Papers

We propose a general Floquet-engineered scheme for squeezed phonon lasing that enables a continuous crossover from conventional phase-diffused phonon lasing to phase-locked squeezed phonon lasing. Our system consists of two pairs of periodically driven spins–a primary pair and an ancilla pair–coupled to a single mechanical mode. While the underlying mechanism applies generally to such spin-mechanical systems, we focus on a solid-state implementation based on color centers embedded in a circular hexagonal boron nitride (hBN) membrane. We show that the primary spins provide mechanical gain, whereas the ancilla pair engineer a dissipative channel for the Bogoliubov mode, thereby stabilizing robust steady-state squeezed phonon lasing. We support these findings through a comprehensive steady-state analysis of the lasing threshold, mechanical occupation, emission spectrum, and second-order correlations. Remarkably, we demonstrate that Floquet engineering intrinsically induces phase locking while enabling controllable quadrature squeezing, providing a simple and versatile route toward squeezed lasing in solid-state spin-mechanical platforms.

Fermionic non-Gaussianity is an essential resource for unlocking the full computational power of fermionic quantum platforms. In this work we develop monotones and efficient quantum algorithms for fermionic non-Gaussianity, all built on the eigenvalue structure of the operator $\Lambda = \sum_{j=1}^{2n}\gamma_j\otimes\gamma_j$ defined on two copies of an $n$-mode fermionic state, accessible via Bell sampling. In particular, we introduce the bridge degree of even pure states, a novel non-Gaussianity monotone defined as the largest eigenvalue of $\Lambda$ whose eigenspace is populated by two copies of the state. Our key technical result is that the bridge degree is non-increasing under post-selected Gaussian protocols, which yields no-go theorems for Gaussian conversion beyond the reach of previously known monotones and shows that the resource theory of fermionic non-Gaussianity is irreversible in the exact-conversion setting. Beyond this, the bridge degree exhibits several further features: it (i) is easy to compute, (ii) is efficiently witnessed through Bell sampling, (iii) lower-bounds the non-Gaussian gate complexity of state preparation, (iv) controls the non-Gaussian gate complexity of producing quantum state designs, and (v) naturally extends to mixed states via the Choi–Jamiołkowski isomorphism. We further develop an approximate variant together with an efficiently measurable lower bound, yielding an experimentally certifiable lower bound on the non-Gaussian cost of approximately preparing any state, based directly on Bell-sampling data. Finally, the same eigenvalue structure underlies two Bell-sampling-based algorithmic primitives, both with polynomial sample complexity: a two-copy Gaussianity test with perfect completeness, optimal among two-copy tests sharing this property, and a test for the state $2$-design property of matchgate-invariant ensembles.

Quantum optimal control can produce fast and robust multi-qubit gates, but experimentally calibrating the resulting high-dimensional waveforms remains challenging because direct searches over large parameter spaces converge slowly. Building on the low-rank structure of quantum-control landscapes, we develop and benchmark a Hessian-based calibration method for optimal-control gates. The method identifies the few waveform directions that affect fidelity to leading order, with the number of directions set by the accessible leakage and coherent error channels, and optimizes only within this principal space using closed-loop experimental feedback. We apply this approach to an amplitude-robust controlled-Z gate on metastable-state 171Yb nuclear-spin qubits. Experimentally, we verify the predicted Hessian-sensitive directions and demonstrate rapid convergence of the optimization protocol. The optimized gate reaches a raw fidelity of 0.9959(2), increasing to 0.99902(7) after postselection on no detected loss, and the performance is essentially unchanged under laser-power variations of up to 20%. We further show that the same fidelity Hessian directions can correct certain Hamiltonian parameter errors. These results establish low-rank Hessian optimization as an efficient and physically motivated calibration strategy for high-dimensional optimal-control gates, which is broadly applicable to many qubit types.

