Papers

The decay of out-of-time-ordered correlators (OTOCs) to a small residual value signals the scrambling of quantum information. This behavior is generic: it occurs in both chaotic and interacting integrable many-body systems, and therefore cannot by itself distinguish between them. In this paper we analytically study the late-time saturation value of a closely related observable, operator purity, and show that its scaling with system size robustly distinguishes chaotic from integrable systems. Our approach is based on the Mazur bound, which provides concrete scaling estimates and makes explicit why the locality of integrable charges matters. We support our findings with numerical simulations of one-dimensional spin chains.

Quantum kernel methods are promising for near-term quantum machine learning, but their training costs are driven by the quadratic scaling of Gram-matrix construction and the measurement cost of estimating individual kernel values. In this work, we investigate one-class anomaly detection as a resource-efficient operating regime for training non-variational quantum kernel models. Using the ULB Credit Card Fraud Detection dataset, we evaluate three quantum feature maps across training-set sizes and qubit counts. Power-law saturation models reveal strong diminishing returns with increasing training data, with 500-sample models retaining approximately 90–93% of their fitted asymptotic average precision (AP). We further observe no severe exponential concentration among the evaluated feature maps over the investigated qubit range, thereby avoiding the exponentially increasing measurement requirements associated with that regime. Finally, Nyström approximation reduces kernel construction from $\mathcal{O}(N^2)$ to $\mathcal{O}(Nm)$ evaluations, with QSVR retaining 94–97% of its exact-kernel AP using only five landmarks. For ZZ-QSVR, downsampling from 4,000 to 500 training samples and applying a five-landmark approximation decreases the number of required kernel evaluations from approximately $8.0$ million to $2,485$, a reduction of more than 3,200-fold, while AP decreases from $0.812$ to $0.737$. Under the minimum IonQ pricing assumption considered here, this corresponds to an illustrative reduction in quantum kernel training cost from approximately \$1.34 billion to \$417{,}480. These results demonstrate that training-set reduction and low-rank approximation can substantially reduce the resources required to train non-variational quantum kernel anomaly detectors while retaining meaningful anomaly-discrimination performance.

Inspired by classical Markov Chain Monte Carlo (MCMC) methods, dissipative quantum algorithms are an emerging class of methods for preparing quantum states on a quantum computer. They operate by simulating a dissipative process governed by a mathematical operator known as the Lindbladian. The performance of these algorithms can be limited by the sequential implementation of the Lindbladian's constituent jump operators, which leads to high circuit depth. To address this issue, this work analyzes a parallelization scheme to implement jump operators simultaneously. We leverage the structure of geometric locality to show that individual jump operators remain spatially localized. This exponentially reduces the circuit depth per time step, resulting in an overall polynomial improvement. Numerical experiments demonstrate a 53x reduction in query depth for a 1D benchmark system of 100 qubits. The reduced circuit depth makes dissipative algorithms more practical for near-term quantum computing.

We consider the Wigner–Smith time-delay matrix of a chaotic cavity with broken time-reversal symmetry, a finite number $M$ of equivalent open channels, and a uniform tunnel barrier of reflection probability $R$. Exact random-matrix results for nonideal coupling are known at the level of the joint matrix distribution, whereas explicit finite-channel formulas for Schur moments are much less developed. We show that every Schur moment of the nonideal Wigner–Smith matrix factorizes into its ideal-coupling inverse-Laguerre value and a barrier factor given by a circular unitary ensemble (CUE) average of a Schur polynomial. A dual-Cauchy generating function reduces the latter to a Toeplitz determinant with a rational symbol, placing the barrier problem within the classical theory of exact CUE characteristic-polynomial ratios. For one-row partitions, specializing the exact CUE ratio formula gives a closed generating function for all barrier coefficients and therefore all finite delay-time moments in the allowed range $n\le M$. The exact ratio formula decomposes into a sector visible to the semiclassical expansion and an additional contribution proportional to $R^{M+1}$, which is invisible to every algebraic order in $1/M$. For the second moment, this contribution exactly reproduces the exponentially small term in the known random-matrix expression for $\langle\tau_W^2\rangle$ that is absent from the semiclassical expansion. We also derive an exact finite sum for all one-column partitions and an exact complement symmetry for Schur averages inside the $M\times M$ square. The new contribution therefore lies not in the underlying CUE ratio identities, which are classical, but in combining them with the exact nonideal Wigner–Smith mapping and extracting the resulting finite-channel time-delay formulas and corrections to semiclassical Schur-moment conjectures.

