Papers

We present a compact, automated, high-brightness entangled photon source capable of generating all four Bell states with high fidelity. The system utilizes a type-0 quasi-phase-matched PPKTP crystal embedded within a polarization Sagnac interferometer. We introduce a switching scheme based on the controlled, motorized translation of the nonlinear crystal. This device is capable of generating any one of the Bell states on-demand. Experimentally, we demonstrate that translating the crystal from the interferometer's balanced position repeatedly toggles the state between $|\phi^+ \rangle$ and $|\phi^- \rangle$ (as well as $|\psi^+ \rangle$ and $|\psi^- \rangle$) at regular intervals of $122 \pm 14 ~\mu m$. Subsequently, a half-wave plate (HWP) in the idler arm transitions between the quantum states $|\phi^{\pm}\rangle$ and $|\psi^{\pm}\rangle$. While the non-collinear geometry imposes an upper limit on the translation range as verified via EMCCD imaging, the source however, displays very little change of intensity in the operational window. State purity and entanglement are certified through quantum state tomography (QST), visibility measurements, Bell state measurements (BSM), and CHSH inequality violations, confirming that the source is robust and provides a repeatable, high-fidelity output.

We investigate the spin resolved vortex properties of electron positron pairs created from vacuum in time delayed, two color electromagnetic fields. By treating the temporal delay G as a continuous tuning parameter, we reveal a dynamic transition from interference-dominated domain patterns at G=0 to the nucleation of quantized vortex lattices at G=0.5. These topological structures exhibit a staggered arrangement analogous to von Karman vortex streets in fluid dynamics. We demonstrate that the momentum-space morphology is strictly governed by spin orbit selection rules, i.e., parallel spin configurations enforce a dipole-like connectivity, while anti-parallel configurations resolve into distinct quadrupole structures. This difference originates from the conservation of total angular momentum Jz, where the spin projection determines the required orbital angular momentum Lz of the created pairs. At large delays (G greater than 1), macroscopic vortex coherence dissolves into a chaotic phase landscope due to multi-channel interference, yet the spin-dependent nodal geometries remain robust. Our findings suggest that these topological signatures provide a high-fidelity diagnostic for the quantum dynamics of vacuum excitations in strong field QED.

The fate of non-trivial many-body states subject to decoherence is of both fundamental and practical interest. Here, we demonstrate a new analytic technique that allows for an exact treatment of dynamics of observables in matchgate circuits subject to arbitrary Pauli noise. We use this to obtain new insights on how decoherence influences critical ground states, focusing on the 1D transverse field Ising model subject to local Markovian Pauli noise. While such noise cannot kill the critical behavior of spin correlation functions, we show that it does lead to a surprising non-equilibrium state, with experimental signatures that are measurable without requiring post-selection or multiple copies of the system. Despite the infinite-temperature nature of the dissipation, the decohered state is characterized by a thermal distribution of low-energy quasi-particles. This is the direct consequence of a noise-induced emergent length scale that manifests itself in fermionic correlators. We show how these phenomena are directly accessible in experiments using a single probe qubit, and that our results also hold for a different dephased critical state (that of an XX spin chain in the zero magnetization sector).

The non-Hermitian skin effect can lead to directional amplification of response, with the associated end-to-end Green's function generally exhibiting size dependence. Any deviation in length or local disorder can drastically alter the amplification factor, rendering the response fragile in practical implementations. In this work, we identify a new type of scale-free, topological, and directionally amplified response in a Hatano-Nelson model under perturbed open boundary conditions. The scale-free response can be attributed to the first order boundary effect and characterized by a winding number defined on a continuous generalization of the finite-size Brillouin zone-a concept introduced in this work. Such scale-free behavior endows the end-to-end Green's function with significant robustness and making it promising for practical applications.

Standard quantum error correction (QEC) models typically assume discrete, Markovian noise, obscuring the continuous quantum nature of physical environments. In this manuscript, we investigate the fundamental limits of an actively corrected surface code coupled to a continuous, un-reset quantum environment at zero and finite temperature. Using the generalized Caldeira-Leggett framework, we map the long-time evolution of the logical qubit to a boundary conformal field theory, establishing an exact equivalence to the anisotropic Kondo model. We evaluate computational times for a finite code distance $L$ for all spatial and temporal correlations. Our analysis reveals that a true thermodynamic threshold exists strictly for short-range environments ($z>1/(s+1)$). In critical or long-range regimes, the macroscopic footprint of the code weaponizes the continuous bath, hindering the topological protection.