An operational way of measuring entanglement in a balanced two-path interferometers is presented, where path information is carried by some internal degree of freedom which, in turn, gets entangled with an ancilla system. The analysis is based on a tripartite description involving paths, an internal qubit degree of freedom, and some ancillary states entangled with the internal degree of freedom. It is then applied to two physically distinct experimental situations: a modified Stern-Gerlach interferometer with spin-1/2 particles and a Mach-Zehnder interferometer with photons carrying polarization. The ancilla degree of freedom may not be experimentally accessible. Tracing out the ancillary system, and employing a quantum erasing procedure based on the internal degree of freedom, it is demonstrated that a concurrence-based measure of the entanglement, between the internal degree of freedom and the ancilla, can be extracted directly from measurable asymmetry of the two output channels. These results show that loss of coherence, quantum erasure, and entanglement estimation in interferometric experiments arise from the same underlying correlation structure and provide a compact experimentally accessible framework for quantifying entanglement in qubit systems.

Automorphisms of quantum codes, when they exist, offer a pathway toward fault-tolerant gate implementation via qubit relabeling. Although useful, the conditions under which automorphisms appear in a given code remain poorly understood. In this paper, we develop an algebraic framework for systematically analyzing and engineering automorphisms in Generalized Bicycle (GB) codes. Central to our approach is the derivation of a three-space dependency between the polynomial ring space, the parity check matrix space, and the $\mathbb{F}_2^{2\ell}$ qubit space, similar to the structure found in the study of classical cyclic codes. By expressing GB codes as a pair of cyclic submodules of $R_\ell^2$, where $R_\ell \cong \mathbb{F}_2[x]/\langle x^\ell-1\rangle$, we reduce the search for code automorphisms to a deterministic algebraic problem, deriving necessary and sufficient conditions for the existence of block-separable automorphisms built from cyclic shifts, ring automorphisms and block-swaps. We connect these conditions to the fold-transversal gate framework, providing explicit criteria for the existence of $H$-, $S$-, and $CX$-type fold-transversal gates. We further discuss structured bases for logical operators in order to determine the logical action of a given automorphism. Finally, we introduce the Maximal Cube Root (MCR) code family, a family of GB codes constructed around the principle of maximizing automorphism flexibility and fold-CX gates. We demonstrate a collection of $k=2$ MCR codes up to $d=13$ generating the 2-qubit Clifford group via automorphism and fold-transversal gates, with stabilizer weight ranging from 8 to 16, and $k>2$ MCR codes with a minimum of 20 distinct logical gates achievable from automorphisms. This serves as a first demonstration of inverse design: using these methods to build codes around a rich automorphism structure from the ground up.

Stimulated Raman transitions in Faraday geometry allow for simultaneous single-shot qubit readout and qubit control. It involves driving an unbalanced $\Lambda$ system via an auxiliary excited state. Due to the simultaneous driving of both transitions with unequal detuning, the resulting time-dependent Stark shift gives rise to additional resonance conditions beyond the conventional picture. We identify a distinct regime in which repeated passages through avoided crossings lead to step-like population inversion arising from Landau-Zener-Stückelberg interference. By changing the detuning beatnote, we demonstrate a controlled continuous crossover from Rabi-like oscillations to adiabatic population switching. These findings establish the oscillating Stark shift as a mechanism for engineering and controlling spin dynamics in Faraday geometry.

We establish a single-letter and efficiently computable strong converse bound on the classical identification capacity of quantum channels. By equipping the $n$-fold channel output space with a product state-weighted $\sigma$-Euclidean geometry, we allow trace-distance separation constraints for identification codes to be controlled by Euclidean covering estimates. Using Sudakov's inequality, we bound the covering numbers of the $n$-fold channel outputs via their Gaussian mean widths in the weighted geometry, whose exponential growth in $n$ is governed by the operator norm of a single-letter positive operator. Upon optimizing over all weighing states $\sigma$, this yields a strong converse bound on the identification capacity of the channel, which also admits a semidefinite representation. Our method improves the best known converse bounds on the identification capacity of several important examples, such as depolarizing, Pauli, erasure, and amplitude damping channels. We also discuss extensions of this method to more general Euclidean geometries on the output space.