We theoretically demonstrate tunable population transfer and entanglement generation in a two-exciton system coupled to a bimodal optical cavity, including an exciton-exciton exchange interaction. By using dressed-state transformations and Löwdin partitioning, we derive effective Hamiltonians that identify three dynamical time scales: local polaritonic oscillations, long-period dressed-state beats, and direct excitonic exchange. We apply the framework to graphene nanoribbon excitons, using excitonic energies, transition dipoles, and lifetimes obtained from first-principles calculations to parameterize the model. In the small- and moderate-exchange regimes, the dynamics become multiscale, and the beat channel sustains sizable quantum entanglement over long dissipative time windows. In the large-exchange regime, the excitonic Hamiltonian dominates and drives nearly complete population transfer between excitonic states over many coherent cycles. The resulting analytical expressions provide direct design rules connecting the beat period, transfer amplitude, and coherent-cycle number to the exchange interaction, cavity detuning, and light–matter coupling. Our results establish a controllable route for engineering long-lived entanglement and coherent excitonic operations in exciton–bimodal-cavity systems.

PyCC is a Python-based quantum chemistry package that emphasizes coupled cluster and related methods. Developed over the last five years, PyCC's purpose is to provide a clear reference implementation that will serve as a validation suite for production code, a platform for rapid prototyping and testing of new methods, and an educational resource for newcomers to the field. The package covers both closed- and open-shell single-determinant references in spatial- and spin-orbital formulations for the coupled cluster hierarchy through triples-including methods such as CC3, as well as for MP2 and CI methods. PyCC provides a broad range of molecular properties such as analytic first and second energy derivatives including gradients, Hessians, polarizabilities and atomic polar tensors, as well as non-energy-derivative properties, such as velocity-gauge atomic polar tensors and diagonal Born-Oppenheimer corrections, plus dynamic linear response functions, and excitation energies. The package includes a number of unique capabilities, such as real-time CC propagation (up through CC3), atomic axial tensors for simulating vibrational circular dichroism spectra at the MP2 and CI levels, and local correlation methods including perturbation-aware correlation domains. The code is designed around a shared Wavefunction base and a method-agnostic derivative layer that enables a new method to inherit the full property set once it supplies its reduced densities and their first-order responses. PyCC is fully open source and available on GitHub under the BSD 3-clause license.

Simulating open-system dynamics on gate-based quantum computers offers a route to exploring dissipative quantum phenomena through programmable quantum processing. Collision models provide a direct and flexible framework by representing the environment through sequential system–ancilla collisions. At finite temperatures, however, they typically require repeated preparation of thermal ancillas, adding circuit overhead and gate errors. Here, we show that, for a broad class of $U(1)$-symmetric system–ancilla interactions, finite-temperature dynamics can instead be reconstructed from simulations with pure ancillas in orthogonal basis states. The target temperature enters only through classical post-processing of the resulting pure-branch data, allowing the same data to be reused for different temperatures without additional quantum processing. Bypassing explicit thermal-state preparation reduces ancilla-preparation overhead and preparation-induced errors.

Neutral atom-based quantum computers have the ability to dynamically reshape the qubit register geometry between experimental runs. This capability enables different types of problems to be mapped directly onto the hardware, significantly enhancing the efficiency of solving, for instance, complex optimization and machine learning use-cases. However, this flexibility requires finding atom positions to implement target pairwise interactions, inducing a difficult nonconvex optimization for setting up the quantum machine. In this work, we introduce a new force-directed method that targets interactions to encode the input problem on the quantum computer. Numerical results show that our new method can improve embedding feasibility and solution quality by orders of magnitude, and remains reliable on the largest instances.

