Papers

For a single trapped ion, the excitation spectrum of a narrow optical transition consists of a Doppler- and recoil-free carrier accompanied by motional sidebands, which are equally spaced by the trap secular frequency and lie under a Doppler-broadened envelope that is shifted by the photon recoil. Outside the Lamb-Dicke regime, the large photon recoil distributes the line strength across many sidebands and suppresses excitation of the carrier. With multiple ions, the motional spectrum becomes dense, and the carrier is further weakened. Here, we predict a counterintuitive revival effect: increasing the number of ions in a linear chain can restore strong carrier excitation even under trapping conditions far from the single-ion Lamb-Dicke regime. Using a quantum-mechanical model of the excitation dynamics in linear ion chains, we find that sufficiently long chains concentrate the spectrum into the carrier. This effect enables efficient excitation of light ions at short wavelengths. It may also benefit multi-ion optical clocks and mixed-species quantum-logic spectroscopy.

Adding a sublattice bias to the two dimensional Bose Hubbard model greatly enriches the available physics, and introduces knobs which can be used to control and interrogate the quantum state. We describe the physics of this checkerboard Bose Hubbard model and how it can be explored using transmon arrays. We show that the sublattice bias brings the commensurate superfluid phase into an experimentally accessible regime, and gives new probes. We characterize the superfluid and insulating phases, with careful attention to finite size effects.

The nitrogen-vacancy (NV) centre in diamond is a leading platform for room-temperature quantum sensing. Improvements in sensitivity require precise control of the NV charge state. Transitions from the neutral NV$^0$ charge state to the negative NV$^-$ charge state can occur during excitation with photon energies below the ZPL transition of NV$^0$. These sub-resonant charge transitions limit modern initialisation protocols and have not been studied in full detail. In this paper we show that sub-resonant charge cycling arises from phonon-assisted anti-Stokes excitation. We further uncover the phonon states which contribute most strongly to the anti-Stokes transition via the development of novel quantitative models. The models indicate that low energy acoustic phonons strongly contribute to the transition close to the ZPL. At longer wavelengths a 43\,meV mode additionally impacts the charge cycling dynamics.

Frequency crowding remains a major obstacle to scaling fixed-frequency transmon processors. Among the widely used all-microwave two-qubit gates, the cross-resonance (CR) gate is particularly sensitive to qubit-frequency spread because the conventional straddling regime condition constrains assignable qubit frequencies tightly and makes the system susceptible to frequency collisions. Here, we propose and analyze the CR gate outside the straddling regime, which we refer to as the far-detuned regime, and evaluate frequency collisions using a numerical method that remains accurate under high-intensity, smoothly ramped microwave drives. Based on this analysis, we perform systematic parameter sweeps and provide collision-free conditions that define designable frequency regions in which qubit frequencies can be assigned consistently with surrounding qubit frequencies. Furthermore, we formulate frequency allocation as a linear programming optimization on a unit-cell lattice with periodic boundary conditions to obtain an optimal allocation. We demonstrate that far-detuned designs significantly reduce collisions compared with designs in the straddling regime. Monte Carlo yield analysis indicates that 10% collision-free yield for a 1024-qubit square lattice at a 0.1% two-qubit-gate error threshold requires $\sigma_{\mathrm{f}}/2\pi \le 6.8~\mathrm{MHz}$. Our findings suggest that this is feasible with an approximately twofold reduction in the state-of-the-art qubit-frequency spread.

Reliable pulsar candidate ranking requires probability estimates that are not only discriminative but also well calibrated. We evaluate hybrid quantum-calssical logistic regression on the imbalanced HTRU-2 dataset using three quantum feature encodings: angle encoding, amplitude encoding, and data re-uploading. The models are trained using analytic gradients and compared with classical baselines and a quantum support vector machine reference model under a paired-seed protocol. Evaluation combines rare-event discrimination, low-false-positive-rate recovery, probability calibration, and runtime analysis. Angle encoding gives the strongest performance among the quantum logistic regression variants. At shallow depth, the angle-encoded model remains close to the best classical baselines in discrimination and low-false-positive-rate recovery, while also giving the lowest calibration error at the benchmark configuration. Murphy decomposition shows that the angle-encoded model maintains low reliability error and high, stable resolution across circuit depths and training-set sizes. This means that its probability estimates preserve both calibration and meaningful separation between candidate groups. Data re-uploading is competitive at small depth but loses discrimination and resolution at larger depth in the present multi-qubit implementation, while amplitude encoding remains weaker across dataset sizes. Shallow angle-encoded quantum logistic regression therefore gives the best balance among the tested quantum logistic models, although simulation runtime remains a practical limitation.

