Papers

Adaptive variational quantum algorithms like ADAPT-VQE construct tailored ansätze by iteratively selecting operators from a pool using gradient-based criteria. While this avoids oversized parameter spaces, repeatedly scanning the full pool incurs a classical cost that scales linearly with pool size-a major bottleneck for systems with long-range interactions or large operator sets. Here, we reformulate adaptive operator selection as a graph-based decision problem and introduce a graph neural network (GNN) policy that predicts the next entangling operator directly from the interaction graph and state-dependent observables. Training data are generated from exact simulations of disordered long-range spin chains, using gradient magnitudes as supervision signals. The learned policy accurately reproduces the dominant structure of the greedy gradient-based selection rule, significantly outperforming heuristics based solely on interaction strength. Integrated into a variational quantum eigensolver (VQE) workflow, this GNN-VQE approach achieves energy errors close to standard ADAPT-VQE while drastically reducing full-pool gradient evaluations. To test transferability beyond spin models, we evaluate the policy on small active-space molecular benchmarks (LiH and BeH_$2$). We find the GNN is highly effective as a shortlist generator: exact rescoring over just a few GNN-proposed candidates recovers near-oracle rollout behavior while searching only a small fraction of the pool. These results demonstrate that adaptive circuit construction contains learnable structure that can be exploited to accelerate variational quantum algorithms.

We study the problem of implementing a quantum channel using a small ancilla with classical randomization and postselection on an output failure flag. The simulation is probabilistic, but exact conditioned on success. We prove that any unital channel on a $d$-dimensional system can be simulated with ancilla dimension $k$ and success probability $\Omega(\frac{k}{\log d})$. Equivalently, every unital channel on $n$ qubits can be simulated with constant success probability using only $O(\log n)$ ancillary qubits. We show that this tradeoff is best possible by constructing a family of channels which cannot be simulated with success probability better than $O(\frac{k}{\log d})$. We also show that the class of highly noncommutative channels, which includes random channels, admits constant-success simulation with just a single ancillary qubit. We further show that this model of simulation necessarily fails for strongly non-unital channels and discuss possible extensions involving adaptivity. On the technical side we rely on a partition-based protocol and matrix concentration inequalities, including the recent refinement of noncommutative Khintchine inequalities due to Bandeira, Boedihardjo and van Handel.

We relate the properties of bivariate-bicycle-surface (BBS) codes, constructed from a pair of bivariate polynomials over a finite field, to the number and location of their common roots in the extension field. The number of roots $(x,y)$ with finite, non-zero coordinates – counted with algebraic multiplicity – determines the dimension of the codes. This dimension is invariant under monomial automorphisms of the Laurent polynomial ring. Conversely, roots with zero or infinite $x$- or $y$-coordinates indicate that specialized generators are required near the corresponding boundary (e.g., the left or right boundary for a root where $x$ is zero or infinite, respectively). These roots can appear or disappear under monomial transformations, which reveals the structure of tilted boundaries. Based on these results, we formulate a prescription for constructing BBS codes that works for regions with rectangular, diagonal, and arbitrarily tilted boundaries. A key advantage of this approach is that no corner corrections are needed, provided the polynomials satisfy orientation-specific edge conditions.

Tensor networks and quantum circuits are structural objects whose meaning depends on connectivity, indices, contraction order, gate placement, measurements, and related design choices. They are often easier to reason about visually than as code, yet in Python they are frequently constructed, transformed, and checked through backend-specific objects or compact symbolic expressions. This can make structural mistakes hard to notice during development, debugging, and communication. This paper presents three complementary packages: Tensor-Network-Visualization for visual debugging and structural inspection of supported tensor-network and traced einsum workflows; Tensor-Network-Editor for visual-to-code authoring, backend code generation, JSON preservation, export, and design-level analysis; and Quantum Circuit Drawer for clear circuit rendering, inspection, and complementary comparison of circuits or documented result distributions. The packages form a visual authoring and inspection layer around existing tensor-network libraries, array-based scientific Python workflows, and quantum SDKs. They are not simulators: they do not implement new contraction algorithms, execute quantum circuits, or guarantee full semantic equivalence across arbitrary backends. Their contribution is to make structural artifacts visible, editable, inspectable, comparable, exportable, and reproducible within those ecosystems.