Quantum entanglement in systems of identical particles is often obscured by the interplay between exchange-induced correlations and the operational framework used to define entanglement. To study the role of exchange statistics, we propose a scheme using two distinguishable particles where an exchange symmetry is artificially engineered via a relative phase $\theta$ in the initial state. This approach allows continuous tuning from bosonic ($\theta = 0$) to fermionic ($\theta = \pi$) statistics. By monitoring the interplay between purity and coherence, we uncover distinct dynamical regimes dictated by the interaction strength $U$ and the phase $\theta$. For particles initially loaded in a bound state, strong $U$ suppresses coherence development by avoiding the scattering band, reducing the purity toward its minimum. For particles initially on neighboring sites, coherence grows linearly in time. While non-symmetric inputs feature a sharp purity reduction at intermediate $U$, due to the competition between bound and unbound states, symmetric initial conditions produce transient coherence bursts that significantly enhance the purity. More generally, tuning the phase $\theta$ reveals a high-purity region over a range of $\theta$ at intermediate interactions, with the purity collapsing to $1/2$ as $\theta$ approaches the fermionic limit. Our results show that the imposed statistics, or lack thereof, reshapes the entanglement dynamics and its response to the interaction $U$.

The negatively charged nitrogen-vacancy center (NV$^-$) in diamond is a versatile platform for quantum magnetometry under ambient conditions. Recently, laser threshold magnetometry (LTM) has been proposed as a means to significantly enhance the sensitivity of NV-based magnetometers by incorporating a diamond hosting NV$^-$ centers within a laser cavity and operating near threshold. While demonstrations have validated the concept, practical implementations remain technically demanding, requiring high pump powers and precise alignment of free-space cavities. It remains unclear whether the benefits of operating near threshold will outpace increased laser noise. In this work, we integrate an NV-diamond with a high NV$^-$ content into a compact external cavity diode laser and demonstrate singlet infrared absorption optically detected magnetic resonance (ODMR). The system exhibits exceptional threshold current stability, enabling ODMR using the threshold current as the read-out parameter. We report a five-fold enhancement in the ODMR contrast by operating near threshold. The best magnetic field sensitivity of $7.6~\mathrm{nT/\sqrt{Hz}}$ (DC-500 Hz) is achieved well above threshold, while near threshold sensitivity is limited by increased probe laser noise. These results establish a compact and mechanically robust platform for singlet absorption-based NV$^-$ magnetometry and highlight key trade-offs between contrast enhancement and laser noise near threshold.

Quantum scientific computing is to solve engineering and science problems such as simulation and optimization on quantum computers. Solving ordinary and partial differential equations (PDEs) is essential in simulations. However, existing quantum approaches to solve nonlinear PDEs suffer from the issues of curse of dimensionality and convergence during the linearization process. In this paper, a Lindbladian homotopy analysis method (LHAM) is proposed as a quantum differential equation solver to simulate nonlinear and nonunitary dynamics. The original nonlinear problem is first converted to a recursive sequence of linear PDEs with the homotopy analysis method and reformulated as a higher-dimensional lower block triangular linear homogeneous autonomous system. The solution is then embedded in the density matrix and obtained through the Lindblad dynamics simulation. Compared to other methods such as Carleman linearization and the Koopman-von Neumann approach where the dimension of Hilbert space increases polynomially with the inverse of truncation error, the Hilbert space dimension in LHAM increases only logarithmically. LHAM is demonstrated with nonlinear PDEs including Burgers' equation and reduced magnetohydrodynamics equations.

