Papers

Magnetically trapped type-I superconductors are promising candidates for precision experiments at the quantum-to-classical borderline, with applications in quantum sensing of forces and accelerations and fundamental tests of quantum physics. Here, we show how the rotational motion of micron-sized superconductors is strongly affected by (i) the diamagnetic torques due to the gradient of the trapping field and by (ii) the gyromagnetic coupling due to Einstein-de Haas and Barnett effects. We show that this allows the three-dimensional alignment of asperhical superconductors in the trap center, as required for future sensing applications and quantum experiments, and determine the resulting librational trapping frequencies. Finally, we propose an experiment to probe gyromagnetic coupling in levitated superconductors and we discuss how it can be used to control the particle rotation.

Fractionalizing a quantum two-level system is usually associated with encodings based on pairs of Majorana fermions—an operational fractionalization. We show an alternative algebraic fractionalization by embedding Székely's classical “half-coin” into a non-Hermitian Krein space. The coefficients of $(q+pz)^{1/2}$ define a signed sequence and a normalized, non-Hermitian biorthogonal operator describing a biorthogonal half-qubit. We prove that two such objects fuse into an arbitrary pure qubit through the signed Vandermonde convolution that the collective $N\ge2$ vectors are null in Krein space. $L_1$ norm of the half-qubit follows in closed form, $\lVert p\rVert_1 = 2\sqrt{q}-\sqrt{q-p}$. Its $L_1$ norm increases monotonically with the bias and attains its supremum $\sqrt{2}$ precisely at the unbiased point $p=q=1/2$. Interestingly, we identify two structural results as follows. First, number parity and the $\eta$-metric generate a distinguished commuting $\mathbb Z_2\times\mathbb Z_2$ subgroup. Second, we find the $\eta$-metric obstructs any local $\eta$-self-adjoint partner of the parity, so a half-qubit carries a $\mathbb{Z}_2$ observable but no local $SU(2)$. The full Pauli algebra emerges only upon fusion. We then show that the construction survives truncation of the Fock basis: the fused qubit is exact at every cutoff, and the Vandermonde cancellation is visible in sign-weighted photon-number statistics, and can be tested using existing cavity and trapped-ion state-synthesis methods. Finally, we generalize this algebraic fractionalization to a $1/n$-qubit, which can be achieved by replacing the square root with an $n$th root.

We theoretically investigate quantum Rabi oscillations in a system consisting of a two-level atom (qubit) strongly coupled to a one-dimensional open waveguide. In contrast to conventional cavity quantum electrodynamics, the qubit interacts with a continuum of propagating modes, which gives rise to fundamentally different dynamical behavior. Within the rotating-wave approximation, we express the multimode Jaynes-Cummings Hamiltonian in terms of collective bosonic operators and show that the system possesses two integrals of motion, enabling an exact diagonalization of the Hamiltonian in the single-excitation subspace. We show that for a strongly interacting qubit-photon system, the dynamics are captured by a reduced two-level model. In this framework, each level is defined as the product of the atomic excited state and a specific field mode, which takes the form of a multiphoton Fock-like state. In this picture, the Rabi oscillations represent a collective phenomenon corresponding to oscillations between multiphoton states differing by a single photon. We then extend our analysis to multiphoton processes in which the initial field is a coherent state with a continuous spectrum. In this case, the Rabi frequency is shown to be sensitive to the spectral profile of the function that generates the coherent state.