A small excitation gap makes a quantum dot a sensitive probe of a weak electric field, but the charge detector that reads the probe can also excite it. We ask how much of this gap enhancement survives continuous readout, for two interacting electrons tuned through a confinement-driven crossover in a gate-defined quantum dot, using a weak-coupling, stationary two-level model. The answer depends on the energy structure of the detector, not only on its noise. A quantum point contact measures more strongly at higher bias, but once the bias energy exceeds the gap it also excites the probe. When this back-action dominates the probe's own relaxation, the optimised mean-current information rate grows only as the inverse of the gap. A sensor quantum dot with a narrow level instead reaches full measurement strength at a bias below the gap, where, for a non-interacting detector and to second order in the coupling, it cannot excite the probe at zero temperature. This energy-filtered readout keeps the inverse-square gap dependence of the quantum Fisher information, down to a gap set by the detector's level width and the electron temperature. Through such a detector the interacting pair gives about three times the information rate of a single electron with the same gap and detector, compared with 1.6-1.7 in the quantum Fisher information, for both contact and Coulomb-type interactions. These different gap dependences, and the different optimal biases of the two detectors, can be tested by bias and gate sweeps on suitably tuned charge-sensed double dots.

We develop a quantum input-output theory for open photonic systems based on macroscopic quantum electrodynamics using the first-order electromagnetic Green's function. Quantum fields enter and leave the photonic system through waveguide ports, while the electromagnetic Green's function propagates the fields through the arbitrary interior of the photonic system, which may be dispersive and absorbing. The resulting relation contains a port-to-port scattering matrix and a Langevin-noise contribution from material absorption that together preserve the bosonic output commutation relations. For embedded emitters, the low-saturation and single-excitation scattering response is determined by Green's functions connecting the ports and emitters while retaining the non-Markovianity of the photonic environment. Using finite-difference time-domain simulation, we calculate the emitter-modified transmission for a nanophotonic cavity and inverse-designed coupler, providing a direct route from computed electromagnetic response to a quantum input-output model.

Extreme values of a physical system are rare by definition, yet they can govern some of its most consequential and anomalous behavior. Their sparsity makes them particularly costly to identify in large data sets. Quantum computation offers a potential way to reduce this cost. The challenge is that quantum amplitudes cannot simply be read, copied, compared, or marked using conventional search oracles. Here we introduce an algorithm for searching amplitude-encoded quantum signals that exploits their underlying structure. Through a nonlinear amplitude transformation, the algorithm identifies and certifies a global extremum while exponentially reducing the effective search space for structured signals. We establish a polylogarithmic query complexity in the data size $N$, together with global completeness, efficient classical verification, and a measurable criterion for quantifying exploitable structure. We demonstrate the framework using computational turbulence data. Applied to line cuts of velocity gradients from a $1024^3$ simulation containing more than one billion grid points, the algorithm consistently isolates the extreme tail and reduces the median search space to $O(\log N)$. The clustered structure characteristic of turbulence further accelerates the search. These results establish a direct connection between physical structure and quantum advantage in extreme-value search. For accessibility, the main text emphasizes the central ideas and results, while the theoretical derivations are provided in the Appendices.

We introduce and study multi-indexed Schatten spaces which lift the commutative $\ell_{q_1}\![\ell_{q_2}\![\ell_{q_3}\![\dots]]]$ -spaces to the quantum toolbox. They extend Pisier's operator-valued Schatten norms and the recently introduced two-indexed Schatten quasi-norms. For every ordered tuple $\mathcal{Q}=(q_1,\dots,q_n)$ of non-zero indices we define a quasi-norm (for positive indices) or an anti-norm (for negative indices) on operators on an $n$-fold tensor product of Hilbert spaces. We do this for indices of the same sign satisfying the mutual compatibility condition $|\frac{1}{q_i}-\frac{1}{q_j}|\leq1$. Beyond establishing desirable properties of these quasi- and anti-norms via factorization formulas that combine operator space and matrix-analytic techniques, we lift several central tools and results of the seminal [Devetak, Junge, King, Ruskai, CMP 2006] to the multi-indexed and quasi-normed setting. These include a general non-commutative Minkowski inequality relating swapped systems and two identity-removal equalities. As a consequence, complete hypercontractivity and complete reverse hypercontractivity tensorize for all compatible indices, including negative ones. Concerning quantum information theory we prove that the completely bounded minimal and the maximal output conditional entropies are additive under tensor products of quantum channels for all $\alpha\geq\frac12$. This extends the case $\alpha>1$ of [Fawzi, Kochanowski, Rouzé, Van Himbeeck, CMP 2026] and the unconditional results of [Devetak, Junge, King, Ruskai, CMP 2006]. Beyond tensor products, we establish chain rules for $\tilde H^\uparrow_\alpha$, $\alpha\in[\frac12,1)$, for compositions of possibly signaling channels, extending upon the smooth max-entropy chain rule used in device-independent entanglement certification to the whole interval $\alpha\in[1/2,1)$.