In a global derivatives market with notional values in the hundreds of trillions of dollars, the accuracy and efficiency of pricing models are of fundamental importance, with direct implications for risk management, capital allocation, and regulatory compliance. In this work, we employ the Black-Scholes-Merton (BSM) framework not as an end in itself, but as a controlled benchmark environment in which to rigorously assess the capabilities of quantum machine learning methods. We propose a fully quantum approach to option pricing based on Quantum Neural Networks (QNNs), and, to the best of our knowledge, present one of the first implementations of such a methodology on currently available quantum hardware. Specifically, we investigate whether QNNs, by exploiting the geometric structure of Hilbert space, can effectively approximate option pricing functions. Our implementation utilizes a compact 2-qubit QNN architecture evaluated across multiple state-of-the-art quantum processors, including IBM Fez, IQM Garnet, IonQ Forte, and Rigetti Ankaa-3. This cross-platform study reveals distinct hardware-dependent performance characteristics while demonstrating that accurate pricing approximations can be achieved consistently across different devices despite the constraints of Noisy Intermediate-Scale Quantum (NISQ) hardware. The results provide empirical evidence that QNN-based approaches constitute a viable framework for derivative pricing. While the analysis is conducted within the BSM setting, the broader significance lies in the potential extension of these methods to more realistic and computationally demanding models, including local volatility, stochastic volatility, and interest rate frameworks commonly used in practice.

Arrays of ultracold neutral atoms, also known as Rydberg atom arrays, are rapidly developing into a powerful and versatile platform for quantum simulation. However, theoretical predictions about the bulk quantum phases of matter present in these systems have often diverged from experimental realizations on finite-sized arrays due to the strong effects of the boundaries. Here we propose a general, experimentally straightforward strategy to mitigate the effects of the boundaries and thus enable finite-sized arrays to stabilize bulk-like quantum order. Our scheme makes use of the properties of the ubiquitous disordered phase in Rydberg systems, driving the boundaries into an unbiased set of configurations that depend on the bulk physics. We numerically demonstrate the efficacy of this protocol in one- and two-dimensional systems on both ordered and critical phases.

Light with complex structures in polarization, phase and amplitude, has attracted a lot of attention in a broad range of applications and fundamental studies in classical and quantum optics. Along with the increased interest in structured light comes a need for efficient modulation platforms operating simultaneously for many modes. Multi plane light conversions (MPLC), i.e., multiple consecutive phase modulations in combination with free space propagation, have enabled such unitary transformations, which are usually built by bulky optical components, limited to scalar modulation, or rely on advanced nanofabrication techniques. Here, we demonstrate an efficient, monolithic MPLC architecture through direct laser writing in standard fused silica glass, resulting in a device with a compact form factor of only a few cubic millimeters. Our scheme is based on volumetric engineering of the glass's birefringence through laser-written nanogratings, which enables spatial control over full vectorial light structures. To showcase the approach's potential for integrated multimode-multipath optical networks, we demonstrate multi-mode unitary transformations, mode conversions, and complex beam-splitting for scalar light. We further extend the MPLC operation to vectorial light and implement various polarization-controlled spatial mode operations as well as the transformation of the topology of an optical Skyrmion. Finally, we highlight our scheme's promise for optical communications and implement a miniaturized multiplexer for spatial modes and polarization operating at telecom wavelength.

The quantum instrument (QI) formalism is required to model mid-circuit measurements (MCMs) and the dependence of the post-measurement state on the measurement outcome. Correctly modeling QIs is essential for applications using MCMs, such as adaptive circuits and quantum error correction. Although QIs yield a joint quantum-classical state after measurement, errors in QIs can still be represented by a $d^2 \times d^2$ superoperator (e.g., process or transfer matrix) for each outcome, just as superoperators describe Markovian errors on unitary gates. However, because the joint quantum-classical system has a distinct error model for each outcome, this complicates the usual interpretation of process- or transfer-matrix error models. This Note offers practical guidance on understanding and interpreting QI error models.

Barren-plateau results have established exponential gradient suppression as a widely cited obstacle to the scalability of variational quantum algorithms. When and whether these results extend to a given objective has been addressed through loss-specific arguments, but a general structural characterization has remained open. We show that the objective itself admits a fixed-observable representation if and only if the loss is affine in the measured statistics, thereby identifying the exact boundary of the standard concentration-based proof template. Existing transfer results for non-affine losses achieve this reduction under additional assumptions; our characterization implies that such a reduction is not structurally available for a class of non-affine objectives, placing them outside the automatic reach of the existing proof template. Beyond the affine regime, a chain-rule decomposition reveals three governing factors – model responsivity, loss-side signal, and transmittance – and induces a loss-class dichotomy: bounded-gradient losses inherit suppression, while amplification-capable losses can in principle counteract it. In the exponentially wide setting, both classes fail, but for different structural reasons. When the interface is instead designed at polynomial width – exposing coarse-grained statistics rather than individual bitstring probabilities – the exponential-dimensional obstruction is relaxed and the dichotomy plays a genuine role. In a numerical demonstration on a charge-conserving quantum system, the amplification-capable objective produces resolved gradients several orders of magnitude larger than affine and inheriting baselines at comparable shot budgets. Over the tested interval, its scaling trend is statistically distinguished from the exponential trend of both alternatives. The boundary is affine; what lies beyond it is a representation-design problem.