Relaxation to equilibrium of a drifted Brownian motion is quantified by a transition probability density function, whose main (multiplicative) entry is an inferred Feynman-Kac kernel of the Schrödinger semigroup operator. Although seemingly devoid of a natural probabilistic significance (except for its explicit path integral definition), the pertinent kernel relaxes to equilibrium as well. The implicit Feynman-Kac potential ${\cal{V}}(x)$, continuous, confining and bounded from below, may take negative values. If positive, ${\cal{V}}(x)$ can be interpreted as the killing rate of the decaying diffusion process. In case of relaxing F-K kernels the killing effects are tamed (often overcompensated). The taming inavoidably appears in conjunction with the existence of the negativity subdomains of ${\cal{V}}(x)$ in $R$. If locally ${\cal{V}}(x) < 0$, its sign inversion $- {\cal{V}}(x)$ can be interpreted as the branching (cloning, alternatively bifurcation) rate in the course of the other wise free random motion. The arising killed diffusion processes with branching, we interpret as the possible path-wise background of tamed (relaxing) Feynman-Kac diffusions. We present acomputer-assisted path-wise arguments, towards a consistency of the killing/branching taming scenario, for a number of nonlinear model systems in one space dimension. Special attention is paid to Feynman-Kac potential shapes in the double well form, where an analytic access to eigenvalues and eigenfunctions is scarce. Throughout the paper the dynamics refers to the positive real time. Since the Newton-type equations of motion for admissible classical trajectories have a Euclidean form (due to the sign inverted force term), we give a brief resume of a couple of their explicit solutions, without recourse to the Euclidean time intuitions, and the instanton lore of related quantum model systems.

Near-fields around nanophotonic structures and waveguides can be used to optically interface particles ranging from atoms and molecules to microscopic biological and synthetic particles. Due to the strong, non-linear dependence of the near-field coupling strength on the particles' position, a change of the spread of the particles' position will change their mean coupling strength. When the particles are trapped, this position spread depends on their temperature, generally leading to temperature-dependent coupling. Here, we experimentally demonstrate that this effect renders optical probing of trapped particles with near fields an inherently transient process. Specifically, we trap cold atoms in a two-color dipole trap surrounding an optical nanofiber and probe them with the evanescent field of guided, resonant light. The scattering of this probe light heats up the atoms, leading to a decrease of the coupling strength as well as loss of atoms. We observe both effects via a concurrent decrease of the absorption signal. In addition, we demonstrate that the coupling strength can be recovered by cooling the atoms back to their initial temperature. Our findings are relevant for numerous situations where stable coupling of trapped particles to a nanophotonic structure is required.

Stochastic unravelings provide a useful way to represent open quantum system dynamics in terms of pure state realizations, and have been widely studied both from a fundamental and from a computational point of view. They were initially formulated for Markovian dynamics described by the Gorini-Kossakowski-Sudarshan-Lindblad master equation. However, due to recent technological and experimental development, most physical relevant dynamics present temporal correlations beyond the Markov approximation. Such correlations cause decay rates to turn temporarily negative, thus requiring the generalization of stochastic unravelings from Markovian to non-Markovian scenarios. Indeed, many unraveling techniques have been introduced in this regime, and a comprehensive review of the different jump methods is currently missing. In this work, we provide an overview of widely used quantum jump unraveling techniques for non-Markovian systems and also discuss them in terms of their numerical efficiency, divisibility requirements, Hilbert space extension, and measurement interpretation.