Algorithms which prepare states via the variational principle assume that closeness in energy implies closeness in state; we formalise how this assumption can fail by showing that a ground-state energy estimate converged to within $\epsilon$ of the true value is compatible with observable expectation values in error by up to $\sqrt{2\epsilon\chi_O}$ at leading order, where $\chi_O$ is the static susceptibility of the observable. As a result, the observable error can be orders of magnitude larger than the energy error, a mechanism we call non-linear error amplification, and because a variational algorithm cannot guarantee the correctness of these properties from energy alone, energy does not necessarily certify an observable. In light of our findings, we examine three algorithms: the variational quantum eigensolver on the transverse-field Ising chain, the density-matrix renormalisation group on a pair of weakly coupled hydrogen chains, and Krylov quantum diagonalisation on lithium fluoride. In each case we demonstrate that a well-converged energy can leave observables uncertified.

Despite its remarkable empirical success and predictive power, quantum theory is riddled with mysteries. One such conceptual puzzle is the measurement problem: why do quantum systems in superposition collapse to a single-valued outcome upon being measured? The scenario of Wigner's friend illustrates this tension, with recent extensions deriving strong no-go results for the nature of reality. Another fundamental puzzle arises at the interface of quantum theory and general relativity—the black hole information paradox. This thesis further explores these two foundational paradoxes. Part I of this thesis focuses on extensions of the Wigner's friend scenario. We introduce several novel extended Wigner's friend arguments, and establish connections with notions of nonclassicality, i.e. the study of quantum phenomena that evade classical explanation. Key results are a refined version of the Frauchiger–Renner paradox, new extended Wigner's friend scenarios based on contextuality, and an argument against the absoluteness of free choices based on the Pusey–Barrett–Rudolph theorem. The latter result complements the Local Friendliness no-go theorem, which challenged the absoluteness of experimentally obtained measurement outcomes. In Part II of this thesis, we turn to black hole puzzles and the interplay of quantum theory and gravity. We present a paradox that unifies extended Wigner's friend arguments with black hole puzzles, strengthening the cloning and firewall paradoxes. After discussing subtleties in these paradoxes, we next present an analysis combining elements of quantum theory with gravity, analyzing the detector response near a superposed black hole using elements of quantum reference frames.

Coherent microwave-to-optical transduction is essential for hybrid quantum networks. Typical transducers, however, have bandwidths set by fixed device parameters and face efficiency-bandwidth tradeoffs. We propose a cavity-based microwave-to-optical transducer mediated by a collective Rydberg excitation, where external driving fields tune the conversion bandwidth in situ from narrowband, spectrally selective operation to broadband operation without modifying cavity parameters. Under impedance-matching conditions, the system operates as a continuous-wave phase-preserving linear transducer, maintaining both near-unit efficiency and quantum-limited added noise throughout the tunable bandwidth range.

