—the great circle in the osculating plane ($\alpha=0$)—supplied by the curvature at the degeneracy. Berry's $\pi$ invariant under reversal of the displacement and the values $\pm\pi/2$ under a reflection symmetry follow as corollaries, and the pure-state limit of the finite-temperature Uhlmann phase selects the osculating-plane closure automatically, turning the heuristic closing rules of the open-path literature into a computable prescription.

Reading an optical quantum reservoir costs repetitions growing super-exponentially with feature order: what it can afford is set by its detector, not its optics. For reservoirs encoding data in a parametric pump's phase, one conservation law fixes what is readable and what it costs. Pairwise photon exchange conserves an integer phase charge: across an ensemble of input masks, order-D readout reaches exactly the assemblies of at most D unit-charge kernels; degree-one homodyne readout is universal for fading-memory functionals along weak-squeezing families, at shot cost polynomial in accuracy; and at fixed squeezing no finite degree reaches every sector, at a computable distance. Nonlinearity sits in the optics, not the detector, whose per-shot variance is fixed at every order. In a hardware-faithful digital twin-no device was built-the rule is visible: on an open RF corpus a displacement-encoded control at identical photon number loses 17.7 accuracy points, as the charge algebra predicts.

We present a variational quantum linear solver (VQLS) for the Poisson equation built on an exact block encoding of the discrete Laplacian, and demonstrate its performance on physically motivated benchmarks. Unlike LCU-based VQLS where the number of distinct circuits required per cost-function evaluation is $\mathcal{O}(L^2)$, where $L$ is the number of terms in the LCU decomposition of the discrete Laplacian operator, this approach requires only a single circuit for cost evaluation. We further empirically demonstrate that the choice of classical optimizer materially affects where the variational optimization ceases to make progress. The solver is benchmarked on three problems: a Poisson equation with sinusoidal forcing and a steady-state heat conduction problem with a localized Gaussian source, both with Dirichlet boundaries, and the pressure-Poisson equation of a two-dimensional lid-driven cavity flow, in which the solver is invoked once per time step under Neumann boundary conditions.

Superconducting circuits exhibit remarkable potential for constructing large-scale quantum simulation and computation systems, featuring numerous qubits, extended coherence time, and precise control. Nevertheless, the growing number of signal cables poses a challenge in dilution refrigerators due to space and heat load constraints. To overcome this issue, we experimentally implemented an optically-assisted transmission line as an alternative to coaxial cables. By modulating microwave signals on laser intensities at room temperature and regenerating the signals at a cryogenic plate within the dilution refrigerator, we demonstrated full control of superconducting qubits using photocurrent. We demonstrate and benchmark both single-qubit and two-qubit gates on frequency tunable transmon qubits, achieving fidelities of 99.915% $\pm$ 0.005% and 99.676% $\pm$ 0.041%, respectively, which have reached the requirement of the surface code.

Ensemble nitrogen-vacancy (NV) centers in diamond enable robust vector magnetometry in unshielded environments, yet deployment remains bottlenecked by complex calibration and a reliance on external data references. Conventional statistical machine learning requires an exorbitantly large volume of training data and suffers from severe simulation-to-reality mismatches. To address this, we introduce a physics-guided hybrid machine learning framework that embeds the Zeeman splitting directly into the learning pipeline. Our physics-guided model significantly reduces the average tracking error demonstrating a 372-fold precision improvement over purely statistical baselines. Furthermore, our hybrid architecture pairs a sparse physical measurement with scalable synthetic data generation, seamlessly incorporating real-world hardware non-idealities. When deployed to decode uncalibrated, raw experimental ODMR data, our framework delivers exceptional predictive accuracy for the scalar magnetic field. This work paves the way toward self-calibrated sensors while establishing a machine learning training method applicable to other data-scarce physical systems