Variational Monte Carlo calculations have recently reached state-of-the-art accuracy in the approximation of ground state properties of quantum many-body systems. Making use of flexible neural quantum states and automatic differentiation has bypassed traditional computational obstacles such as reliance on basis sets. In this paper, we propose a neural quantum state architecture capable of representing symmetric bosonic wavefunctions in Fock space, enabling the study of systems with variable particle number. By supplementing our variational state with Monte Carlo sampling and geometric optimization, we demonstrate competitive variational energies across an array of one- and two-dimensional systems, converging to the physical boson number under a set chemical potential. Our approach enables accurate estimates of one-body reduced density matrices, opening access to observables such as condensate fractions and radial density profiles from first principles. Our method opens the door to numerical predictions of key measurable quantities in practical grand canonical systems.

Uncovering and understanding universal dynamics in matter far from equilibrium remains a key challenge. In this work, we identify a so far unrecognized form of universal behavior that emerges after a sudden symmetry-breaking quench at continuous phase transitions. Our key observation is that the order-parameter fluctuations in Ising models exhibit a compelling temporal collapse across a wide range of system sizes and quench strengths, indicative of an emergent single-variable scaling form. This phenomenon can be explained by introducing a so far unknown dynamical critical exponent for the underlying continuous phase transition. We find evidence for a lower critical effective dimension of this universal regime: it is observed in the 2D quantum and 3D and 4D classical Ising models, but not in the 1D quantum or 2D classical cases. Our results suggest that our observed universal far-from-equilibrium scaling may extend beyond the Ising models studied here and could more broadly characterize systems with non-conserved order parameters, opening new avenues for exploring universal dynamics both theoretically and in current experimental platforms.

Robust quantization of particle transport, as in a Thouless pump, is a hallmark of topological quantum systems with externally controlled system parameters. Here we instead propose and analyze a Thouless pump, for fermions in a one-dimensional lattice, in which external control is not needed, because an additional dynamical degree of freedom allows the pump to work autonomously. The external control parameters are replaced by a quantum spin in a static magnetic field, so that Larmor precession of the spin performs the control cycle that induces topologically quantized transport of the fermions – at least in some higher energy eigenstates of the combined system. In other states, the back-action of the fermions on the spin can distort the control cycle enough to disrupt the transport, but we find numerical evidence for a critical value of the magnetic field above which the autonomous pump works with topological robustness, suggesting that topological protection and autonomous operation together may permit robust "quantum motors".

Quantum simulation of complex many-body systems beyond classical computational capabilities provides a promising route toward understanding novel quantum phases and their transitions. In particular, analog quantum simulators with global control fields have attracted considerable attention due to their potential to simulate arbitrary Hamiltonians and perform quantum computing tasks. However, a clear, quantitative measure for the complexity of implementing specific quantum operations in such systems is still lacking. In this Letter, we address this challenge by introducing generalized Krylov complexity, a concept originating from operator growth dynamics, as a direct diagnosis for this synthesis complexity. We construct the block Krylov basis generated by a set of Hamiltonians, which naturally organizes the operator space achievable through the simulator's native interactions and their nested commutators. By analyzing representative systems including Rydberg atom arrays, we demonstrate that the generalized Krylov complexity of a target operation serves as a strong predictor of the minimum time required for its realization. Our results establish Krylov complexity as an intuitive and predictive tool for designing efficient control protocols in analog quantum simulators.

Combinatorial optimization problems play a central role in computer science with many real world applications. A number of relevant problems remain computationally difficult to solve as they lie in the NP-hard complexity class. We present a unified framework for solving such optimization problems represented in the quadratic unconstrained binary optimization (QUBO) formalism, namely two-SAT, XOR-SAT, mixed-two-XOR-SAT, set packing, quadratic assignment, binary clustering, and protein folding, by expanding the domain of applications of PRR, 6(2), 023031. A direct mapping from the QUBO form of these problems onto the Rydberg quantum platform is demonstrated as our first step. This mapping to the Rydberg system depends on distance-dependent long-range interactions and configurable local detuning, thus reducing resource overhead and improving scalability. Following-up on the encoding, the solution is reached by steering the system toward the ground state of the target Hamiltonian using an optimized quantum annealing protocol that controls the time-dependent detuning and Rabi frequency profiles. The framework can handle a variety of problems, each with different complexity. To quantify the complexity of any problem, a generalized hardness parameter is introduced that compares different problems based on the structure of their optimization landscapes. This is a proceedings contribution to the Athens Workshop in Theoretical Physics: 10th Anniversary, held at the National and Kapodistrian University of Athens on December 17-19 2025.