To utilize quantum error-correcting codes, a decoder must infer the logical sector from the measured syndrome. Beyond a hard logical decision, some decoders provide soft information estimating its reliability. For minimum-weight perfect matching (MWPM), a common confidence measure is the complementary, or logical, gap. Neural-network decoders trained with binary cross entropy naturally output a different score, the logit, whose relation to the matching gap remains unclear. Here, we test whether the logit of a graph neural network (GNN) decoder behaves as a learned proxy for the logical gap. Using a pretrained GNN for the rotated surface code under uniform circuit-level noise [PRR, 7(2):023181, 2025], we compare its logit with the MWPM gap shot by shot on identical syndromes. The native GNN decoder-logit pair yields a lower post-selected logical error rate than the native MWPM decoder-gap pair over the operationally relevant range of acceptance rates. Exchanging the scores between decoders isolates score discrimination from hard-decision accuracy and shows that both contribute to the advantage. At larger distances each score best ranks failures of its own decoder, whereas at small distance, where the bounded matching gap saturates, the learned confidence better discriminates MWPM failures than the gap itself. Finally, GNN confidence is substantially better described by the posterior logistic form implied by an ideal log-likelihood ratio, while the MWPM gap shows systematic departures. Thus, a neural-network decoder trained only on syndromes and logical labels learns a gap-like confidence proxy whose native scale more closely follows the expected posterior calibration form, supporting learned soft output when MWPM gap estimates are unavailable, costly, or insufficiently expressive.

We investigate how quantum resource constraints affect deep thermalization, the emergence of universal local wavefunction distributions from partial measurements of a quantum many-body state. Quantum resources, such as non-stabilizerness (magic), coherence, asymmetry, imaginarity, and non-Gaussianity, are essential for quantum information processing, and constraints on their global abundance can reshape these emergent distributions. To address this question, we develop a unified framework for deep thermalization within general quantum resource theories (QRTs). Our central result is that QRTs fall into two classes: “smoothly localizable” (SL) QRTs, where the resource content of local post-measurement states changes continuously with the global resource density, set by the initial state and measurement basis, yielding continuously tunable wavefunction distributions; and “threshold localizable” (TL) QRTs, where the local resource content jumps discontinuously from minimal to near-maximal past a critical global resource threshold, producing a sharp transition between a resourceless, “deep-ergodicity breaking” distribution and a resourceful, maximally random one. We trace this SL-TL dichotomy to an information-theoretic mechanism, block sharpening: by viewing each QRT as coherence between blocks in Hilbert space, we show that the local resource content depends on the measurement's ability to collapse an initial superposition into a single resourceless block. Our theory is analytically tractable and quantitatively predicts the phase boundaries across all studied QRTs, which we validate with extensive numerical simulations. Finally, we highlight two consequences: a novel magic transition in zero-rate quantum error-correcting codes–previously believed to occur only at finite rates–and new implications for quantum resource certification protocols based on post-measurement state ensembles.

We propose a theoretical mechanism to indirectly detect the small parity-violating energy difference (PVED) between chiral enantiomers through a macroscopic enantiomeric excess observed in an ultracold gas. We consider that chiral molecules are formed resonantly via ultracold collisions of achiral diatomic molecules, with PVED inducing a slight asymmetry in the resonance energies of right- and left-handed configurations. After formation, chiral molecules evolve within a Bose-Einstein condensate (BEC), incorporating nonlinear interactions, tunneling between enantiomeric states, intrinsic PVED, and thermal conversion rates. These collective dynamics enable amplification of the microscopic bias into a global population imbalance. Using coupled rate equations, we show that, under realistic regimes, a complete enantiomeric excess can be achieved even for extremely small intrinsic asymmetries. We illustrate the model with concrete examples (HSOH, H$_2$Se$_2$, H$_2$Te$_2$), predicting observable enantiomeric excesses under plausible experimental conditions. We also consider non-PVED effects that could be amplified under the proposed mechanism, including electric and magnetic fields as well as thermal fluctuations, the latter being illustrated through the aforementioned molecular examples. Overall, our results suggest that ultracold physics could provide a new pathway to probe molecular parity violation, a fundamental weak effect that remains experimentally undetected.