We investigate the energetic and thermodynamic performance of an interacting two-qubit system serving as both a quantum battery and a quantum Otto heat engine. The working medium is described by an anisotropic Heisenberg Hamiltonian supplemented by a dipolar interaction, a symmetric spin–orbit interaction, and an external magnetic field. Within a unified microscopic framework, we first analyze a coherent unitary charging protocol and characterize the resulting energy-storage performance through the ergotropy, anti-ergotropy, charging power, storage capacity, and $\ell_1$-norm of quantum coherence. We investigate the effects of the dipolar interaction, temperature, and magnetic field on these quantities. We then employ the same working medium in a quantum Otto cycle and study the absorbed and released heat, net work, and thermodynamic efficiency as functions of the magnetic-field modulation, dipolar interaction, and temperature bias. A direct comparison between the two protocols reveals a pronounced contrast in their response to the dipolar interaction. In the investigated parameter regime, increasing the dipolar interaction substantially enhances the maximum ergotropy and storage capacity of the quantum battery, whereas the maximum work extracted per Otto cycle decreases. The Otto efficiency exhibits a nonmonotonic dependence on the dipolar interaction while remaining below the Carnot bound. These results demonstrate that an enhancement of quantum energy-storage capability does not necessarily imply an enhancement of heat-to-work conversion. Our findings highlight the complementary nature of quantum batteries and quantum heat engines and show how microscopic spin interactions can be used to control different forms of quantum energy conversion within the same physical platform.

Machine-learning force fields provide a promising route for accelerating molecular simulation by replacing expensive quantum-chemical calculations with differentiable models of molecular energies and atomic forces. However, learning accurate and energy-conserving forces remains challenging, especially when the model must capture both global energy trends and local potential-energy gradients from limited data. In this work, we propose a Hybrid Quantum SchNet architecture that integrates variational quantum circuit modules into the continuous-filter SchNet framework. Quantum modules are inserted into the filter generator, atom-wise update, and readout transformations, allowing quantum-enhanced feature mappings to contribute to distance-dependent interactions and atomic energy prediction while preserving the energy-gradient formulation of forces. The model is evaluated on eight MD17 molecular systems using 1000 training configurations per molecule. Compared with energy-only training, joint energy–force supervision substantially improves both energy and force prediction accuracy. Compared with energy-only training, joint energy–force supervision substantially improves both energy and force prediction accuracy. Averaged over the benchmark, the energy MAE decreases from 2.567 to 0.593 kcal mol$^{-1}$, while the force MAE decreases from 16.340 to 1.540 kcal mol$^{-1}$ Å$^{-1}$. Ablation experiments on ethanol further show that the performance of the hybrid model depends on the balance between quantum circuit width, circuit depth, and optimization stability. These results demonstrate that variational quantum circuits can be incorporated into neural force-field architectures and trained end-to-end to improve molecular energy and force prediction.

Identifying single photon emitters from large, inhomogeneous candidate populations is key to realizing many quantum applications. This requires measuring the emitters second order autocorrelation function, whose statistical reliability is fundamentally limited by acquisition time. Machine learning classifiers can accelerate identification from sparse data, but their performance relative to physics-based inference has not been systematically examined. Here, we introduce sequential Bayesian inference for single photon emitter classification and benchmark it against Levenberg-Marquardt fitting and a feedforward neural network. We use synthetic training and test data calibrated against real Hanbury Brown-Twiss measurements from hexagonal boron nitride emitters, enabling evaluation against an exactly known ground-truth emitter number under realistic noise and background conditions. All three approaches achieve high, near-perfect accuracy with sufficient integration time, but differ greatly in convergence rate and robustness under sparse photon statistics. The neural network is most robust at short integration times. The Bayesian classifier reaches near-perfect accuracy fastest, while retaining full physical interpretability. Levenberg-Marquardt fitting remains a valuable, fully interpretable method, achieving the highest recall despite being the slowest to converge. These results lead to several key conclusions. No single method dominates across all performance metrics. Relying on any one metric alone can give a misleading picture of classifier performance, particularly under sparse photon statistics. Physics-based and data-driven methods are complementary rather than competing approaches. Together, these findings provide practical guidance for selecting and combining classification strategies for scalable single-photon-source screening and other quantum-emitter characterization tasks.