Semiconductor quantum-device design still lacks an integrated Technology Computer-Aided Design (TCAD)-like environment that connects material geometry, quantum many-body simulation, and automated design. Here we introduce zenDot, a large-language model (LLM)-integrated quantum TCAD platform that links a material-labelled device state to a unified condensed-matter physics toolbox. The device and calculation components are integrated into a desktop workbench, Python API, and an embedded LLM agent, allowing electrostatics, charge and transport characterization, correlated-state calculations, and qubit modelling to be executed within one reproducible environment. We demonstrate zenDot on a Si/SiO2 double quantum dot, where a single device state reproduces the characterization workflow and supports hybrid, tunnel-charge, and singlet-triplet qubit analyses. A platform-level universal-control scan revises the singlet-triplet operating point and reduces the predicted worst-gate infidelity by nearly 30-fold. Beyond analysis, the LLM agent directly operates the same physics environment as human users, proposing design changes, executing registered simulations, and iterating on solver-returned metrics under physics-aware validation. Across three demonstration tasks it completes 18 validated design iterations, including geometry modification followed by a full re-solve from the material stack. zenDot establishes a machine-operable quantum TCAD workflow that connects device physics with LLM-driven design exploration.

Entanglement among particles is a defining feature of strongly correlated quantum materials, distinguishing them from conventional metals and semiconductors. The ability to certify intrinsic entanglement among interacting electrons in solid-state materials is important not only for classifying quantum states of matter, but also for developing material-based quantum technologies. Here, we introduce a transport-based protocol for witnessing multipartite entangled electronic states, based on the equilibrium noise spectrum as an experimentally accessible observable. The appropriately integrated, symmetrized, and projected current noise obeys an upper bound that can be derived from microscopic model parameters and is invariant with respect to the choice of electronic basis. We benchmark this framework in several paradigmatic systems, including twisted bilayer graphene, twisted bilayer MoTe$_2$, and Hubbard models, certifying entanglement in the fractional Chern insulating state. The method extends recently developed scattering-based entanglement witnesses to ultralow-temperature materials, where conventional spectroscopic probes are inaccessible but candidate entangled states are expected to arise.

In quantum optics, quantization of the electromagnetic field typically occurs in a finite volume - a cavity - which results in discrete frequency modes where photons are created, annihilated and exchanged between such modes. As a result, evolution of quantum optical states is periodic in the carrier wave of the field, measurement protocols return cycle-averaged information, and any sub-cycle evolution that is foundational to many light-matter interactions, especially at high field strengths, remains hidden. Adapting an attosecond technique, here we capture the electric-field evolution of a quantum optical state, femtosecond bright squeezed vacuum, with sub-cycle precision. We find that it consists of many stochastic, time-localized bursts within each pump pulse whose phase randomly switches between two values. We exploit the random phase flips to generate quantum random bit sequences with a generation rate that can reach petahertz frequencies. In addition, the sub-cycle resolution allows us to measure coherence functions of the waveforms between any two times, which we explain with a superposition of time-limited modes. These results bridge attosecond metrology and quantum optics and pave the way to measuring quantum light-matter interactions as they evolve on a few-femtosecond time scale, and integrate quantum randomness in petahertz electronics.

Electronic position controls how a crystal polarizes and responds to an external field. In crystals, it is usually described through local changes of electronic states in momentum space. This Berry framework has reshaped modern solid-state physics, but strong fields drive electrons across a finite momentum range, where coherence between different momenta becomes part of the response. Here we establish a first-principles theory of electronic position at finite momentum that retains this missing information. We show that unequal-momentum coherence can cancel under spatial averaging and still produce polarization, forming a coherence dipole. We obtain the matrix directly from material wave functions, without model bands or fitted transition elements. In Si, the finite-momentum geometry sets a material momentum scale. Comparing this scale with the momentum change driven by the field predicts when finite-momentum physics becomes active. Crossing the scale strongly reorganizes the fifth and higher harmonics, showing that momentum-space geometry, rather than emitted photon energy alone, controls the nonlinear response. HHG is the first demonstration, but the theory applies whenever driven electrons explore a finite momentum range. It therefore extends quantum geometry beyond the local Berry limit and provides a general basis for predicting field-driven phenomena in real materials.