We introduce QuIC, a training-free quantum graph embedding that maps graphs to sorted output distributions via a fixed parameterized circuit. In the ideal one-repetition setting, we prove that the resulting sorted distribution is permutation-invariant and injective on labeled graphs under an irrational-angle condition, yielding completeness on isomorphism classes for the ideal one-repetition exact-arithmetic embedding. We then use those ideal structural properties to motivate a practical embedding pipeline and study how much of that behavior survives under finite-shot estimation, truncation, realistic noise, transpilation, and hardware execution. The sorted distribution concentrates discriminative signal in a compact head, making fixed-length head truncation an effective practical operating point in the tested regimes. Under noise-model simulation, all tested graph pairs satisfied the study's operational separation criterion, including strongly regular graph pairs that are standard 2-WL stress tests and CFI families used as hard instances for fixed-k WL methods. A hardware study comprising 14,800 transpiled circuits across 37 CFI families on IBM Heron (ibm_fez, 156 qubits), including paired one- and two-repetition evaluations, reports empirical separation up to 66 qubits for the tested families under the reported execution protocol, identifies a device-dependent depth limit near 210-250 layers, and characterizes the current practical boundary of the method under the reported execution protocol.

This research investigates the performance and efficacy of machine learning models in stock prediction, comparing Artificial Neural Networks (ANNs), Quantum Qubit-based Neural Networks (QQBNs), and Quantum Qutrit-based Neural Networks (QQTNs). By outlining methodologies, architectures, and training procedures, the study highlights significant differences in training times and performance metrics across models. While all models demonstrate robust accuracies above 70%, the Quantum Qutrit-based Neural Network consistently outperforms with advantages in risk-adjusted returns, measured by the Sharpe ratio, greater consistency in prediction quality through the Information Coefficient, and enhanced robustness under varying market conditions. The QQTN not only surpasses its classical and qubit-based counterparts in multiple quantitative and qualitative metrics but also achieves comparable performance with significantly reduced training times. These results showcase the promising prospects of Quantum Qutrit-based Neural Networks in practical financial applications, where real-time processing is critical. By achieving superior accuracy, efficiency, and adaptability, the proposed models underscore the transformative potential of quantum-inspired approaches, paving the way for their integration into computationally intensive fields.

Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous benchmarks against strong classical baselines remain scarce. We present a comprehensive empirical study of quantum kernel support vector machines (QSVMs) across nine binary classification datasets, four quantum feature maps, three classical kernels, and multiple noise models, totalling 970 experiments with strict nested cross-validation. Our analysis spans four phases: (i) statistical significance testing, revealing that none of 29 pairwise quantum-classical comparisons reach significance at $\alpha = 0.05$; (ii) learning curve analysis over six training fractions, showing steeper quantum slopes on six of eight datasets that nonetheless fail to close the gap to the best classical baseline; (iii) hardware validation on IBM ibm_fez (Heron r2), demonstrating kernel fidelity $r \geq 0.976$ across six experiments; and (iv) seed sensitivity analysis confirming reproducibility (mean CV 1.4%). A Kruskal-Wallis factorial analysis reveals that dataset choice dominates performance variance ($\varepsilon^2 = 0.73$), while kernel type accounts for only 9%. Spectral analysis offers a mechanistic explanation: current quantum feature maps produce eigenspectra that are either too flat or too concentrated, missing the intermediate profile of the best classical kernel, the radial basis function (RBF). Quantum kernel training (QKT) via kernel-target alignment yields the single competitive result – balanced accuracy 0.968 on breast cancer – but with ~2,000x computational overhead. Our findings provide actionable guidelines for quantum kernel research. The complete benchmark suite is publicly available to facilitate reproduction and extension.