We show that the entanglement structure of quantum many-body states defines a natural and optimal distributed representation for their simulation. An arbitrary entanglement cut induces a bipartite decomposition of the wavefunction, mapping its distribution onto that of the entanglement spectrum. In this representation the Hamiltonian application, the core of Krylov-subspace methods, reduces to local contractions and communication-optimal operations. Using benchmarks from different methods and models, we demonstrate near-linear scaling for sufficiently large systems and identify entanglement spectrum fragmentation as a key factor controlling computational cost. This establishes entanglement as an organizing principle and unified, method-independent, route for scaling up quantum many-body simulations.

Mixing in open quantum systems is often summarized by a single worst-case time, even though that benchmark can be set by exponentially rare initial states. We show that for broad unstructured ensembles the nonlinear trace-distance relaxation curve itself concentrates around a deterministic mean. For Haar-random pure states this yields fixed-time concentration of the instantaneous trace distance to the steady state, which we term vertical concentration since typical relaxation curves bundle along the distance axis. Whenever the mean curve crosses the distance threshold with a finite slope, it converts this vertical concentration into a horizontal concentration of the mixing time, extending typicality from standard physical observables to a fundamentally non-observable dynamical quantity. This sharp concentration naturally raises the question of how the typical mixing timescale compares to the worst-case benchmark. We show that in a one-mode tail regime, this separation is controlled by the logarithmic ratio of extremal to typical initial-state overlaps for the slow left eigenoperator. This rare-state bottleneck law yields a hierarchy that is logarithmic in skin-effect settings, linear for boundary-supported many-body slow modes, and exponential in a protected-sector family where generic states mix rapidly while rare states stagnate. The framework also extends beyond Haar to exact and approximate unitary 2-designs and Hilbert-Schmidt/induced ensembles.

Quantum machine learning holds the promise of combining the success of classical machine learning methods with the power of quantum computing, however one of the largest obstacles facing the field is the problem of barren plateaus. Parameterised quantum circuits offer a flexible framework for developing quantum machine learning models, but their practicality is constrained by a trade-off between trainability and classical simulability. In general, circuits that are sufficiently expressive to model complex behaviour often exhibit barren plateaus, where gradients vanish and optimisation fails. In this work we investigate a compositional approach to mitigate this trade-off by assembling larger quantum models from smaller subcomponents. To ensure trainability of these subcomponents, we describe a framework for constructing group-invariant loss functions, which introduce symmetry-induced inductive bias and lead to improved gradient behaviour and generalisation. In particular, we use this framework to design permutation-equivariant quantum graph neural networks for identifying maximal cliques in graphs. The models we construct exhibit superior training gradients through symmetry-induced bias, and our experiments demonstrate that the trained models generalise to larger, more complex problem instances. Finally, inspired by Quantum-Informed Recursive Optimisation Algorithms (arXiv:2308.13607), we implement a recursive hybrid quantum-classical heuristic using the learned quantum models to guide a classical search procedure, demonstrating improved inference accuracy and scalability. Together, these results suggest that compositional circuits could be a viable pathway towards scalable quantum learning models that remain challenging to reproduce classically.

We present a Python package for ground-state preparation based on the probabilistic imaginary-time evolution algorithm, with particular focus on its state-vector-based implementation. A standard shot-based simulation is also supported, and results can be benchmarked against exact diagonalisation via a dedicated wrapper. The package enables efficient tuning of initial parameters, facilitating systematic exploration and optimisation of the method's performance. Starting from the prepared ground state, the strong interoperability with other packages further enables real-time evolution and the computation of spectral functions, such as the spin-spin dynamical structure factor.