Predicting the output of quantum circuits is a central bottleneck for verifying quantum processors because a generic wavefunction grows exponentially with system size. We introduce a neural statebank that learns this wavefunction along the circuit trajectory. Each layer is stored as an autoregressive Transformer checkpoint trained from local gate updates to the preceding checkpoint, producing a compact neural representation that can evaluate amplitudes and generate independent samples. On long-range circuits combining entanglement, magic, and non-diagonal branching, a 0.3-million-parameter statebank reaches $\sim 10^{-2}$ infidelity at 34 qubits, outperforming the other tested approximate simulators while using far less memory than exact state-vector evolution. The same architecture accurately simulates quantum approximate optimization, Clifford+$T$, and Clifford circuits.

We develop a regularised arbitrary-gauge formulation of nonrelativistic quantum electrodynamics and use it to compare Coulomb and multipolar descriptions with a Lorentzian form factor. We analyse the effect of regularisation in perturbation theory, including alternative partitions of the Hamiltonian into free and interaction parts, and the limits of the electric dipole approximation. The regularised multipolar gauge exhibits a cut-off-dependent trade-off between the strength of individual interaction terms and the localisation of material subsystems that suppresses direct inter-atomic interactions. We discuss the implications of the framework for short-range phenomena, including Dicke criticality.

We investigate entanglement transfer in a multi-giant-atom waveguide system. By tailoring chiral spontaneous emission and exploiting dark-state dynamics, the setup enables perfect, unidirectional sequential or selective transfer of quantum states and their associated entanglement. The distance between two entangled atoms, i.e., the entanglement length, can be dynamically adjusted, allowing robust conversion between long-range and short-range entanglement during propagation. The system inherently converges to a dark state, guaranteeing high-fidelity directional transfer. When the additional phase is modulated as a periodic piecewise function, spatially separated giant atoms exhibit stable, nearly lossless state exchange and maintain steady entanglement even under non-Markovian conditions. This behavior mimics conventional braided architectures without suffering from propagation delays or spatial restrictions. Our proposal offers a scalable pathway for continuous long-distance entanglement transport and resilient state exchange in quantum networks.

Optimization of continuous-variable objectives on standard gate-based quantum computers via variational algorithms such as QAOA is typically approached by first discretizing each decision variable into a finite binary representation. This increases qubit requirements and restricts solution precision through fixed-resolution encodings. We propose a qubit-efficient variational framework for continuous optimization that instead encodes each decision variable into the squared amplitude or equivalently, the measurement probability of a single qubit state. This removes explicit discretization from the variable representation while remaining entirely within the standard qubit circuit model unlike methods like CV-QAOA employing qumode based hardware to achieve the same. To read out encoded variables, we propose using amplitude estimation rather than naive sampling or tomographic reconstruction, with the goal of improving precision scaling for continuous-value recovery. We outline how amplitude-estimation error propagates to decision-variable error and then to objective-value error under standard regularity assumptions, suggesting a distinct width-versus-precision tradeoff relative to discretized approaches. In particular, the framework replaces the logarithmic increase in qubits needed for finer binary precision with a constant cost of one qubit per decision variable, while shifting accuracy requirements into the estimation procedure. We position this approach relative to traditional discretized variational formulations, and argue that it provides a promising new direction for continuous optimization on standard qubit architectures.

We look at Bell inequalities from the lens of information structures in stochastic teams. We consider the usual Clauser-Horne-Shimony-Holt (CHSH) game and a dynamic variant of the same to study how various classes of strategies, classical, projective and quantum, behave under team theoretic solution concepts. We endow players with a fixed quantum state that we take as the Bell state, and allow variation of the measurement strategies. In the static game, projective strategies (where each player performs projective measurements) are team optimal for all parameter values and are closed under best response. In the dynamic game these properties fail: projective strategies may be strictly outperformed by classical ones, best responses to projective strategies need not be projective, and, beyond a sharp threshold on the channel noise, team optimal projective strategies cease to be person-by-person optimal, even though person-by-person optimal projective pairs always exist. These results shed light on the delicate interplay of information structure in quantum strategies and the fragility, under a change of information structure, of structural properties that are taken for granted in the static setting.

We study the finite-temperature stabilizer Rényi entropy of the open critical quantum spin chains. At Rényi index one half, this observable probes the distribution of thermal Pauli-string expectation values and can be written as a sum over absolute values of all square minors of a finite-temperature correlation matrix for the transverse-field Ising chain. We show that this exponentially large sum is exactly reducible to a single Pfaffian. The Pfaffian representation reveals a block Toeplitz–Hankel structure and allows us to extract the large-size scaling in several thermal regimes. In the crossover window where the inverse temperature is proportional to the system size, the stabilizer entropy factorizes into a saturated extensive contribution and a universal finite-size scaling function. We find that this scaling function is a level-eight eta quotient, rather than the ordinary free-boundary Majorana thermal factor. The deviation is exponentially hidden at low temperature but controls the high-temperature crossover, where it gives a Cardy-like asymptotic for the Pauli-string expectation-weight spectrum. These results show that finite-temperature stabilizer entropy reveals hidden defect-like conformal data invisible to ordinary thermodynamic probes.