The silicon T-center is a telecom-band spin-photon interface in a manufacturable photonics platform. In nanophotonic devices, however, its optical linewidth is broadened by a fluctuating charge environment, limiting photon indistinguishability for quantum networking. Here, we address this challenge through characterization and suppression of the local charge-noise of single T-centers in lateral p-i-n waveguides, demonstrating an unheralded method of T-center optical linewidth narrowing. Above-band illumination resets the charge environment, neutralizing the local field and suppressing spectral diffusion. Across 46 emitters this reset narrows the median linewidth 3.5-fold to 0.57(11) GHz, and an optimized emitter reaches 128(22) MHz, the narrowest unheralded linewidth reported for an integrated T-center. The narrowed transition supports coherent optical Rabi oscillations with a coherence time of 20(2) ns. Finally, we perform Stark-shift tuning using the p-i-n junction, and read out the local electric field and the charge-noise width at 15 mK. An analytical model of proximal surfaces, bulk, and junction field effects provides good agreement with our findings. Our multi-pronged characterization of the nanophotonic-integrated T-center charge environment enables future device optimization toward scalable quantum interconnects.

We study nonconvex optimization of measured quantum $f$-divergences over rank-one projective measurements. For smoothly operator-Fenchel liftable generators and faithful states, every projective local maximum and every second-order stationary point is globally optimal over all POVMs. The criterion is blockwise: a critical PVM is optimal exactly when the compressed states are proportional on each equal-score block; otherwise an explicit two-vector rotation has positive ascent curvature. Operator-convex generators admit a positive atomic curvature resolution, and the quadratic $\chi^2$ case yields two-sided residual bounds. For binary accessible information, this framework proves the known adaptive-capacity equality and shows that every nonidentical qubit ensemble has exactly two stationary projective measurements, proving conjectures of Keil and Thai–Dall'Arno. A rare-prior limit connects weighted Jensen–Shannon information to relative entropy and yields a finite counterexample to the proposed equivalence between observational-entropy and all-ensemble mutual-information orders. The landscape theorem also covers measured Rényi divergences of finite positive order and measured relative entropy.

In this paper, we propose a novel quantum solution to address the problem of reachability in bounded quantum Petri nets (QPNs), an advanced modeling framework that combines the classical Petri net model with quantum mechanical principles. The proposed approach exploits quantum parallelism to construct a superposition over all reachable markings from the initial marking. Grover's amplitude amplification algorithm is then applied to efficiently identify a desired target marking, while ancillary q-tokens used solely for transition control are excluded from the search space, significantly reducing its size. Theoretical analysis shows that our approach achieves a quadratic speed up over classical exhaustive algorithms. Experimental results also confirm the correctness and feasibility of the proposed quantum algorithm for solving the bounded reachability problem in Quantum Petri Nets.

System-level resource estimation is a key component of fault-tolerant quantum computing (FTQC) toolchains. Its efficiency depends on how global error tolerance is allocated across logical operations, T-state distillation, and rotation synthesis to minimize physical resource overhead. The commonly used uniform-allocation strategy ignores circuit-specific structure and can overprovision inactive or less critical subsystems, leading to inflated space-time estimates. Prior work aims to address this limitation using supervised models trained on offline-generated datasets. However, this approach incurs additional data-generation costs and limits deployment flexibility. To overcome these drawbacks, we propose a training-free optimization framework that performs derivative-free search directly on the Azure Quantum Resource Estimator (AQRE), enabling instance-specific error budget allocation for previously unseen circuits without requiring offline training data. To evaluate robustness to optimizer choice, we instantiate the framework with two structurally distinct metaheuristics, simulated annealing and quantum particle swarm optimization. We evaluate our framework across 433 circuits spanning 2 to 91 qubits from 31 families in the MQT Bench suite. Across the benchmark suite, both methods reduce space-time cost by more than 33% on average and agree within 1.34% points, indicating that the gains are stable across different metaheuristic search strategies. Our analysis further finds that the optimization benefit is driven primarily by error-profile asymmetry rather than circuit scale, and the metric, Gini coefficient of optimized allocation, provides an interpretable diagnostic of expected improvement. Together, these results position adaptive error budget allocation as a system-software optimization layer for FTQC resource estimation pipeline.