Dispersive readout of the transmon qubit in circuit QED is known to lose its quantum non-demolition character at small to moderate measurement drive amplitudes. This phenomenon is understood to originate from Laundau-Zener transitions at accidental multi-photon resonances, where $n$ drive photons can promote the transmon by $m$ levels. This interpretation has been shown to be in agreement with experiments characterizing the dispersive readout of a single transmon. The impact of these measurement-induced state transition (MIST) of a transmon embedded in a multi-qubit chip, however, remains largely unexplored. Here, we show that the presence of other components, such as qubits and couplers, can affect the MIST threshold of a measured transmon. To arrive at these results, we present a general method to characterize measurement-induced transition when the qubit under readout is coupled to other circuit elements, a ubiquitous situation in circuit QED-based quantum processors. As an example, we consider the case of two transmon qubits, and we show that the spectator qubit can be impacted by the measurement-induced transition of the readout qubit and, conversely, that the presence of the spectator qubit can lower the MIST threshold of the readout qubit. Finally, we explore how adding a coupler mode between the two qubits further modifies these effects.

The laser-accessible nuclear transition in the thorium-229 isotope has been identified as a promising candidate for the realization of an optical nuclear clock. Such a nuclear clock might rival or outperform current optical clocks based on electron-shell transitions in atoms or ions, is expected to be more robust against external perturbations, and provides enhanced sensitivity in clock-based tests of fundamental principles of physics. Here, we implement a thorium-229 nuclear clock by stabilizing a continuous-wave laser to the 148 nm nuclear transition with rapid feedback based on continuous absorption spectroscopy. The thorium-229 nuclei are embedded into a millimeter-sized, room temperature calcium fluoride crystal. A subharmonic of the 148 nm radiation is continuously compared to a Yb+ single-ion clock. The nuclear clock shows a simple shot-noise limited scaling of the fractional frequency instability of $3\cdot 10^{-12} \sqrt{\tau/\text{s}}$ where $\tau$ is the averaging time, approaching $10^{-15}$ instabilities over 1 day of continuous operation. Improvements of the instability by several orders of magnitude can be projected for future solid-state nuclear clocks. We use the nuclear clock to constrain models of ultralight dark matter by searching for periodic fluctuations and slow drifts in the nuclear transition energy, on time scales between 20 s and 1 day. Drawing benefit from the enhanced sensitivity of the thorium-229 transition, these constraints compete with the best atomic clocks concerning dark matter coupling to photons and go beyond previous measurements regarding coupling to the strong force and quarks.

We propose a bipartite entanglement measure defined as the minimal order-1 quantum Wasserstein distance from a state to the set of separable states. Owing to the universal data-processing inequality of the Wasserstein metric, the measure satisfies all fundamental axioms within a single geometric framework. A Lipschitz dual formulation yields explicit lower bounds for pure and mixed states, a sharp constant for two-qubit systems, and an expected value for Haar-random pure states. We further establish a quantitative connection to entanglement witnesses: any negative witness expectation value certifies a lower bound, and the dual variational bound is exactly the maximal violation achievable by a Lipschitz-1 witness. The approach naturally provides subadditivity, trace-distance estimates, and bounds on local observables, while pointing toward large-deviation conjectures. This work introduces a framework at the interface of entanglement theory, optimal transport, and experimental entanglement detection.