Markovian open quantum theory assumes evolution with respect to an external classical time parameter, yet no preferred notion of time exists fundamentally in relativistic physics. We resolve this tension by formulating relativistic open quantum dynamics relationally through finite-resolution quantum clocks. Using the Schwinger-Tomonaga formalism, we derive a covariant master equation directly on spacetime hypersurfaces and show that the resulting reduced dynamics is generically non-Markovian even for local interactions. Environmental correlations and clock fluctuations jointly generate the memory kernel, while the Gorini-Kossakowski-Lindblad-Sudarshan (GKLS) structure emerges only after relational coarse-graining, implying that exact Markovian evolution is effective rather than fundamental. In the sharp-clock limit, the formalism reduces to the Anastopoulos-Hu gravitational decoherence equation.

Efficient quantum error correction is essential for the advancement of quantum computing. We propose a quantum neural network with a global structure that reduces the number of unitary matrices required in quantum circuits. This approach resulted in a 97% reduction in training time and up to a 25% improvement in the training completion rate, ultimately achieving a 100% success rate in training while surpassing the error correction performance reported in previous studies. In addition, we demonstrated the enhanced robustness of quantum error correction against internal network noise. Moreover, the fidelity of quantum error correction under internal network noise increased by up to 15% due to the reduced computational load.

This note is written for the special issue of OSID dedicated to the 50th anniversary of the Gorini–Kossakowski–Lindblad–Sudarshan equation. Its purpose is not to give a comprehensive historical review, but rather to reconstruct, in a self-contained way, one logical route leading to the GKLS generator. The emphasis is placed on Kossakowski's structural insight: the combination of Markovianity, complete positivity, trace preservation, and the infinitesimal structure of quantum dynamical semigroups naturally leads to the celebrated form of the generator. I also include a few personal recollections of Andrzej Kossakowski, under whose guidance I spent one year as a postdoctoral researcher in 2003–2004.

We study particle-loss resistant entanglement within the framework of stabilizer and graph states. A pure state is \(m\)-resistant if it remains entangled after the loss of any \(m\) particles and becomes fully separable after the loss of any \(m+1\) particles. The smallest previously unresolved qubit case was the existence of a five-qubit \(1\)-resistant pure state, which is resolved here by the five-cycle graph state \(\ket{C_5}\). A stabilizer-subgroup method is also developed for verifying \(m\)-resistance in graph states, using local stabilizers to certify full separability and exact negative partial transpose~(NPT) witnesses to certify entanglement. Applying this to all graph states associated with non-isomorphic graphs on five, six, and seven vertices, we obtain a graph state classification up to local Clifford equivalence, which also classifies stabilizer states up to local Clifford equivalence. Thus, the five-qubit \(1\)-resistant stabilizer states are exactly the local Clifford class of \(C_5\). Six-qubit \(2\)-resistant stabilizer states exist in three distinct local Clifford classes, whereas no seven-qubit stabilizer state is \(m\)-resistant for any nonzero admissible \(m\). Finally, we prove that the cycle graph states \(\ket{C_N}\) with \(N\ge 7\) are not \(m\)-resistant for any \(0\le m\le N-2\).