Monitored quantum circuits exhibit a measurement-induced phase transition between volume-law and area-law entanglement as a function of the measurement rate $p$. Prior work places measurements at random locations and treats the rate as the control parameter. We instead fix the measurement budget and vary the placement process, comparing random placement against hand-designed and learned policies in brickwork random Clifford circuits at matched budget. First, placement geometry matters more than placement information. A deterministic contiguous sweep cuts the half-cut entropy by a factor of 3.4 relative to random placement, while equal-coverage unstructured placement and a greedy policy with full state access do far worse. The effect is carried by spatial order alone: measuring the $k$ least recently measured sites gives $4.14 \pm 0.06$ bits with random tie-breaking and $1.29 \pm 0.04$ bits with position-ordered tie-breaking. Second, the sweep eliminates the transition rather than shifting it. Tripartite mutual information crossings recede as $p^* \propto 1/L$, the steady-state entropy saturates at an $L$-independent ceiling near $0.46/p$, and data for $64 \le L \le 512$ collapse onto the form $S = p^{-1} f(pL)$ predicted by a ballistic regrowth argument. Third, in stabilizer dynamics every outcome is deterministic or a fair coin flip, so the record's Shannon entropy is exactly countable; the sweep dominates the entropy-versus-record-cost frontier while paying the same roughly one bit per measurement as random placement. Policies trained by cross-entropy and proximal policy optimization do not find the sweep: score-based policies parameterize which sites to measure, not the order in which degenerate scores are resolved, and the effect lives in that order. The phase diagram of monitored dynamics is a property of the placement process, not only of the measurement rate.

Demonstrating quantum advantage in a manner that can be independently verified by classical means remains one of the most pressing open problems in quantum computing. Here we propose a new heuristic approach for constructing quantum circuits that are hard to classically simulate yet whose output distributions can be efficiently verified classically. Our approach is based on Clifford circuit obfuscation. This obfuscation scheme hides the Clifford structure and injects a controlled and rapidly growing non-stabilizer resources that resist known classical attack strategies including reverse engineering and direct simulation. Our protocol is heuristic but is supported by both numerical and theoretical evidence. This work provides a new avenue toward classically verifiable quantum advantage that avoids the stringent implementation requirements of known approaches.

Quantum Annealers (QAs) are among the first commercially scaled quantum computing systems designed for large-scale optimization. Unlike digital systems that execute sequences of compiled instructions, QAs operate as analog single-instruction machines that directly evolve an Ising Hamiltonian toward low-energy solutions. To execute an application, the logical problem graph must first be mapped onto the hardware's sparse connectivity via embedding, where logical variables are represented by chains of connected physical qubits. As a result, embedding becomes the dominant system challenge in QAs, shaping whether and how workloads can execute on the machine. Despite its central role, embedding has largely been treated as a preprocessing step rather than a system bottleneck. In this work, we present ChainForge, the first systems and architecture characterization of embedding in QA workloads. Using diverse workload families and graph topologies on modern QA hardware, we characterize how embedding impacts effective hardware capacity, routing overheads, runtime variability, and solution quality. Our results show that embedding inflates physical resource usage, long chains degrade annealing fidelity and scalability, and heuristic embedders may fail even when valid embeddings exist. We further show that embedding latency can become a runtime bottleneck for dynamic workloads requiring frequent remapping, while nominal qubit counts significantly overestimate the usable capacity of QAs for realistic applications. Overall, our findings establish embedding as the defining workload bottleneck and systems abstraction of QAs, providing architectural insights for future hardware topologies, runtime systems, and workload-aware annealing platforms.