Resolving extended incoherent objects below the diffraction limit poses an application-rich imaging challenge whose solution may enable a new generation of observational instruments and capabilities. In this work, we invoke a practical model for general imaging by approximating an arbitrary extended incoherent object as a finite grid of thermal point emitters parameterized by their brightnesses. We derive the quantum Fisher information matrix (QFIM) for simultaneous brightness estimation and show that the symmetric logarithmic derivatives weakly commute, indicating that the Helstrom bound furnishes the ultimate quantum limit on the estimation error for incoherent imaging. Furthermore, for deeply sub-diffraction scenes, we find numerical evidence of a gap between the Nagaoka-Hayashi (NH) bound and the Helstrom bound. This gap reveals that separable measurements, though more experimentally accessible, are insufficient to reach the quantum limit, and points to the prospective advantage of joint measurements acting on multiple state copies. Additionally, we show that spatial mode-demultiplexing (SPADE) often saturates the NH bound solidifying its status as a near-optimal separable measurement strategy that significantly outperforms direct imaging. Finally, we articulate two joint detection receivers implemented with bona fide quantum resources that asymptotically achieve the Helstrom bound.

It is well established that in time-dependent quantum systems, integrability preserving time-dependent interaction strengths are identical to the renormalization group (RG) trajectories of the corresponding static model when time `$t$' in the driven model is identified with the logarithm of the cutoff `$\log\Lambda$' of the static model. We refer to this integrability preserving driving as the RG protocol. In this work we extend the class of time-dependent integrable models to include non-Hermitian quantum models with time-dependent interaction strengths. Using the recently formulated generalized Bethe ansatz framework [P. R. Pasnoori, Phys. Rev. B 112, L060409 (2025)], we show that the constraints imposed by integrability are more general: The interaction strengths of the static model that flow in the RG follow the respective RG trajectories in the corresponding time-dependent model as described above. In addition, the interaction strengths of the static model that are RG invariant can either be constant or have a specific time-dependence in the corresponding time-dependent model which is constrained by integrability. Thus we establish that in the context of time-dependent non-Hermitian systems, the set of integrability preserving time-dependent strengths is larger than the set corresponding to the RG protocol.

The integration of distributed energy resources into power networks is accelerating. The resulting variability narrows operating margins, so a disturbance can cascade into a wide-area blackout. Controlled islanding arrests that propagation by splitting a compromised grid into self-sustaining islands that keep coherent generators together. Exact classical solutions become intractable as the bus count and island number grow. Gate-based quantum optimization provides a different route through this combinatorial space, although its reach is limited when one circuit carries every bus assignment, since qubit count and depth then follow grid size. In this study, a round-synchronous distributed quantum computing framework is developed for coherent controlled islanding under a fixed per-circuit qubit budget. Every round derives all regional subproblems from one frozen grid-wide snapshot, dispatches them to independent quantum backends at the same time, and merges the returned candidates classically into one globally evaluated update. Circuits executed in parallel therefore keep a constant size as the grid grows, and a round costs the slowest region rather than the sum of all of them. Benchmarking spans IEEE systems from 9 to 300 buses on simulation and on quantum processors of different architectures. The framework attains optimal and operationally feasible partitions on every platform under noise, even where compilation cost differs by nearly an order of magnitude. Bounding width in this way places grids beyond the reach of monolithic circuits within range of present devices and establishes a multi-backend baseline for quantum computing in large-scale power-system optimization.

Verification protocols provide cryptographic guarantees that the outcome of a delegated quantum computation is correct, a key requirement for scalable and trustworthy quantum computing. A central assumption underlying these protocols is secret independence: the noise affecting state preparation must not depend on the classical secrets that specify the prepared states. In earlier protocols this assumption was enforced by the physical separation of client and server; in the recent on-chip setting it becomes a property of the hardware itself, and one that high-fidelity devices do not automatically satisfy. We first make the requirement quantitative, deriving a bound that shows how residual secret dependence limits the size of any verifiable computation within this framework. Motivated by this, we introduce a calibration framework that enforces secret independence through pulse-level quantum control, producing state preparations that are simultaneously high-fidelity and secret-independent. We assess its performance on a nitrogen-vacancy centre system via emulation, showing that it reduces the secret dependence of the preparations by more than an order of magnitude compared to standard optimal control, and that it is robust against detuning and drive-amplitude errors commonly encountered in experiment. The framework operates entirely at the calibration stage and is platform-agnostic, offering a practical pathway towards verifiable quantum computing at scale.