An ultimate theory of a phase of matter should describe all its universal properties via quantities that are measurable numerically and experimentally. In this work, we present a microscopic universal theory of symmetry-enriched topological quantum spin liquids (TQSLs) in two spatial dimensions, which directly utilizes microscopically measurable quantities to describe the universal properties. This theory applies to generic TQSLs, which can be Abelian or non-Abelian, chiral or non-chiral. The symmetries are also general, which can include both internal and lattice symmetries, unitary and anti-unitary symmetries, and discrete and continuous symmetries. There can be spin-orbit coupling, the microscopic degrees of freedom may transform linearly or projectively under the symmetries, and the symmetries can permute anyons. The input of the theory is some microscopic states with anyons, operators that control the dynamics of anyons, and symmetry actions in the TQSL, and its output is a set of data characterizing the universal properties, whose underlying mathematical structure is a generalization of category theory. Based on this theory, we find an explicit bijective map between the universal data characterizing a TQSL with a symmetry described by a group $G$, where the symmetry actions may include both lattice and internal symmetries, and the corresponding universal data for a TQSL with only an internal symmetry group $G$, and thus establish a precise crystalline equivalence principle. We demonstrate our theory in symmetry-enriched TQSLs realized on quantum processors based on superconducting qubits, trapped ions, and Rydberg atoms, and in each example we verify the Lieb-Schultz-Mattis anomaly matching condition. Our theory provides a solid basis for identifying and manipulating symmetry-enriched TQSLs, which further paves the way for fault-tolerant quantum computation based on these systems.

We formulate a quantization scheme for open electromagnetic systems with arbitrary passive boundary conditions. Rather than specifying reservoirs phenomenologically, the method identifies them from the dissipation geometry of the Maxwell operator. Factoring the imaginary part of the Maxwell operator gives a bosonic realization of the field operator and separates the fluctuation channels into medium-assisted reservoirs from material absorption and boundary-assisted reservoirs from exchange through the open boundary. Depending on the boundary condition, the latter become free-space radiation modes, impedance-load channels, guided port modes, or more general boundary channels. Green-function input-output relations then follow as an application, yielding frequency-dependent scattering and noise kernels without Markov or single-mode assumptions. To illustrate the practical application, we consider a lossy structure with mixed impedance and outgoing boundaries, and photonic integrated circuit configurations with waveguide port boundaries.

We propose a cascaded Rydberg antiblockade (RAB) regime via a Floquet modulation in four fully connected interacting atoms, which establishes a new synthetic dimension, Dicke-state lattice (DSL), in the space of collective spin excitations. By applying a global periodic driving, we synthesize an effective Hamiltonian that enables perfect state transfer across the five-site DSL with multiple programmable pathways from stepwise nearest-neighbor jumps to a single-step transition. This DSL platform further allows us to simulate a dynamic Su-Schrieffer-Heeger model, where soft quantum control is employed to achieve topologically inspired full RAB $|0000\rangle \to |1111\rangle$ with enhanced robustness against disorder. Moreover, by incorporating the shortcut to adiabaticity technique, we generate high-fidelity entangled twin-Fock and Greenberger-Horne-Zeilinger states on the four atoms within sub-microsecond timescales, outperforming the speed limits of conventional adiabatic protocols. Our work demonstrates a flexible and programmable synthetic dimension for quantum simulation and multipartite entanglement engineering in Rydberg atom arrays, paving the way for the future development of quantum information processing.

Bound states in the continuum (BICs) are spatially localized states embedded in the continuous spectrum without hybridizing with extended bulk modes. Recent advances in topological band theory have greatly enriched the understanding of BICs, which gives rise to boundary-localized topological BICs with extremely high robustness against disorders. However, there remains a challenge in realizing corner-localized topological BICs in a three-dimensional system due to the absence of both realistic theoretical models and effective topological characterization schemes. In particular, how to engineer a controllable number of corner-localized topological BIC is still an open question. Here, we propose that the corner-localized topological BICs can emerge in a class of generalized breathing pyrochlore lattice with general inter-cell hoppings. We further show that the number of BICs at each corner can be arbitrarily adjusted by changing the parameters of inter-cell hoppings. Remarkably, although these corner-localized topological BICs are intertwined with a substantial number of bulk modes, we can accurately characterize them through the polarized topological charges, which are nodal points with topological properties in Brillouin zone and are measurable in experiments. We also reveal three types of topological phase transitions of corner-localized BICs, which are associated with the different ways of closing the bulk energy gap and can be intuitively captured by the polarized topological charges. This work not only promotes the theoretical research of corner-localized topological BICs, but also opens an avenue for their experimental observation in the future.