The increasing complexity of 5G and emerging 6G wireless networks has intensified the need for scalable optimization techniques capable of addressing computationally challenging combinatorial problems. While quantum optimization offers a promising alternative, current near-term quantum hardware remains limited by qubit availability, noise, and scalability constraints. Pauli Correlation Encoding (PCE) has emerged as a qubit-efficient optimization framework that addresses these challenges through polynomial variable compression. However, the impact of compression on representability and solution recovery remains poorly understood. In this work, we investigate the hypothesis that increasing compression ratios introduce representability constraints arising from algebraic closure relationships among encoded Pauli observables. Using Maximum Independent Set benchmark problems, we evaluate multiple PCE ansatz designs and compression ratios. We find that ansatz selection is critical to both solution quality and feasibility, with two ansatz families consistently outperforming alternatives and achieving approximation ratios approaching 0.9. Furthermore, increasing circuit repetitions provides limited benefit and can degrade performance. Most notably, reducing compression by increasing the number of available qubits significantly improves raw solution quality, while post-processed performance remains comparatively stable. These findings suggest that representability effects play an important role in compressed PCE encodings and provide new insight into the relationship among compression, ansatz design, decoding, and solution quality in scalable quantum optimization.

Non-Clifford operations required for universal quantum computation can be implemented by measuring logical Clifford operators. We present a generalized framework to fault-tolerantly measure transversal Clifford logical operators in quantum low-density parity check (QLDPC) codes. Our protocol takes any QLDPC code and deforms the code across additional ancillary qubits such that a product of stabilizers measures the desired logical Clifford operator. We prove that the deformed, Clifford-stabilized code remains LDPC and that its distance is preserved up to a constant factor independent of the number of physical qubits. We further prove that the Clifford measurement has a fault distance against data qubit and measurement errors that grows linearly with the distance of the original code. We apply our methods to implement logical non-Clifford gates in algorithmically relevant subroutines and to prepare magic states in quantum stabilizer codes.

Span programs provide a general method for designing quantum query algorithms, and for Boolean coordinate queries their optimum cost agrees with the general adversary bound. We develop a matroidal organization of this optimization that separates dependence data determined by the query problem from the dependence structure chosen by a span program. The resulting source matroid records query dependence, target determination, and separation data, while a program matroid records dependencies among the target and response-labeled input elements of a candidate program. When a query problem admits a source matroid, we use its structure to organize adversary upper and lower bounds. For separating binary query sets in this setting, the global matroid-span-program optimum equals $\mathrm{Adv}^{\pm}_{\mathcal Q}(f)$. For a fixed regular program matroid, projective uniqueness reduces optimization over real representations to positive element weights. We express positive and negative costs as weighted basis ratios and derive exact $1$-, $2$-, and $3$-sum reductions, giving a compositional evaluation of the fixed-weight objective along a Seymour decomposition. For pointed $R_{10}$ closure, exact certificates give $3.9087553<\mathrm{Adv}^{\pm}_{\mathcal Q_R}(f_R)<3.9301$. The natural $R_{10}$ program has optimized cost $5$, while another regular program has cost at most $\sqrt{87/5}$. Recursive self-composition yields a family on $N$ input bits with randomized query complexity $\Omega(N^{0.7324867\ldots})$, an explicit quantum algorithm using $O(N^{0.6500178\ldots})$ queries, and $Q(F)=\Theta(N^\alpha)$ for $0.6204277\ldots<\alpha<0.6229063\ldots$.

The sign problem remains a major obstacle to extending the success of Monte Carlo methods to generic quantum many-body systems. A powerful alternative relies on cluster expansions, which provide convergent representations of free energies and related quantities, with applications ranging from tensor network contractions to quantum dynamics and circuits. Within their convergence regime, these expansions often yield deterministic polynomial-time algorithms. However, such methods require exhaustive enumeration of connected clusters, leading to a computational overhead of $(N\varepsilon^{-1})^{\mathcal{O}(\log\Delta)}$, where $N$ is the system size, $\varepsilon$ the desired precision, and $\Delta$ the interaction-graph degree. This becomes particularly severe in long-range systems, where $\Delta\equiv N$, resulting in quasi-polynomial runtimes. Here, we introduce a Markov chain algorithm that samples cluster expansions directly in the space of connected clusters, replacing deterministic enumeration by Monte Carlo sampling. Because sampling occurs in an abstract cluster space rather than a physical configuration space, the conventional exponential sign problem is absent within the convergence regime. For general polymer models, we establish an FPRAS with runtime $\tilde{\mathcal{O}}(N^4\varepsilon^{-3})$. For quantum partition functions at temperatures $\beta\leq\beta_{\mathrm{sampling}}\equiv\mathcal{O}(1)$, this improves to $\tilde{\mathcal{O}}(N^{3+1/D}\varepsilon^{-3})$, where $D$ is the lattice dimension. The framework directly applies to long-range quantum systems with interactions $\propto1/r^\alpha$, including $2$-local models with $\alpha>2D$.