Considerable effort has been devoted to developing techniques for witnessing and characterizing quantum resources that emerge from collective properties of a set of states. In this context, Bargmann invariants play a central role: they witness coherence and related resources, and underpin important applications. In this work, we introduce a unified formalism that fully characterizes and organizes the capability of Bargmann invariants to witness different manifestations of coherence in sets of states. It is formulated around the construction of Bargmann scenarios, which specify relevant tuples of Bargmann invariants, and Bargmann polytopes, which bound the values that said invariants can have when the states are incoherent. We study their basic geometry, connect them to existing formalisms, and illustrate their physical relevance. Our construction opens new opportunities for the certification of quantum devices and lays the path toward a full quantum resource theory based entirely on multivariate traces of states.

We demonstrate the generation of sub-Poissonian twin fields via near-degenerate spontaneous four-wave mixing (SFWM) in warm $^{85}\mathrm{Rb}$ vapor at 795~nm. When seeded with a weak coherent field, the generated twin beams exhibit approximately $5.5~\mathrm{dB}$ of intensity-difference squeezing in free space and retain about $3~\mathrm{dB}$ after coupling into polarization-maintaining (PM) fibers. Under vacuum seeding, time-resolved photon-counting measurements yield Mandel parameters of $Q\approx-0.7$ for each individual field, demonstrating strong photon-number squeezing. To explain these observations, we develop a finite-resource saturation model in which occupation-dependent SFWM gain, arising from competition for a finite nonlinear gain resource, suppresses large photon-number fluctuations within an effective collective mode selected by the PM-fiber spatial projection, thereby producing the observed negative Mandel-$Q$ parameters. The temporal cross-correlation between the twin photons exhibits a distinctive flat-topped profile resulting from the interplay of multiple $\chi^{(3)}$ processes in the atomic medium and is in excellent agreement with the theoretical model. Combining high photon flux, near-resonant operation, robust sub-Poissonian photon statistics, and fiber compatibility, this source provides a promising platform for scalable quantum-enhanced sensing and quantum information processing.

We investigate spontaneous radiative processes in a driven polar two-level system whose interaction with the laser field is dominated by broken inversion symmetry rather than by the usual transition dipole coupling. Using a polaron transformation, we derive the dressed eigenstates of the atom-laser system and show that their longitudinal coupling reshapes the spectrum into two displaced harmonic ladders. We then analyze spontaneous transitions induced by a bosonic reservoir, and obtain transition rates that depend on both the laser parameters and the overlap between displaced field states. In the few-photon regime, we identify conditions under which spontaneous emission from the excited state can be strongly suppressed, thereby extending its lifetime, as well as regimes where the ladder structure enables spontaneous absorption from the ground state. In the semiclassical limit of a strong coherent drive, we derive compact analytical expressions for the total transition rates and show that they are governed by Bessel-function weights associated with multiphoton channels. Our results show how broken inversion symmetry qualitatively modifies decay dynamics and radiative cascades, and they establish driven polar quantum systems as a platform for controlling spontaneous light emission beyond the standard inversion-symmetric setting.

Two-path complementarity in polarized interferometric fields is reconsidered by retaining the complete path–polarization density matrix instead of reducing the description to the path degree of freedom from the outset. The familiar relation connecting the Cartesian visibility components, path predictability, and reduced-state mixedness is recovered as a marginal consequence of the reduced path state and is not interpreted as a new complementarity law. Attention is focused instead on the full path–polarization description in a real reference basis adapted to the path and linear-polarization degrees of freedom. Within this framework, the normalized purity separates naturally into path, polarization, and path–polarization-correlation contributions, while the antisymmetric sector provides a sector-resolved measure of cohesion. The resulting decomposition identifies which parts of the complete state store phase-sensitive interferometric coherence and which contributions are removed when polarization is traced out. The formalism therefore provides a sector-resolved accounting of complementarity within the full path–polarization state and clarifies the connection between reduced visibility loss, polarization marking, path–polarization correlations, and quantum-eraser recovery. The present article establishes the framework and associated purity decomposition; a more detailed exploration of full path–polarization complementarity and its dynamical aspects is left for future work.

We develop a unified quantum framework for subgraph counting in graphs. We encode a graph on $N$ vertices into a quantum state on $2\lceil \log_2 N \rceil$ working qubits and $2$ ancilla qubits using its adjacency list, with worst-case gate complexity $O(N^2)$, which we refer to as the graph adjacency state. We design quantum measurement operators that capture the edge structure of a target subgraph, enabling estimation of its count via measurements on the $m$-fold tensor product of the adjacency state, where $m$ is the number of edges in the subgraph. We illustrate the framework for triangles, cycles, and cliques. This approach yields quantum logspace algorithms for motif counting, with no known classical counterpart.