A quantum $(r,\rho)$-locally recoverable code ($(r,\rho)$-qLRC) is a quantum code in which every qudit can be recovered from at most $r+\rho-1$ other qudits, even after $\rho-1$ additional erasures inside the recovery set. The bounds currently known for this class, namely the Singleton-like and the GG Singleton-like bounds, are alphabet independent and are therefore loose for small-to-moderate qudit dimensions. In this letter, we derive three alphabet-dependent upper bounds for pure $(r,\rho)$-qLRCs obtained through the Hermitian CSS construction: a Griesmer-like, a Plotkin-like, and a sphere-packing-like bound. We further establish the asymptotic hierarchy among these bounds and identify the relative-distance regions in which each of them yields the tightest rate constraint.

We develop a quantum algorithm for solving the dynamics of the nonlinear viscous Burgers equation. We apply the Carleman linearization procedure on the spatially discretized equation, followed by a padding scheme that allows implementation on qubit registers. Existing Carleman-based quantum algorithms commonly formulate the lifted linear differential equation in an oracle model. Here we decompose the padded generator into diagonal masks and reversible arithmetic permutations using the Permutation Matrix Representation (PMR), which we show to be naturally compatible with the Linear Combination of Hamiltonian Simulations (LCHS) algorithm. Under the assumptions required by LCHS - most importantly positive semidefiniteness of the Hermitian part of the linear generator, possibly after a stabilizing shift - the algorithm prepares a normalized quantum state proportional to the solution of the truncated lifted system; the stabilizing shift introduces an exponential postselection overhead, which we quantify and mitigate through a rescaling scheme. We show that our algorithm scales with the off-diagonal norm of the Carleman generator instead of the matrix norm, which can be advantageous for other generators that are diagonally dominant. We also extend the PMR scheme to general fluid equations that may contain higher-order derivatives or nonlinear terms, or may involve multiple fluid variables or spatial dimensions. The construction illustrates how PMR can serve as a convenient Hamiltonian-simulation primitive for a broader class of LCU-based algorithms.

Nonlinear multiphoton conversion processes are theoretically and experimentally challenging systems in quantum optics and related areas. These phenomena bring remarkable quantum properties and can be of fundamental relevance in quantum information and computation applications. Based on recent experimental advances and the possible uses in generalized three-photon squeezing, we investigate degenerate three-photon parametric down-conversion generated inside a cavity fed by a classical pump. In addition, we consider that the process is stimulated by a coherent driving mode and subjected to dissipation by spontaneous emission of the cavity. A treatment using phase-space methods and stochastic differential equations is applied and the stability conditions of its steady states are found. We find that the system does not exhibit multistability, instead it has a limited parameter region leading to a single stable steady branch, alongside multiple unstable branches. Focusing on the stable steady states and linearizing the dynamics around them, we calculate the spectral densities of the quadrature variables. We identify and characterize the squeezing of three-photon down-conversion. Moreover, since the quasi-probability differential equations of the higher-order multiphoton processes have higher-order derivatives, hindering their mapping into stochastic differential equations, we make use of a positive Wigner function approach to calculate two-dimensional spectral densities associated with three-time correlation functions, with the goal of studying non-Gaussian properties of the system.

We establish a general framework for developing fast sampling and counting algorithms for stoquastic spin systems at high temperature. Our framework is based on a rapidly mixing Markov chain for polymer models and a subcritical percolation process for sampling individual polymers. We apply our framework to obtain fast algorithms for approximating the partition function and sampling from the thermal distribution of (1) general stoquastic spin systems, (2) ferromagnetic Heisenberg models, and (3) antiferromagnetic Heisenberg models on bipartite graphs. For the Heisenberg models, we obtain an improved bound on the inverse temperature by using their respective cycle and loop representations.

Partial entanglement may provide enough connections in the universe to satisfy the definition of a small world. To investigate this possibility, we define a network of particles on a given space-like hyper-surface with a long-range link between any two particles that are connected by a chain of exchanged particles involving a limited number of interactions. Considering the mean free paths of particles in different regions of space and the resulting probability distributions of entanglement connections vs. distance, we find evidence of small-world or random network structure on all but the smallest scales - corresponding to stars and planets - on which other types of connections would need to be added to complete the small world picture.

A realist, anti-psi interpretation of quantum field theory is introduced, based on an expanded ontology that was inspired by the renormalization group. A goal will be to work with a classical non-interacting scalar field "toy model" in the canonical ensemble and show that an observer in this expanded ontology, under certain limiting conditions, will see a QFT. The inverse renormalization group (IRG) will be key in both interpreting this expanded ontology and furnishing novel examples of QFTs. This will lead to a definition of Planck's constant that flows as we head towards a fixed point, is expressed in terms of a temperature, and depends on the observer being studied in this expanded ontology. Our hope is that this approach will lead both to insights into quantum foundations and to prescriptions for developing new QFTs.