The variational quantum eigensolver (VQE) is a promising algorithm for near-term quantum chemistry applications, but selecting optimal ansatz circuits remains challenging. Expressibility, a metric quantifying a circuit's ability to explore the Hilbert space, has been proposed as a guide for ansatz selection, but recent work showed it inconsistently predicts VQE performance under realistic noise for $H_2$. We extend this investigation to cover both $H_2$ and $H_3^+$ under four execution scenarios: ideal, noisy, and noisy with zero-noise extrapolation (ZNE) or probabilistic error cancellation (PEC). We find that error mitigation does not reliably restore expressibility's predictive power. ZNE reduces error for only 4 of 12 $H_2$ circuits and 4 of 6 $H_3^+$ circuits, while PEC actually increases error in 11 of 12 $H_2$ circuits and all 6 $H_3^+$ circuits. We reproduce and extend Saib et al.'s key finding that circuit rankings scramble under noise (Spearman $\rho \approx -0.1$ between ideal and noisy rankings), and identify a new result: ZNE largely preserves noisy rankings ($\rho = +0.80$ for $H_2$) while PEC actively reorders them ($\rho = -0.22$). Noisy expressibility, computed from density matrix simulations, strongly predicts unmitigated performance for $H_3^+$ (Pearson $r = +0.91$, $p = 0.01$), but this metric is computationally intractable at scale. We demonstrate that zero-cost circuit topology metrics such as two-qubit gate count provide comparable or superior predictive power for PEC degradation ($r = +0.96$ for $H_3^+$), while standard expressibility best predicts noisy and ZNE performance for $H_2$ ($r = +0.74$ and $r = +0.77$).

Phase randomization is essential for the security of practical decoy-state quantum key distribution (QKD) systems. Commonly, implementations rely on laser sources which are either actively phase-randomized, or gain-switched. However, at high repetition rates these show correlations, which can ultimately compromise security and performance. We present a 1.25 GHz phase-randomized QKD source based on a super-luminescent light emitting diode (SLED) operating in the C-band as a compact and cost-effective alternative. The source generates $\sim100$ ps optical pulses with $400$ ps pulse-to-pulse separation, compatible with high-speed time-bin encoding. Interferometric measurements demonstrate $>99\%$ visibility between adjacent time bins, confirming strong first-order coherence within the same quantum signals, while the spontaneous-emission-driven nature of the SLED ensures intrinsic global phase randomization between adjacent signals. This work establishes a scalable SLED-based platform for high-speed prepare-and-measure QKD systems.

Moment/Sum-of-Hermitian-Squares relaxations for noncommutative polynomial optimization problems have become an important tool for analyzing problems within quantum theory. Despite their widespread success, little is known about their rate of convergence and, consequently, their accuracy. In this work, we develop explicit convergence rates for relaxations of noncommutative polynomial optimization problems generated from the Pauli algebra – covering applications to the ground state energy problem for n-qubit systems. In particular, we show that the rate of convergence can be bounded in terms of the smallest roots of a family of orthogonal polynomials known as Krawtchouk polynomials. Our result represents the first quantitative analysis of the rate of convergence for relaxations of noncommutative polynomial optimization problems.

The quantum-technology revolution is reshaping computing, sensing, and communication. In magnetometry, recent advances leverage precise control of spin qubits and color centers in solid-state crystals for mesoscopic-scale sensing. Yet at very low temperatures, superconducting sensing technology remains unrivaled because of its non-invasiveness and higher sensitivity. Here we describe a class of superconducting sensors that offers low loss and quantum non-demolition measurement characteristics. We designed and fabricated a superconducting flux-tunable resonator (tRes) in a superconducting chip foundry and matured it to a level that combines the speed of an inductor-capacitor circuit with the flux sensitivity of a superconducting quantum interference device (SQUID) to perform magnetometry at milli-kelvin temperature to investigate targets. We introduce its fundamental functionality readily at MHz magnetic sampling rate, showcase two measurement modalities, and investigate three circuits with gradually increasing complexity to extract target-specific information. The combination of high sensitivity and fast readout characteristics make tRes an attractive and versatile magnetometer.