We investigate discriminability from an operational and contextuality-oriented perspective using a two-copy comparison game based on SWAP-type measurements. The resulting score $D_{\mathrm{op}}$ provides an experimentally accessible notion of distinguishability that does not rely on a minimum-error discrimination task. We first examine whether this discriminability game can directly witness preparation contextuality. Within a preparation-noncontextual ontological model, we derive a direct upper bound on the game score under a SWAP-like comparison rule and a sharp single-copy test, and show that this bound is saturated in the natural qubit realization. Thus, the direct game alone does not provide a contextuality witness in that regime. We then consider a Bell-coupled scenario in which two-copy comparison measurements are applied to Bob's conditional preparations. This yields a state-dependent upper bound on the CHSH value in terms of operational separation parameters, and hence in terms of the distinguishability of the conditional states. Our results establish a quantitative link between operational discriminability and the strength of nonclassical correlations, showing that discriminability can act as an operational resource constraining Bell-type nonclassical correlations.

Dynamic quantum circuits integrate unitary evolution with mid-circuit measurement and feedforward, enabling conditional operations essential for efficient quantum algorithms and foundational for fault-tolerant quantum computation. However, such operations introduce measurement-induced errors and control constraints that are not addressed by conventional error-suppression techniques. Here, we introduce an empirical learning framework that optimizes dynamical decoupling (DD) sequences for dynamic circuits at the level of circuit subintervals and qubit subregisters. Applying empirically learned DD sequences, we achieve a three-fold reduction in average dynamic circuit error rates as measured via randomized benchmarking. We apply the learned strategies to the dynamic circuit implementation of the quantum Fourier transform with measurement (QFT+M), demonstrating nontrivial process fidelity on connected chains of up to 20 qubits. Applying the resulting enhancement, we perform a high signal-to-noise QFT immediately following the preparation of a 10-qubit entangled state. Our results demonstrate that empirically optimized DD systematically outperforms theoretically derived sequences for dynamic circuits, establishing it as an efficient approach for error suppression in dynamic quantum circuits, with direct relevance to applications requiring measurement and feedback such as quantum error correction.

The effective description of a bosonic quantum system identifies the minimum finite dimension required to capture its essential dynamics. This effective dimension plays an important role in the complexity of classical and quantum algorithms for learning and simulating bosonic systems. While generic bosonic states require a dimension scaling as $1/\epsilon^2$ for a precision of approximation $\epsilon$, here we identify a natural energy condition which allows us to improve this scaling exponentially to $\log(1/\epsilon)$. We then prove that most bosonic quantum states satisfy this condition, and in particular those produced by combining Gaussian dynamics with generic energy-preserving dynamics, which include the output states of universal bosonic quantum circuits. We apply this finding to enhance learning algorithms for bosonic quantum states and we further obtain new classical simulation algorithms for a large class of bosonic systems. Finally, using efficient decompositions of Kerr gates as sums of Gaussian gates, we significantly refine these classical simulation algorithms for universal bosonic quantum circuits. Our results demonstrate that physical bosonic systems are significantly more well-behaved than previously assumed, allowing for efficient descriptions even at high precision.

Quantum low-density parity-check codes are promising candidates towards scalable fault-tolerant quantum computation. Among these, bivariate bicycle (BB) codes offer superior encoding rates and large code distance compared to surface codes. However, their requirement on long-range stabilizer measurements poses significant challenges for implementation on realistic hardware with limited connectivity, such as superconducting circuit platforms. In this work, we introduce a novel hardware-software co-design that leverages a programmable communication network architecture to address these limitations. Our approach utilizes a 2D toric network of oscillators as a flexible communication fabric linking qubits at each site. Such architecture significantly reduces the number of long-range couplers required from $O(n)$ to $O(\sqrt{n})$. Dual-rail qubits, along with native gates including Swap-Wait-Swap gates and beamsplitter SWAPs, ensure that long-range two-qubit gates can be executed with high fidelity and low latency. To further enhance performance, our qubit layout and routing algorithm utilize symmetries of the codes and enable maximum parallelism for long-range two-qubit gates, maintaining a low syndrome extraction cycle duration and scalability over the code length. We perform circuit-level simulation with realistic noise modeling based on experimental hardware parameters, observing an logical error rate per logical qubit per cycle of 3.06% for $[[18, 4, 4]]$ BB code, 2.6$\times$ less than the existing experimental result. These findings provide a practical roadmap and identify key technological advancements needed to achieve low-overhead fault-tolerant quantum computing at scale.