We consider the task of simulating the real-time dynamics of weakly interacting fermionic systems. In particular, we focus on computing the expectation value of a local observable $A$ at time $t$. By analyzing the convergence of the perturbative expansion in the interaction strength $\lambda$ for the Heisenberg-picture observable, we propose a polynomial-time algorithm for estimating this expectation value in the weakly interacting regime $\lambda |t|^{2D+1}=\mathcal{O}(1)$, when the Hamiltonian is geometrically local on a $D$-dimensional lattice. Importantly, this condition is independent of the system size. If the goal is instead to approximate the time-evolved observable in normalized Frobenius norm, we extend the convergence regime to $\lambda |t|=\mathcal{O}(1)$ with quasi-polynomial runtime. When the non-interacting part exhibits Anderson localization, our polynomial-time algorithm can be extended up to $\lambda |t|=\mathcal{O}(1)$, modulo polylogarithmic factors. Our algorithm brings together ideas from continuous-time QMC, diagrammatic QMC, and Majorana Propagation, but with a new Heisenberg-picture operator-growth analysis that makes the sampling complexity rigorously controllable. This leads to provably efficient classical algorithms in regimes where the interaction is weak enough that the sampling variance remains bounded independently of system size. Together, these results identify broad regimes in which weak interactions, locality, and localization can be leveraged to make real-time fermionic dynamics classically tractable.

Bit threads, arising as the convex dual of the minimal-surface formula for holographic entanglement entropy, are line-like structures with nontrivial bulk trajectories. Their physical picture has long been associated with Bell-pair interpretations, yet the meaning of their detailed trajectories, particularly their nonuniqueness, remains unclear. We propose to apply Coecke's notion of quantum information flow (QIF) in protocol-network geometry, developed within categorical quantum mechanics, to a holographic setup, and show that its trajectories within the discrete holographic bulk—the tensor network—obey the divergence-free and density-bound conditions of bit threads. From this process-centered perspective, bit-thread nonuniqueness becomes natural: for a given resource state, different protocols realizing the same distillation task can give rise to different QIF trajectories. We further analyze stabilizer-type holographic tensor networks using ZX string diagrams and show that QIF trajectories can probe finer entanglement structure beyond the level captured by entanglement entropy.

One of the most counterintuitive effects in the examination of quantum states is the phenomenon of superactivation, which describes the fact that a quantum state, which is useless for a specific task, may become useful when one considers multiple copies of it. This effect can be observed in the case of genuine multipartite entanglement, where local projections from multiple copies to the single-copy Hilbert space could significantly simplify the practical accessibility of its superactivation. We investigate how such projections behave in the limit of many copies, using the Hadamard map as an example, and studying different state families. We show that, in this scheme, for a fixed map, an optimal number of copies exists, beyond which the obtained entanglement decreases, highlighting the differences between entanglement distillation and local projection schemes.

For pure state unitary dynamics, the Mandelstam Tamm (MT) lower-bound functional compares the endpoint Fubini Study return angle with path averaged energy dispersion. Their distinct size and temporal dependences obscure the origin of period two MT structure in discrete time crystal (DTC) like dynamics and its relation to the spin response. For binary Floquet drives, we derive an exact segment resolved MT expression without assuming commuting segment Hamiltonians and apply it to a disordered Floquet Ising chain. Absolute and uniform summability of connected covariances of local energy terms implies an $O(\sqrt{L})$ upper bound on the path-averaged energy dispersion. Endpoint data for four system sizes are consistent with this leading behavior and support the corresponding rescaling of the MT functional. At a representative point in the finite-size region with a locked spin response, odd and even rescaled MT branches remain separated throughout the $10^2$ period observation window. The return angle alternates strongly, whereas the size-normalized path-averaged energy dispersion shows little discernible parity dependence, indicating that endpoint geometry is the main source of the branch splitting. Across the interacting parameter grid, the period-two MT component has a strong partial Spearman rank correlation with the locked spin response after controlling for pulse error and interaction strength. The rescaled MT functional characterizes the global return geometry of finite-size period-two dynamics and quantifies its association with the locked spin response.