Decoded Quantum Interferometry (DQI) uses coherent decoding and a quantum Fourier transform to find high-quality solutions of structured optimisation problems. Existing analyses are closely tied to Hamming space, which underlies the optimisation objective, Dicke state preparation and the decoding step of the algorithm. Here we extend the core DQI mechanism beyond Hamming space to finite geometries with translation symmetry, where points are grouped into shells by their distance from a basepoint. Mathematically, these geometries are translation association schemes. In this setting the algorithm can be analysed by tracking one amplitude per shell, and biasing the prepared state towards high-quality solutions becomes a finite tridiagonal eigenvalue problem. As a non-Hamming example, we develop an efficient DQI protocol for finding an m x n finite-field matrix with smallest rank difference to a target matrix. Initial states are uniform superpositions over fixed-rank matrices, and Gabidulin codes provide candidates for efficient low-rank decoding up to a cutoff l. For this objective, this finds solutions with an effective-rank proxy near min(m,n)-l, and the corresponding expected score can be converted into a constant-probability bound on the residual rank of a sample. For Gabidulin nearest-codeword instances, a covering-radius obstruction shows that this bound does not imply an additive guarantee for the true optimum, and we do not claim a quantum advantage for the rank-metric construction. The results instead identify the geometric and coding ingredients for DQI beyond Hamming space.

Essential-state models are central for quantum control and technology in broad regimes of light-matter interaction. The canonical effective Hamiltonian is obtained equivalently from adiabatic elimination, the Markov approximation, and the pole approximation. These approximations are known to break down at high intensities, significantly limiting their applicability to moderate light-matter interaction. We show how this limitation can be addressed by applying quasi-degenerate Rayleigh-Schrödinger perturbation theory (QD-RSPT). We reconcile QD-RSPT with adiabatic elimination and propose a quasi-degenerate extension of adiabatic elimination that is robust when the detuning of the essential states is non-negligible. The accuracy of QD-RSPT is demonstrated in both the low- and high-frequency regime, showing excellent agreement with Floquet calculations at high intensities. The crucial corrections to adiabatic elimination make the eigenvectors of the effective Hamiltonian non-orthogonal. Physically, this allows us to account for the asymmetric strength with which different essential states couple to the non-essential states. We expect that our systematic approach to effective Hamiltonians from QD-RSPT will constitute a new state of the art in intense light-matter interaction and quantum optics with novel forms of strong coupling and quantum control phenomena being conceivable.

Isotropic errors have been shown to be immune to conventional error correction techniques. While general theoretical frameworks have been proposed to model such errors, there have been no studies so far analysing their concrete impact on practical use-cases. Here we explore the effect of isotropic errors on the complexity of Grover's search algorithm through numerical simulations, with an analysis of the impact on the algorithm's performance and success probability. The results provide insights into the robustness of Grover's algorithm against isotropic errors, highlighting potential challenges for implementations on noisy quantum hardware. All results presented here are obtained through numerical simulations using the open-source python library isotropic developed as part of this work. The source code, numerical simulations and documentation for the library are available online at https://www.github.com/lazyoracle/isotropic-error-analysis .

We review the problem of description of the dynamics of driven-disspipative spinor polariton condensates, focusing on the terms corresponding to the coupling between a macroscopic wavefunction of the condensdate and incoherent excitonic reservoir created by a non-resonant pump. We demonstrate that the existing version of the theory breaks down in case, when reservoir has non-zero components of the Stokes vector corresponding to in-plane linear polarization. The polarization invariant theory of reservoir to condensate coupling is formulated with use of the spin density matrix formalism.