The quantum circuit model essentially treats every quantum algorithm as a straight-line program. While this view is universal, recent work has shown that it is inconvenient for using different-length quantum subroutines in superposition. Using the quantum walk formalism of quantum algorithms, it is possible to model such branching behaviour, and get better composition in this setting. We apply the above branching composition to Grover's algorithm, which gives a variable-time quantum search algorithm that is worse than previous work. The reason it is worse is because branching composition does not take into account another deviation from straight-line programs: looping. We show that by modifying branching composition to also include looping, we can get a complexity that matches previous work. This highlights the importance of properly modeling the program control flow when designing quantum algorithms.

To overcome the limitations of classical partially connected Boltzmann machines and mainstream quantum Boltzmann machines (QBMs), this work extends the conventional circuit of the quantum approximate optimization algorithm (QAOA) to a bilevel optimization architecture and proposes a fully connected QBM. The inner-loop training simulates positive phase energy minimization based on the computational process of the conventional QAOA circuit, whereas the outer-loop training simulates negative phase contrastive divergence learning by optimizing the structural parameters of the target Hamiltonian. It is found that, first, the model exhibits superior performance using only a single layer (p=1) in the QAOA circuit, with an average probability of 0.9559 in measuring the target quantum state under noiseless conditions. Second, the model exhibits notable noise robustness. Under the typical noise level of current mainstream commercial quantum computing devices, the average probability of measuring the target quantum state reaches 0.6047; when the noise rises to a more stringent level with doubled intensity, this probability remains at 0.3859. In both scenarios, the target quantum state maintains the highest measurement probability among all detected states, with a value several times higher than that of the second-ranked state. This indicates that the model retains strong robustness even when noise meets or exceeds the upper limit of current mainstream commercial quantum computing devices. Third, under a block-by-block learning strategy with p=1 and only 10 measurement shots, the model consistently generates the target "qubit" grid image regardless of noise interference, demonstrating strong robustness in image generation.

We propose and analyze a hybrid qubit-qutrit quantum battery (QB) based on a mixed spin-1/2 and spin-1 system interacting via an anisotropic Heisenberg exchange coupling in the presence of a homogeneous magnetic field. The nonclassical properties of the system are characterized using the l1-norm of coherence and negativity, which quantify quantum coherence and entanglement, respectively. The performance of the quantum battery is evaluated through key indicators such as ergotropy, power, and capacity. Our results reveal that both ergotropy and power exhibit oscillatory dynamics, while the capacity remains constant over time. We further investigate the influence of system parameters and magnetic field strength on both quantum correlations and battery performance, demonstrating that nonclassicality plays a crucial role in enhancing energy-storage efficiency. Importantly, we establish a connection between the theoretical model and an experimentally realizable nickel-radical molecular complex, showing that quantum coherence, entanglement, and efficient energy storage persist even at room temperature. These findings provide a realistic pathway toward the implementation of hybrid qubit-qutrit quantum batteries in solid-state molecular platforms.

Single-Hessian Gaussian wavepacket dynamics (GWD) significantly reduces the computational burden of Heller's local harmonic GWD, while maintaining comparable accuracy in approximating vibronic spectra. Here, we provide a new, symplectic derivation of the equations of motion of single-Hessian GWD and show that, unlike the local harmonic version, this method conserves the non-canonical symplectic structure on the manifold of Gaussian wavepackets and$-$for bounded dynamics in smooth potentials$-$avoids the drift of energy. Our numerical results suggest that, despite being much more efficient than the local harmonic variant, the single-Hessian GWD exhibits the same $\mathcal{O}(\hbar)$ asymptotic error in averages of observables. To further accelerate numerical simulations, we implement high-order time-stepping geometric integrators that are time-reversible and conserve the norm and symplectic structure exactly, regardless of the time step. In addition, we present explicit expressions for the exact evolution of the width of a single-Hessian Gaussian wavepacket in a general potential, as well as for the exact evolution of the whole wavepacket in a global harmonic potential. Using on-the-fly ab initio Gaussian wavepacket dynamics on the first excited-state surface of ammonia, we numerically confirm the conservation of geometric properties by these integrators and demonstrate that high-order integrators can enhance both accuracy and computational efficiency. We also compute the photoelectron spectrum of the difluorocarbene anion and the absorption spectrum of methylamine, and find that, in comparison with experiment, single-Hessian GWD outperforms global harmonic models and matches the accuracy of local harmonic GWD. Finally, we identify which spectral features are sensitive to the choice of reference Hessian.