Combinatorial optimization can be addressed with quantum computing through variational quantum algorithms, but a central challenge in this approach is to design an ansatz expressive enough to explore the feasible subspace of the Hilbert space where the optimal solution lies. Another major challenge is tuning the Lagrange multipliers in penalty terms to enforce feasibility and guarantee solution quality. To address both challenges, we propose the first feasibility-preserving mixed Dicke state ansatz for Hamming weight constrained combinatorial optimization, extending the density matrix formalism to structurally encode equality and inequality constraints directly into the quantum circuit, thereby eliminating the need for penalty terms in the objective function. The proposed framework handles both constraint types, with the pure Dicke state ansatz recovered as a special case corresponding to equality constraints, and generalizes to multiple constraint groups via tensor products of individual pure or mixed Dicke states. We validate the proposed approach in the context of combinatorial portfolio optimization across three experimental scenarios with increasing constraint complexity, using the CMA-ES optimizer and comparing its performance against random search with replacement restricted to the feasible subspace. As the feasible search space grows, the proposed ansatz demonstrates a clear advantage over random search in terms of the number of objective function calls required to identify high-quality solutions. Hardware experiments on IBM NISQ processors confirm that noise mitigation and circuit transpilation optimizations remain open challenges for practical deployment. The framework is general and directly applicable to other combinatorial optimization problems with Hamming weight constraints.

Quantum-entangled measurements are known to enable multi-party behaviors that are impossible with unentangled measurements on nonlocal resources, even those that are super-quantum and bound only by the no-signaling principle. This advantage can be witnessed by the entanglement swapping protocol, along with corresponding impossibility results for "nonlocality swapping". However, the advantage assumes the absence of pre-existing nonlocal resources shared by the swapped-to parties; it no longer holds if all pairs of parties are allowed to share bipartite nonlocal resources. Here, we consider a resource-theoretic perspective in which bipartite nonclassical resources are free resources that can be shared by any pair of parties in a multipartite network, and ask whether quantum entangled measurements can still provide an advantage over certain basic measurements, known as wirings, of nonsignaling nonlocal resources. We resolve this question in the affirmative by demonstrating an explicit four-party behavior that can be achieved with bipartite quantum resources subject to entangled measurements, and cannot be achieved if the bipartite resources are allowed to be more general nonsignaling nonlocal "boxes" so long as the measurements are restricted to local wirings, even also allowing for globally shared classical randomness. Furthermore, the argument generalizes: the same separation can be witnessed for K+2 parties with access to K-partite nonlocal resources for any K > 2. We also examine these results in different contexts, such as the star network configurations and scenarios not admitting globally shared classical randomness, further enhancing understanding of the capabilities of entangled measurements in multi-party configurations.

The Schrödinger cat state protocol (SCSP) makes use of a Schrödinger cat (SC) state generated with one-axis-twist squeezing (OATS) to enhance the sensitivity. In principle, the SCSP can magnify the phase shift by a factor N, the number of atoms under interrogation, accompanied by a quantum noise amplification by a factor of root-N, enabling an atomic sensor to reach the Heisenberg limit. However, the current SCSP can reach this benchmark only if the parity of N is known, which is almost impossible for a large ensemble. The reason is the orientation of the SC state generated with the conventional OATS depends on the parity of N. In this paper, we propose a modified OATS (MOATS) operation that generates the SC state aligned in a certain direction regardless of the parity of N.

The echo squeezing protocols (ESPs) are techniques that amplify the phase shift in a quantum sensor with one-axis-twist squeezing (OATS). For atomic sensors, spontaneous emission (SE) in the OATS operations is an important imperfection, which, however, is prohibitively difficult to study. SE can transfer atoms to all the Zeeman substates, making the number of relevant collective states excessively large. In this paper, we focus on a relatively simple isotope, namely Sr-88, which only has one Zeeman substate in each of the two ground states. Nevertheless, studying the effect of SE is still challenging because SE will populate all collective states, either symmetric or asymmetric, thus putting the ensemble into a mixed state. Based on analytical derivation and numerical simulations, we conclude that the GESP is more resistant to SE than the conventional echo squeezing protocol (CESP). This is another advantage of the GESP that has not been realized formerly. We also find that for the GESP employing Sr-88, the SE-induced reduction in the signal contrast is the same as that for a Ramsey protocol without squeezing and the quantum noise is suppressed by SE. This is an unexpectedly favorable result. The SE-induced suppression of quantum noise constitutes a previously unrecognized effect that challenges both earlier conclusions and intuitive expectations.