Functional electro-optic (EO) tunable photonic integrated circuits are crucial to next-generation information processing and advanced computing, where ferroelectric materials such as lithium niobate (LiNbO3) provide outstanding optical properties and versatile tuning mechanisms. However, beyond achieving high-performance devices, practical deployment of LiNbO3 photonic integrated circuits requires rapid and cost-effective scale-up, which remains challenging because of the fabrication complexity and high costs of ion-sliced thin-film lithium niobate and wafer bonding processes. To address this gap, we demonstrate a fully monolithic photonic platform, amorphous silicon carbide (a-SiC) on bulk LiNbO3 crystal substrate, enabling scalable, CMOS-compatible, low-cost, and high-density photonic integrated circuits without relying on thin-film LiNbO3. This integration approach breaks the long-standing cost-performance-scalability tradeoff, and intrinsically eliminates the need for sophisticated fabrication processes, including ion slicing, wafer bonding, and LiNbO3 etching. It realizes high-density, low-loss, and record high EO tuning efficiency of VpiL = 2.87 Vcm on bulk LiNbO3 crystal substrate. Furthermore, leveraging slow-light effect near photonic crystal bandgap, an 8.5-fold EO tuning efficiency enhancement is achieved, highlighting the platform's capability in confinement control and dispersion engineering.

We study the differential, statistical, and geometrical consequences generated by the geodesic quantum $f$-divergences introduced in [14], which are constructed by interpolating the relative modular operator and the commutant Radon-Nikodym derivative using a geodesic with parameter $t\in [0,1]$. For an invertible state $\rho$ and an operator convex function $f$, we compute the Hessian and obtain an explicit formula for the induced monotone quantum information metric $g_{\rho,t}^{(f)}$. Furthermore, we also compare these metrics with the Petz-Hasegawa metric and find the meaning of the interpolation parameter $t$ in this new geometry. We next show that the interpolation of relative modular operators $\Gamma_t$ in the reference purification of a state $\sigma$ defines a canonical finite binary experiment $(p_t,q_t)$, and introduce a log-likelihood cumulant function $\Psi_{\rho,\sigma}(t,s)$, recovering the Nussbaum-Szkoła distributions at $t=0$ and the Matsumoto construction at $t=1$. Finally, using Busemann functions, we endow this statistical framework with a geometric meaning in the cone of positive operators.

Anti-Zeno enhancement of quantum heat machines has been established for working fluids driven by an external classical field, but whether the same quantum advantage survives when a quantized piston replaces the classical drive has remained open. Here we study an autonomous thermal machine in which a two-level working fluid is coupled to a quantized harmonic piston and to two spectrally separated reservoirs, without external modulation. We derive a finite-time master equation for the resulting carrier and sideband channels, whose rates interpolate between the Zeno, anti-Zeno, and Markovian regimes as the coupling time is varied. For an initially coherent piston, the machine charges the piston and generates more ergotropy than the Markovian reference in the anti-Zeno window. Reversing the same retained channels turns the device into a finite-resource refrigerator with enhanced cooling. The anti-Zeno effect therefore increases the rate of operation while leaving the underlying carrier and sideband energy ratios unchanged. These channel ratios remain consistent with the usual Carnot bounds over the operating regimes considered here. Our results establish finite-time reservoir sampling as a mechanism for enhancing autonomous thermal-machine operation with a quantized piston, without external modulation.

Currency arbitrage (CA) involves trading currencies in cycles to exploit discrepancies in market valuations. Quadratic unconstrained binary optimization (QUBO) involves minimizing a quadratic cost (energy) function of binary variables. Previous works have explored the use of QUBO to solve CA problems. We build on these previous works by introducing realistic constraints such as beginning cycles from a held currency and accounting for per-transaction trading fees. We show that this formulation requires fewer logical variables (qubits) than previous QUBO encodings in the literature. We derive provably sufficient penalty weights for its constraint terms. We also introduce an exact anchor-gauge reweighting of the exchange rates that compresses the QUBO coefficient range from the rate scale to the arbitrage scale, addressing the finite analog precision of annealing hardware. We demonstrate the efficacy of this formulation using classical simulated annealing against an exact Held-Karp baseline on the same CPU and show that it can effectively find profitable cycles and account for trading fees. Finally, we benchmark faithful implementations of five prior QUBO encodings at matched sampler budgets and show that the proposed encoding is the only one to recover the exact fee-adjusted optimum.