Noncommutativity of states and observables is a fundamental signature of quantum theory, and a minimal requirement for nonclassicality. We provide a universal necessary and sufficient condition for pairwise commutativity of quantum states $\rho_1$ and $\rho_2$: they commute if and only if $\mathrm{tr}(\rho_1^2\rho_2^2) = \mathrm{tr}(\rho_1 \rho_2 \rho_1 \rho_2)$. For qubits the identity simplifies to an equality between polynomials of purities and of the two-state overlap $\mathrm{tr}(\rho_1\rho_2)$. These multivariate traces (known as Bargmann invariants) are directly measurable, allowing commutativity tests that bypass full state tomography. We point out possible applications to the analysis of POVM simulability and partial photonic distinguishability.

We analyze the complexity of synthesizing random states and unitary operators in a multi-qudit system in two paradigms. In one case, we consider the situation in which we manipulate the system by applying a sequence of one- and two-qudit quantum gates that constitute the elementary, and universal, gate set. The minimum number of gates required to perform the desired operation represents the computational complexity. In the other case, we consider the situation in which we manipulate the physical system using physical fields with optimized control pulses. The minimum time required to perform the desired operation represents the physical complexity. In both cases, we use analytical arguments in combination with optimal-control-theory numerical calculations to determine the complexity of random operations. We show that the computational complexity of random states or unitary operators scales exponentially with the number of qudits. Our numerical results suggest that the physical complexity of preparing random quantum states and unitary operators scales more slowly than the computational complexity. We discuss various implications of our results, especially concerning the relationship between random and pseudorandom states and unitary operators.

We propose a method to break the dark mode of two degenerate mechanical resonators (MRs) in optomechanical systems via the Coulomb interaction. Two degenerate MRs can be cooled to their ground-state simultaneously beyond the resolved sideband regime using the Coulomb interaction and an optical parametric amplifier (OPA). We show that strong and robust mechanical squeezing beyond 3 dB can be generated using the OPA and mechanical parametric amplification (MPA) introduced by the Coulomb interaction. Our results manifests that robust bipartite and genuine tripartite entanglement can be produced in a degenerate optomechanical system.

Rare-earth-ion-doped crystals has emerged as a promising platform for quantum technologies, owing to their narrow telecom-band optical emission, long spin memory, and compatibility with silicon integrated photonic architectures. However, the realization of scalable quantum devices requires host materials with intrinsically dilute spin environments to suppress decoherence. In this context, erbium (Er$^{3+}$) doped cerium oxide (CeO$_2$) is an attractive candidate due to the ultra-low concentration of nuclear spins in the host matrix and its compatibility with silicon-based technologies. Here we perform a comprehensive investigation of the coherence properties of Er$^{3+}$ electron spin qubit in CeO$_2$ via semiclassical as well as detailed cluster correlation expansion simulations. By systematically exploring magnetic field strength, pulse sequences, erbium concentration, and spin temperature, we identify regimes where decoherence from the spin bath is strongly suppressed. Our investigations illustrate that at dilute doping concentration (of the order of 10 ppb) and sub-Kelvin temperatures, operation near clock transitions enables Hahn-echo coherence times to approach the second timescale even at natural isotopic abundance. Importantly, from a practical standpoint, coherence times on the order of $\sim 10$ ms are expected even at liquid helium temperature (about 2 K) for similar concentrations. Moreover, we demonstrate that an additional enhancement can be obtained with conventional multi-$\pi$-pulse dynamical decoupling protocols. Thus, our findings establish Er$^{3+}$ doped CeO$_2$ as a front-runner for realizing spin qubits, quantum memories, and integrated quantum networks.