Neutral-atom arrays combine scalable qubit registers, long coherence times, flexible optical control, and strong Rydberg-mediated entangling interactions, making them a promising platform for quantum information processing. However, physical error rates remain a challenge, and fault-tolerant quantum error correction (QEC) requires repeated mid-circuit measurement and reset of ancilla qubits without disturbing nearby data qubits. This requirement introduces significant control and architectural overhead, making qubit encoding an important architectural decision. Here, we propose a dual metastable-state qubit encoding for $^{171}\mathrm{Yb}$ atoms that utilizes two independent qubit subspaces in the $(6s6p)\,{}^3\mathrm{P}_0$ and $(6s6p)\,{}^3\mathrm{P}_2$ manifolds. The ${}^3\mathrm{P}_0$ manifold provides a long-coherence nuclear-spin (NS) qubit suitable for storage and arithmetic operations, while the ${}^3\mathrm{P}_2$ manifold provides a hyperfine-spin (HF) qubit, with $\Delta_{\mathrm{HF}} = 2\pi \times 6.7~\mathrm{GHz}$, that enables fast Raman operations and direct state-selective imaging. Coherent shelving between the two metastable manifolds connects the qubit subspaces, allowing operations to be assigned to spectrally distinct processor zones. We simulate single-qubit and two-qubit gate fidelities in ${}^3\mathrm{P}_2$, as well as coherent shelving between the HF and NS qubit subspaces. We incorporate these physical-level estimates into an architectural resource estimation and logical-level simulation. Our approach integrates mid-circuit measurements and fast qubit operations within a single-species platform, providing a versatile framework for future fault-tolerant quantum computing with neutral-atom qubits.

We present a quantum algorithm for the Kravchuk transform that scales logarithmically in both the dimension and the inverse of the error parameter. The quantum Kravchuk transform maps computational basis states to states with amplitudes proportional to Kravchuk functions. We achieve this by combining two key techniques: the structural relationship between the Kravchuk transform and the Lie algebras $\mathfrak{su}(2)$, and a recent fast-forwarding simulation method for $\mathfrak{su}(2)$ operators in the oscillator representation. More precisely, we first establish the map from Kravchuk transform in computational basis to $\mathfrak{su}(2)$ in Fock basis. Then built on this connection, we apply the fast-forwarding to achieve an efficient quantum Kravchuk transform.

Advancing superconducting quantum devices to higher operating frequencies broadens their functionality and enables operation at elevated temperatures, but it also requires near-quantum-limited amplifiers beyond the few-gigahertz regime. Here we present a junction-free, kinetic-inductance parametric amplifier based on thin-film niobium nitride (NbN) operating at 23 GHz in the microwave K-band, achieving a gain up to 40 dB, a 100 MHz gain-bandwidth product, a 1 dB saturation input power of -85 dBm with 23 dB gain, and added noise no greater than 1.4 quanta for phase-preserving amplification. Leveraging the large superconducting gap of NbN, this architecture can be extended to even higher frequencies, supporting applications such as high-fidelity readout of millimeter-wave superconducting qubits and axion searches over an expanded mass window.

During the century of existence of the notion of coherent states, either linear or nonlinear, several schemes for their construction, theoretical or experimental, have been developed. Generally, the mathematical structure of coherent states depends on the choice of ladder operators, and consequently on the structure constants. In this paper, we propose a way to construct generalized coherent states for anharmonic oscillators that is based on a diagonal operator ordering technique (DOOT) applied to generalized hypergeometric functions, that is, on some of the most general special functions. These states are generated by the action of a pair of ladder operators, the creation and the annihilation, whose ordered normal product is equal to the dimensionless Hamiltonian of the quantum system. In addition, the action of these operators is easy to find if the expression for the dimensionless energy eigenvalues is known.

Stochastic nonlinear dynamics underlie many models in engineering and computational physics, yet accurate high-dimensional simulation remains challenging. We present a quantum algorithm for a broad class of $N$-dimensional stochastic differential equations with dissipation and quadratic drift. The algorithm applies to strongly nonlinear systems with all-to-all interactions, thereby extending the scope of previously known quantum algorithms that were limited to weak nonlinearity and sparse systems. For norm-preserving drifts, a condition satisfied by key fluid dynamics discretizations, our method approximates expectation values of low-order correlation functions with rigorous error bounds at a cost polynomial in $\log{(N)}$ and linear in the evolution time. Our main technical advance is a subroutine for simulating an auxiliary system of $N$ interacting quantum harmonic oscillators with cost polylogarithmic in $N$. Finally, we formulate turbulence models, including Navier-Stokes and damped Euler equations, within this framework, opening a route to quantum simulation of strongly nonlinear SDEs governing turbulence and nonlinear wave dynamics.