Analog quantum computing with neutral atom computers is rapidly evolving into a promising way to perform combinatorial optimization tasks at scale. As technology matures, exploring real use cases leveraging such unique information processors becomes warranted; one example is logistics management where vehicles are assigned routes to perform a given task. Autonomous aerial vehicles (drones) are increasingly used for surveillance over a given territory. In this paper, we propose an innovative framework integrating classical machine learning forecasts and quantum annealing to find the best combination of tours for a given surveillance criterion. The risk maps produced by machine learning are used to find possible surveillance tours, while a quantum-assisted heuristic chooses the best combination, producing a full surveillance plan. By modelling our decision problem for drone paths as a Team Orienteering Problem (TOP) with additional constraints, we construct a new heuristic using quantum annealing to find good solutions. We apply this framework to wildfire prevention, where finding tours most likely to detect a starting fire has clear benefits. We study the effect of larger quantum resources on heuristic performance and compare those results to the use of classical resources.

When the two largest eigenvalues of an entanglement or negativity spectrum meet, the gap of the corresponding entanglement or negativity Hamiltonian closes while entropies stay smooth, an event we call a temporal criticality. We ask when such criticalities occur in open fermionic systems, in which order, and whether they precede the geometric decoherence time, at which the negativity starts to fall. We compare loss and gain, which respect fermion parity only on average (weak symmetry) and keep the state Gaussian, with density dephasing, which respects it in every jump (strong symmetry) and makes the state non-Gaussian. We find that Gaussianity, rather than the symmetry, fixes the criterion for a criticality. In a Gaussian state the entanglement gap of a subsystem closes exactly when its two parity sectors carry equal weight, and the negativity gap closes exactly when this happens for the subsystem or its complement, so the negativity criticality never comes after the entanglement one. Uniform balanced loss and gain only shrink the two parities and leave both criticalities in place at every rate, for any quadratic Hamiltonian. In an open Kitaev chain, weak imbalanced loss and gain shift the criticalities by amounts set by the occupations at the cut, and at strong rates they allow a closing near the relaxation time only when an odd number of neighboring sites pass half filling. For dephasing we solve the non-Gaussian dynamics exactly at the point where the bond terms of the chain commute. Weak dephasing delays both criticalities and, away from the chain ends, moves them ahead of the parity balance, where the parity rules of Gaussian states would place them. As the dephasing rate approaches critical damping, both criticalities recede to infinite time, and at strong dephasing the negativity starts to fall long before either gap begins to shrink.

Quantum signal processing (QSP) realizes operator functions through polynomial transformations of a finite degree. In addition to function approximation errors, realistic implementations incur algorithmic errors also when encoding the input operator and in synthesizing gates into a discrete fault-tolerant gate set. We provide an end-to-end analysis of these errors and show that the input error propagation is governed by the operator-Lipschitz constant $L_\star$ of the implemented function. We then derive an efficient classical algorithm to compute tight lower and upper bounds on $L_\star$, and we show that these bounds induce in turn lower and upper bounds on the total quantum-resource cost (for instance, $T$-gate count) needed for QSP implementations compatible with a target total error. We further devise a procedure to search for a polynomial with minimum cost based solely on its coefficients, yielding a resource-aware polynomial-design procedure for QSP. We illustrate the resulting method for ground state energy estimation via eigenvalue thresholding for the H$_6$ molecular Hamiltonian. In the tested hardware profiles, the resource-aware family improves the total-error budget and reduces the modeled $T$-gate count by $51.9\%$–$56.7\%$ relative to the standard analytical polynomial obtained from an error-function approximation.