We investigate geometric-phase control in resonantly driven two-level atoms exposed to near-degenerate dichromatic laser pulses. In contrast to conventional two-color schemes based on widely separated frequencies, the closely spaced frequency components generate a slowly varying beating envelope that repeatedly reverses the pseudo-spin rotation on the Bloch sphere. This enables coherent control of the geometric phase accumulated during Rabi dynamics and strongly modifies the resulting photoelectron spectra. Using an exactly solvable model for flat-top pulse envelopes, we derive the essential-state dynamics analytically and analyze photoionization induced both by an auxiliary field and by the dichromatic driving field itself. We show that the formation of Autler–Townes doublets can be interpreted in terms of destructive interference associated with geometric phases acquired during completed pseudo-spin rotations. Beyond the canonical Autler–Townes regime, beat-induced reversal dynamics lead to qualitatively different spectral structures, including re-emergent central peaks and higher-order sidebands. These effects originate from the nonuniform temporal evolution induced by the beating envelope, which modifies the balance between positive and negative excitation amplitudes. Our results establish beating-field control as a route toward engineering geometric-phase interference in ultrafast light–matter interactions and suggest broader applications in coherent control of atoms, molecules, and x-ray-driven systems.

We extend the random-matrix theory of chaotic scattering to quantum dots whose dynamics is monitored by time-resolved measurements. Starting from a scattering matrix drawn from a circular ensemble, we construct the corresponding ensemble of Kraus operators for the monitored evolution of the many-body density matrix. In the single-particle sector the sum over measurement outcomes can be carried out algebraically, giving a discrete-time quantum master equation for the transferred charge. We solve this equation numerically and compare the resulting charge-transfer statistics with closed-form random-matrix predictions. The latter rely on an equipartition rule for monitored particles, which we formulate as a conjecture and test against the master equation.

The energy-based approach to operator selection in ADAPT-VQE relies on reconstructing the one-parameter energy landscape for each operator in the pool. In fermionic implementations, the cost of reconstructing this energy landscape often becomes a bottleneck. We address this issue through an exact Hamiltonian transformation that reformulates the one-parameter energy landscape according to a generator-dependent fragmentation of the transformed Hamiltonian. While our method is mathematically identical to standard fermionic Rotoselect, it effectively reduces its cost by about a factor of two, bringing it close to that of gradient-based ADAPT-VQE. We use this formulation to benchmark the gradient-based and energy-based selection approaches in combination with two ansatz-optimization strategies – "last", where only the appended operator is optimized, and "full", where the full ansatz is re-optimized – and with both fixed-orbital and orbital-optimized formulations. The benchmark comprises $\text{LiH}$, $\text{BeH}_2$, and $\text{H}_2\text{O}$ at both equilibrium and stretched geometries. In the most weakly correlated system, pairing energy-based selection with "last" optimization enables the efficient construction of an accurate ansatz, which avoids any VQE optimization. As correlation increases, full ansatz re-optimization and orbital optimization become the main factors governing convergence and overall resource cost. This study shows how exact Hamiltonian transformations provide an effective route to reducing the measurement overhead of fermionic energy-based ADAPT-VQE. Moreover, the benchmark clarifies the relative role of operator scoring approach, re-optimization strategy, and orbital treatment in the performance of ADAPT-VQE.

Establishing quantum advantage requires comparison against the best achievable classical simulation. The Q-CTRL team recently simulated quench dynamics of the one-dimensional Fermi-Hubbard model on an IBM processor, completing a $L=60$ evolution to time $t=6$ in under three minutes and claiming a $3000\times$ speedup over classical Time-Dependent Variational Principle (TDVP) simulation at bond dimension $\chi=4096$. Their classical benchmark required over 160 hours on a CPU cluster, failed to converge in the high-entanglement regime $t\in[5.2,6]$, and left the most challenging window of the experiment unverified. Here, we push the boundaries of classical simulation by exploiting the full $\mathrm{U}(1)\times\mathrm{SU}(2)$ symmetry of the Fermi-Hubbard Hamiltonian combined with GPU-accelerated tensor contractions. Reaching bond dimensions up to $\chi\approx62{,}000$ on four NVIDIA H200 GPUs – among the largest ever achieved in TDVP simulations and fifteen times larger than Q-CTRL's classical baseline – we achieve fully converged results across the entire simulation window, including rigorous certification of the previously unresolved high-entanglement regime $t\in[5.2,6]$. We further advance the classical frontier to $t=7$, which lies beyond the quantum hardware experiment and any previously verified classical evolution of the full wavefunction. At the bond dimension comparable to Q-CTRL's best classical run, our GPU implementation completes in $\sim\!100$ minutes, directly reducing the claimed $3000\times$ quantum advantage to $\sim\!36\times$. These results substantially narrow the quantum-classical performance gap and establish a new standard for tensor-network benchmarking of large-scale quantum simulations.