With the advent of the construction and deployment of entanglement-based quantum networks, the efficient use of network resources will become a critical challenge for the scalable operation of such a system. Recently, architectures that incorporate memoryless optical switches have gained attention for forwarding entangled photons. By leveraging these architectures, costly resources such as high efficiency Bell State Analyzers (BSAs) can be shared across the network. Nevertheless, the introduction of switching substantially complicates the process of multiplexing and resource allocation compared to an individual link. In this work, we propose a switching protocol for unbuffered, multidrop quantum networks in a photonic synchronization domain that establishes a link between two end nodes using a shared BSA in the switched network. To achieve this, two end nodes cooperatively select the target BSA node with the lowest path cost and independently reserve each path within the network. Bi-path reservations are performed to allocate resources in a distributed manner. The proposed protocol is evaluated through simulation on Q-Fly network topologies under varying traffic conditions. The results demonstrate high link establishment success with stable performance even under increased network load. These capabilities which are driven by our proposed protocol are an essential way to realize large-scale, managed, and automated quantum networks.

Integrated quantum photonics requires compact, efficient, and low-power phase modulators. While silicon nitride (SiN) is a promising platform, existing modulators suffer from high power consumption, thermal crosstalk, or high driving voltages. Liquid crystal (LC) offers a compelling alternative because of the large index changes and industrial maturity. However, their suitability for supporting various applications in the photonic quantum system has not been experimentally confirmed.Here, we report the first experimental demonstration that LC-based phase modulators integrated on a SiN platform show highly visible quantum interference. We fabricated a liquid-crystal integrated Mach-Zehnder interferometer (LC-MZI) that achieved CMOS-compatible performance with V_pi * L < 1 V-mm. In two-photon interference experiments, the devices exhibited high-visibility quantum interference (~98.5%) with voltage-tunable phase control. Furthermore, we validated the scalability of our approach by demonstrating wafer-scale fabrication using stepper lithography. This work establishes LC-integrated SiN photonics as a scalable, reconfigurable, and energy-efficient platform for quantum photonic circuits.

We present topological characterization of classical stochastic processes described by discrete-time Markov chains on lattices. We point out that point-gap topology of stochastic matrices entails two distinct physical consequences that hinge on the choice of the reference point. The point-gap topology around a generic reference point is related to the direction of transport, and nontrivial topology around the origin of the complex spectrum of a stochastic matrix implies non-Markovianity caused by, e.g., feedback control. On the basis of this characterization, we identify the topological origin of directed transport in a classic experiment of Maxwell's demon [S. Toyabe et al., Nat. Phys. 6, 988 (2010)] and find the topological nature of feedback control beyond thermodynamic interpretation. We demonstrate that a topologically enforced non-Markovian classical stochastic process can be simulated by a Markovian quantum master equation, indicating a topological form of quantum advantage.

Measurement-Device-Independent Quantum Key Distribution (MDI-QKD) provides unconditional security against detector vulnerabilities, but its practical deployment is severely hindered by asymmetric channel turbulence. Fluctuations in optical fibers induce arbitrary polarization drift, degrading Hong-Ou-Mandel interference and forcing extensive calibration downtime. In this work, we propose a hardware-free polarization stabilization technique utilizing a Correlated Twirling protocol based on a unitary 2-design. By applying a synchronized, public twirling supermap, Alice and Bob mathematically transform deterministic, asymmetric geometric rotations into an isotropic Pauli depolarizing channel. Executed entirely as a virtual post-processing step during classical sifting, this protocol mathematically suppresses intrinsic channel noise by a factor of 2/3. We demonstrate through exact quantum state simulations that this induced symmetry neutralizes catastrophic axis-dependent failures, extending the Y-bias tolerance from 0.68 to 0.84 radians. Furthermore, the protocol passively extends the absolute angular misalignment tolerance for the 11% security threshold from $38.7^\circ$ to $47.9^\circ$, sustaining secure key distillation over extended fiber distances in highly turbulent regimes where standard architectures fail. Inherently compatible with decoy-state weak coherent pulses, this algorithmic approach provides a highly scalable, resource-efficient framework for robust long-distance quantum networks.

We consider an ontology, in which contextual nonlocal hidden variables are stored as pre-existing possibilities in a repository outside space-time; and in which the context can be chosen “freely” (measurement independence) by each agent, both in spacelike and timelike configurations. We show that, in Bell-type experiments involving several agents, for this ontology to be consistent, the context must include not only the measurements that can be performed, but also the time ordering of the choices of different agents.