We present release 3.0 of the ALPS (Algorithms and Libraries for Physics Simulations) project, an open-source software project to develop libraries and application programs for the simulation of strongly correlated quantum lattice models such as quantum magnets, lattice bosons, and strongly correlated fermion systems. As in previous releases, development is centered on common data formats, on libraries to simplify and speed up code development, and on full-featured simulation programs that let non-experts carry out serial or parallel numerical simulations using the important algorithms for quantum lattice models: classical and quantum Monte Carlo (QMC) using non-local updates, extended-ensemble simulations, exact and full diagonalization (ED), the density matrix renormalization group (DMRG), and continuous-time QMC solvers for dynamical mean-field theory (DMFT). Major changes in release 3.0 include distribution of the pyalps binary through the Python Package Index (pip install pyalps) and through Spack for HPC systems; migration of development to GitHub with continuous integration and automated testing; relicensing of the package under the permissive MIT license; a rebuilt documentation and tutorial website, including a set of Jupyter-notebook tutorials and localized content; a broad modernization of the C++ codebase (C++17 compliance, Boost and NumPy 2.0 compatibility, and warning and dead-code cleanup) together with a major DMRG update and associated reliability and build-compatibility fixes; the removal of legacy components (the VisTrails provenance integration and the TEBD, MPS, and directed-worm-algorithm application codes); archival of release 3.0.0 with a Zenodo DOI; and a formal governance and sustainability model developed under the US National Science Foundation (NSF) POSE program. The software is available at https://alps.comp-phys.org.

Krylov subspace methods provide an efficient tool for modeling any quantum initial value problem as a one dimensional chain. A time ordered path formulation, in which the dynamical generator successively splits probability between paths, yields a speed limit within such dynamical subspaces. For power law growing Lanczos coefficients $b_n \sim n^\delta$ we find super ballistic lightcones, with an exponentially spreading lightcone for maximal growth $\delta = 1$. Causality in the Krylov chain constrains the Lanczos coefficients to grow at most linearly. We illustrate the validity of the above in the $SL(2, R)$ and Heisenberg Weyl algebras, and in a many body skew field Ising model. Moreover, we extend the above bound to the family of $Q$ complexities. Lastly, mapping to a spin chain gives a Lieb Robinson bound analogue and opens the problem to standard condensed matter methods.

We present a method for constructing time-dependent integrable chiral quantum field theories on a finite spatial interval from the scattering and reflection data of an autonomous integrable model. Each particle carries a spectral label that is constant along its free trajectory. Requiring a right–left scattering event at a fixed time to be independent of its position forces the map from characteristic coordinates to spectral labels to be affine, with the same slope for both chiralities. As a result, all bulk scattering events at a given time sample one common spectral argument, and the two boundary reflections sample the corresponding endpoint arguments. The bulk and boundary drives are not independent time-dependent deformations: their time dependence is fixed by the same affine spectral coordinate. Successive reflections at the two endpoints translate the spectral label by a finite amount. Following a particle through a complete round trip then produces a difference transport whose consistency gives the boundary quantum Knizhnik–Zamolodchikov equations. The Yang–Baxter and reflection equations ensure local compatibility, while BqKZ flatness ensures compatibility of complete round trips. We then reconstruct the local Hamiltonian by inverting the one- and two-particle matching problems. The bulk matrix fixes the contact interaction, while the reflection matrix fixes the corresponding boundary coupling. The same autonomous factorized data determine the boundary qKZ transport problem and the local driven Hamiltonian. Once the boundary qKZ system is solved, its characteristic pullback gives the corresponding time-dependent many-body wavefunction. We illustrate the construction with rational $SU(N)$ models and an $O(3)$-invariant model with dynamical boundary impurities, obtaining driven Kondo interactions whose couplings are fixed by the same affine spectral coordinate.