The noise threshold below which the qubit depolarizing channel retains positive quantum capacity has been studied since 1996. The classic constructions come with exact finite formulae, but in every reported threshold to date – most recently the record of Agarwal et al. – the final step, the sign of the coherent information at the reported point, is a floating-point evaluation. We give the first exact-arithmetic, independently machine-checkable positivity proofs in this regime: an explicit 45-copy rank-two input state $A$, published as a 1,472-byte witness, with certified $I_c > 0$ at the exact rational $p = 16239/250000 = 0.064956$ (per-Pauli convention; total error rate $3p = 0.194868$) – beyond both the best value printed in the Agarwal et al. paper ($0.064657$) and the strongest state in the same authors' public repository ($\approx 0.064911$), both numerical. Because the depolarizing family is a semigroup under composition, fixed-input coherent information is nonincreasing in $p$ on $[0,1/4]$, so a single certified point extends to the entire interval below it. The same machinery confines the positivity boundaries of $A$ and of the strongest public state of Agarwal et al. to disjoint rational intervals separated by more than $1/25000$ – to our knowledge the first proven ordering of the positivity boundaries of two explicit competing code states. Every computational claim in the paper reduces to a finite list of big-integer comparisons, checkable by a dependency-free few-hundred-line verifier whose soundness rests on three self-contained half-page lemmas. Payloads, certificates, and verifier accompany the paper as a supplementary artifact.

We show that in strong-field ionization driven by bright squeezed vacuum, the mean longitudinal photoelectron momentum scales with the mean incident intensity $I$ as $I^{2/3}$, rather than linearly as under coherent-light driving. This anomalous scaling originates from a field-amplitude saddle-point structure: nonlinear tunneling selects two dominant field amplitudes of equal magnitude and opposite sign from the broad quantum fluctuations. Tracing over the final photon state erases their relative-phase information, leaving their common magnitude to determine the field-dependent longitudinal momentum shift and its $I^{2/3}$ scaling. Photon-number resolution instead preserves this coherence, producing parity-dependent modulations of the longitudinal momentum transfer through time-domain double-slit interference between two field pathways separated by half a cycle. These results establish longitudinal momentum transfer as a distinct observable in strong-field quantum optics that encodes both the photon statistics and the field coherence of intense quantum light.

We introduce the geodesic quantum $f$-divergences $D_f^t$, $0\leq t\leq1$, obtained from the affine-invariant geodesic between the standard and maximal relative modular operators. They reduce to the classical $f$-divergence for commuting states. The logarithmic generator yields geodesic relative entropies joining the Umegaki and Belavkin-Staszewski entropies, while the power generators yield $(t,\alpha)$-Rényi divergences joining the Petz and geometric families. Our first main result is data processing of $D_f^t$ for every finite operator-convex generator $f$ and every $t\in[0,1]$. In particular, this gives DPI for the geodesic relative entropies and for the $(t,\alpha)$-Rényi divergences when $0<\alpha<1$ or $1<\alpha\leq2$. Our second main result identifies equality in the DPI: for invertible states, every equality-determining operator-convex generator has, at each nonmaximal parameter $0\leq t<1$, exactly the Petz sufficiency class; at $t=1$, this changes to the generally larger maximal, or BS, class, which also coincides with the equality class of the divergences associated with the quadratic generator for every $t$. Our third main result is the corresponding collapse of invertible geodesic quantum Markov chains: for each of the three ordered conditional-mutual-information constructions, the $t$-quantum Markov chains are precisely the quantum Markov chains for $0\leq t<1$, whereas at $t=1$ they are the invertible BS quantum Markov chains, a class that can be strictly larger. We also determine parameter-monotonicity regimes and the intersections with the $(\alpha,z)$ family. We prove strengthened data-processing and reconstruction estimates; we compare the three conditional orientations; we establish continuity bounds under positive lower-eigenvalue assumptions together with complementary discontinuity results; and we give a capacity-per-unit-cost interpretation.