We study synchronization in the XX qubit chain subject to local or multi-local amplitude-damping noise. Analyzing the decoherence-free subspace (DFS) structure of the model, we show that it is completely determined by a simple number-theoretic function involving the noise sites and the chain length. We derive a closed-form expression for local qubit observables restricted to the DFS and prove that stable synchronization of the edge qubits for arbitrary initial states occurs if and only if the DFS supports exactly one single-excitation eigenstate. We further show that this same condition also guarantees constant asymptotic entanglement between the edge qubits, so that generic stable synchronization and constant asymptotic entanglement necessarily coexist. By contrast, when the DFS supports multiple single-excitation eigenstates, synchronization becomes initial state dependent and may be entirely absent, even though stable oscillatory entanglement can persist indefinitely.

Global control offers a promising route to scalable quantum computing. A recent conjecture by Hu et al. (arXiv:2508.19075) proposes that any connected qubit graph equipped with global Ising-type interactions and tunable global transverse fields achieves universality if and only if an additional control field breaks every non-trivial automorphism of the underlying graph. We disprove this conjecture by exhibiting explicit seven- and nine-qubit counterexamples: connected graphs with trivial automorphism group for which the generated Lie algebra is nonetheless not universal. Our analysis reveals that graph automorphisms capture only part of the Hamiltonian symmetry structure: there exist hidden symmetries beyond the automorphism group of the graph. Additionally, in the case of non-trivial automorphism group, we find control terms which break the graph symmetries but are still not universal. These findings sharpen the characterization of universality for globally controlled quantum systems.

High temperature is usually expected to destroy order: as the Gibbs state approaches the infinite-temperature limit, it becomes an equal-weight ensemble over all states and the system is generically disordered. Recent works showed that entropic order can violate this expectation through coupling to bosons in classical lattice models and quantum field theories, where the ordered states have higher entropy. Here we present new analytic methods for constructing quantum lattice models that exhibit entropic orders. In particular, we construct quantum lattice models with continuous symmetry breaking at high temperature in 1+1 dimensions and clarify how entropic order can evade the Hohenberg-Mermin-Wagner theorems. We also construct high-temperature entropic $p+ip$ chiral topological superconducting states in 2+1 dimensions with temperature-independent anyon correlation functions. In addition, we obtain a broad family of high-temperature entropic non-chiral topological orders. We show that the entropic topological orders have strong higher form symmetries at high temperature unlike the conventional topological orders, and the symmetry is spontaneously broken. These results follow from two general constructions that couple a given lattice model with a low-temperature ordered phase either to ordered bosons or, for local commuting-projector Hamiltonians, to more general bosonic degrees of freedom.

Quantum machine learning has attracted significant interest in recent years. Most existing approaches, however, are variational in nature and require extensive parameter optimization subroutines. Here, we propose a conceptually distinct quantum machine learning approach that goes beyond the variational paradigm. Harmoniq takes a recently developed data augmentation technique from quantum harmonic analysis and implements it as a stochastic mixture of n-qubit circuits with at most quadratic depth each. A key strength of Harmoniq is its modularity: viewed as a quantum process acting on density matrices, it can readily be combined with other quantum data processing and learning subroutines. A subsequent case study demonstrates this modularity by combining Harmoniq with stochastic amplitude encoding for the input density matrix and quantum PCA on the output density matrix. This results in a promising signal denoising pipeline that works particularly well in the small sample size regime.

We demonstrate dynamical control of the effective spin-spin interaction, dominated by Fermi-contact interaction, in a hybrid spin system via parametric modulation. We show that, in an alkali-noble-gas comagnetometer, periodic modulation of the direction of the electron spin polarization with respect to the nuclear polarization leads to a Floquet-induced renormalization of the spin-exchange coupling, governed by a zeroth-order Bessel function. This effect enables continuous tuning and suppression of the effective interaction strength without altering the intrinsic properties of the system. We develop a theoretical model that supports the experimental measurements. The results establish a general mechanism for controlling interaction strengths in hybrid atomic systems and provide new opportunities for precision measurements and quantum memories.