Neural decoders for quantum error correction have achieved high accuracy on surface code memory experiments, but practical fault-tolerant computation requires decoders that transfer across devices, noise conditions, and logical operations. We introduce a modular graph-recurrent decoder designed for this setting. Built from reusable local syndrome encoders, it is pretrained in simulation and fine-tuned to hardware using a common pipeline, with only lightweight spatial aggregation adapted between experiments. The decoder performs competitively on conventional memory benchmarks, outperforming minimum-weight perfect matching (MWPM) at distances 3 and 5 on an IBM Nighthawk-generation processor and closely approaching AlphaQubit on Google's below-threshold hardware data. We then test the framework in two settings beyond memory decoding on the same IBM device. First, in an $X$-type stability experiment, we continuously tune the effective measurement error rate using a variable pre-readout rotation and train a single decoder jointly across the resulting noise range. It matches or outperforms MWPM across the sweep without requiring a separately calibrated decoder for each setting. Second, we decode a $ZZ$-merge lattice-surgery experiment between two distance-3 surface-code patches, improving the decoded correlation between the merge outcome and the final $ZZ$ parity relative to MWPM on real hardware. Together, these experiments demonstrate neural decoding that is portable not only across hardware, but also across qualitatively different surface code observables and logical operations, using models trainable on single-GPU academic resources.

We construct large families of mixed-state topological orders in (3+1)D, including intrinsically mixed-state topological orders. We realize these theories by relating the data parameterizing braided fusion 2-categories to the strong higher-form symmetries and their anomalies that characterize mixed-state topological order. We focus here on bosonic theories, which include theories which admit an emergent fermion in the spectrum of line operators. Our general framework is exemplified on the lattice, where we present concrete models and identify how the data of braided fusion 2-categories are physically encoded. We then formalize this data and show that every braided fusion 2-category arises from a general construction (decoherence followed by strong and weak gauging) as the category of topological operators of a (3+1)D mixed state, with intrinsically mixed-state topological orders distinguished by the presence of strong higher-form symmetries whose anomalies cannot be realized in any (3+1)D gapped ground state. Notably, we show that (3+1)D admits a new mechanism with no (2+1)D counterpart: intrinsically mixed-state order whose topological operators braid non-degenerately, but which carries a gravitational anomaly.

Dissipative protocols offer a route to ground state preparation that does not require a large initial overlap with the ground state. To move beyond time independent Lindbladians and randomized Lindbladians sampled from a fixed distribution, we study driven-dissipative protocols in which the generator varies over time. Our construction uses a family of randomized Lindbladians whose jump operators are obtained by averaging the unitary evolution of random matrices under a fixed target Hamiltonian. We show that empirical averages of these Lindbladians drive arbitrary initial density matrices to low energy states. The resulting mixing time bounds depend on the Hamiltonian only through its eigenvalue distribution and remain valid even for cold starts.

We demonstrate that Maxwell electrodynamics interacting with a planar Chern-Simons interface does not have plane wave solutions localized on the interface. We re-examine the derivation of such waves presented by N. Kaloper in [Phys.Rev.D114(2026)L051903] and show that at an intermediate step certain terms were missed in an equation therein. Taking these terms into account changes the final conclusion of [Phys.Rev.D114(2026)L051903]. We also show that the method used in [arXiv:2606.12513 [hep-th]] to reproduce the same result is not consistent.

A recent trapped-ion experiment realised a lattice gauge theory in which the internal states of the ions carry the gauge field and their vibrations carry bosonic matter. In that experiment, a charge moving around a loop was frozen in place by Aharonov–Bohm interference. The noise of such a device is usually simulated after the measurements have been made. Here we show that it can be predicted before the machine is run. For each quantity that the experiment measures, we derive, from the reported noise rates alone, a formula for how it decays together with a bound it can never exceed. The results reveal a simple pattern. The local conservation law of the theory is a parity of the vibrational mode, so heating violates it at a rate that grows as the mode warms, and the violation accelerates rather than accumulating steadily. At zero electric field, each of the three noise processes of the device leaves its own fingerprint. Heating affects only the conservation law, motional dephasing only the interference, and qubit errors only the stored flux and the frozen state. Once the electric field is switched on, the frozen state begins to leak, and we write an exact formula and a guaranteed bound for that leak.