We present a framework for topological learning based on exceptional point (EP) braiding in a non-Hermitian Bogoliubov-de Gennes Hamiltonian. A closed algebraic equation for the EP super-surface is derived; through momentum quantisation in finite systems, it predicts the exact number and parameter positions of all EPs in real space, irrespective of system size. The EP topology is characterised by two quantised invariants the state-swap fidelity and the normalised Berry phase which cannot both be zero for a topological EP. A complete topological map shows that all EPs lie within a specefic region. Adiabatic encirclements confirm robust state swapping and yield a universal set of braid gates, including Pauli-X, Pauli-Y, Pauli-Z, a Hadamard-like gate, the T-gate, and a SWAP operation, with the special case where a is 0, providing additional phase gates. Building on these generators, we reformulate learning as braid programming a discrete search over the braid group that replaces gradient descent on continuous weights with combinatorial optimisation. A proof-of-concept genetic search successfully discovers short braid words that reproduce the standard Hadamard gate and the H.Z gate with perfect fidelity. This paradigm offers inherent noise immunity, catastrophic-forgetting prevention through compositional concatenation, and guaranteed generalisation by mathematical construction, establishing EP braiding as a promising substrate for robust, interpretable, and topologically protected neuromorphic computation.

Spatially varying coefficients are the primary source of circuit complexity in quantum simulation of linear advection-diffusion-reaction equations. This work presents a resource analysis of single-step quantum propagators constructed using sparse FABLE block encodings, Quantum Singular Value Transformation, and linear-combination-of-unitaries. We derive theoretical estimates for qubit count, gate complexity, and circuit depth in terms of the spatial discretization, sparsity, and polynomial degree, and compare these predictions with synthesized quantum circuits for one- and two-dimensional variable-coefficient problems. The resulting analysis quantifies the cost of encoding realistic transport operators and provides practical resource estimates for near-term implementations.

We investigate the ground-state properties and quantum critical behavior of a non-Hermitian transverse-field Ising chain subjected to longitudinal and complex transverse magnetic fields. To address this interacting many-body problem, we employ real-valued neural quantum states based on Restricted Boltzmann Machines (RBMs), optimized using Variational Monte Carlo (VMC) sampling. Spectral analysis of finite chains reveals exceptional points associated with spontaneous parity-time (PT) symmetry breaking. A real-valued RBM framework is developed to reconstruct the ground-state eigenstates of the non-Hermitian Hamiltonian. Benchmark comparisons with exact diagonalization demonstrate that the RBM approach accurately reproduces the ground-state energy, magnetization, and spin-spin correlations. Extending the analysis to larger system sizes, we identify a non-Hermitian quantum phase transition characterized by PT-symmetry breaking and the emergence of magnetic order. Our results establish real-valued neural quantum states as an efficient and scalable framework for investigating critical phenomena in interacting non-Hermitian quantum systems.

Large-scale quantum systems have advanced rapidly via the exploration of more particles and higher dimensions, offering great potential for developing quantum technologies. However, their characterization becomes prohibitive with increasing local dimensionality and particle number. Here we propose high-dimensional quantum overlapping tomography based on a graph-theoretic formulation, which allows one to efficiently reconstruct few-body marginals of multipartite high-dimensional quantum systems. We experimentally realize it on a photonic four-party entangled state in a $4 \times 4 \times 2 \times 2$ system. Using measurements in mutually unbiased bases, we reconstruct all six two-body marginals with only 25 projective measurement settings, compared with 94 and 225 settings for independent tomography of all two-body reduced states and full state tomography, respectively. The reconstructed marginals reveal a layered entanglement structure vital for high-dimensional quantum networks. We further show that these marginals enable more noise-resilient certification of multipartite high-dimensional entanglement than the fidelity-based criterion. Our work thus offers a scalable route for learning multidimensional quantum systems.

Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppress logical errors and preserve logical quantum information. Building fault-tolerant architectures requires increasing the code distance, which in turn places growing demands on decoding accuracy, scalability, and practical deployability. While a wide range of decoding algorithms have been proposed and demonstrated, achieving reliable, scalable, and real-time decoding remains a significant challenge. Machine-learning (ML) approaches are particularly well suited to this setting, as quantum error decoding is fundamentally a problem of processing large volumes of classical data with complex spatiotemporal correlations. This chapter surveys ML-based methods for quantum error decoding, with a focus on topological codes and an emphasis on architectural principles, practical performance, and real-time considerations. We first frame decoding as a learning problem and outline key paradigms, including discriminative, generative, and reinforcement-learning formulations. We then introduce the neural network building blocks that underpin most contemporary neural decoders and discuss how these components can be integrated to balance expressivity, scalability, and latency. Building on this architectural perspective, we review recent progress and benchmarks in neural decoding for memory experiments, and discuss real-time decoding, open challenges, and future directions toward scalable fault-tolerant quantum computing.

A nuclear Fermi liquid exhibits competing pairing instabilities in different spin, isospin, and orbital channels. In a Fermi-surface renormalization group (RG) treatment, the channel that develops a pole first is determined not only by its tree-level attraction but also by its one-loop RG coefficient. We illustrate this mechanism in a minimal $S/P$-wave model. A spherical Fermi surface establishes the reference competition between the lowest even- and odd-parity interactions. Axial deformation changes the relevant Fermi-surface integrals and lifts the degeneracy between longitudinal and transverse $P$-wave components. In isospin-asymmetric matter, neutron–proton Fermi-momentum splitting restricts the simultaneous low-energy contribution of the two species and can terminate the $np$ running at finite threshold scales. Our calculations are intended as controlled one-loop RG illustrations rather than as quantitative nuclear-matter calculations. We show how the Fermi-surface geometry and composition can change the ordering of competing pairing instabilities.

We present a reinforcement learning (RL) approach to the co-design of stabilizer sets of Quantum Error Correcting Codes (QECCs) and decoders. We show how to produce a generative model that produces Bivariate Bicycle (BB) codes based on the choice of decoder. Specifically, we fix a decoder architecture and use Proximal Policy Optimisation (PPO) to train an agent over BB codes to maximise decoder performance under a depolarising channel noise model.

We study the quantum dynamics of two interacting anyons in the Anyon-Hubbard model on a four-site plaquette, a system that is exactly mappable to a Bose-Hubbard model. We reveal that static Aharonov-Bohm (AB) caging, induced by only specific statistical phases, emerges in the strongly interacting limit but breaks down under weak interparticle interactions. To address this, we demonstrate that statistical-factor-induced AB caging can be dynamically restored via Floquet engineering. This dynamical mechanism, governed by the synthetic Floquet flux and the anyonic statistical phase, extends the caging effect into the weakly interacting regime across the full spectrum of statistical phases. Furthermore, we show that the external drive enables the selective caging of anyons, providing an efficient approach for manipulating anyons and identifying statistical phases.

We benchmarked quantum kernel $k$-means against classical clustering for credit-card fraud detection on real transaction data, at up to \MaxQubits{} qubits, under a protocol with separated selection and reporting data and matched search budgets. We find no robust quantum advantage: the sign of the difference depends on register size, all effect sizes are below $0.013$ ARI, and the single significant advantage we observe is fully explained by the number of configurations searched. We further show that ordinary hyperparameter choices move performance by considerably more than the quantum kernel does, that additional qubits degrade rather than improve performance through kernel concentration, and that the clustering framing itself fails at realistic class imbalance though kernel-based anomaly scoring does not. We regard the methodological contribution as the more durable one. The search-budget ablation in particular is inexpensive and, in our case, decisive: it converted a statistically significant advantage into a procedural artefact. We would encourage its routine use.