Imaginarity has shown to be an important resource in quantum information. The witness theory of quantum resource, such as entanglement witness, coherence witness, and imaginarity witness, has been established, in particular entanglement witness and coherence witness have been extensively explored. We present here another class of imaginarity witnesses beyond the one in [{Phys. Lett. A} 530, 130135 (2025)]. Within our framework, any nonreal Hermitian operator under the reference basis is an imaginarity witness and only finite of them can detect all the imaginarity states. As the common approaches that were explored in the witness theories of entanglement and coherence, we then explore the relations between these imaginarity witnesses in different cases: (i) when they can detect common imaginary states, (ii) when they can detect the same sets of imaginary states, and (ii) when they obey the finer relation. Finally, we define an imaginarity measure in terms of witnesses, termed witnessed imaginarity, and prove that it coincides with both the trace norm of imaginarity and the robustness of imaginarity.

We show that the recent proposal for superradiant emission of neutrinos cannot evade our proof that superradiant neutrino emission is fundamentally impossible. Pairing two fermions in a molecule does not remove the cancellation of interference terms in neutrino emission due to fermionic anticommutators.

A single quantum pulse undergoing parametric amplification feeds into at most two pulses in the output. In this work, we present an efficient, analytical method for finding the quantum state of these output modes. Our method applies the amplification to the vacuum rather than to the input state, and subsequently applies a transformed version of the operator that creates the input state from vacuum. Given the input and output pulse mode functions, the method is analytical, and therefore computationally very efficient, and it can be readily generalized to multiple non-vacuum input modes. We exemplify the method by computing the output quantum states resulting from the input of a coherent, a Schrödinger cat, and a single photon input quantum state. We further employ the method to obtain the quantum state in one of the two output modes heralded upon detection of vacuum in the other, least populated, mode.

This study presents an analytical investigation of entanglement quantification in three-qubit pure states through the Acín canonical representation, which serves as a generalization of the two-qubit Schmidt decomposition. Driven by the intricacies of multipartite entanglement and the shortcomings of current measures, we employ a global concurrence measure derived from the generalized concurrence of various bipartitions within the system. The characteristics of this measure are explored both analytically and numerically across a range of SLOCC entanglement classes, such as product, biseparable, GHZ-type, and W-type states. When compared to the three-tangle and tripartite negativity, our findings indicate that this measure yields complementary insights into multipartite correlations. The outcomes underscore the significance of this measure in elucidating the structure and distribution of entanglement within three-qubit quantum systems.

Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is constrained by long-tail predicate imbalance. Classical SGG models rely on dataset statistics, leading to biased predictions toward frequent relations rather than fine-grained semantic predicates. Although debiasing improves mean recall, predicate classification in existing frameworks still relies on high-dimensional classical decision modules with high parameter overhead. This work introduces a resource-efficient hybrid quantum predicate classifier for SGG by replacing the classical predicate head in the CFEN with a QP-Head trained using class-balanced WCE. To the best of our knowledge, this work is among the first evaluations of a hybrid quantum architecture for scene graph predicate classification on Visual Genome 150. We evaluate the impact of qubit count, encoding strategy, entangling topology, and circuit depth. Under a 4-qubit configuration using Amplitude Embedding and Strongly Entangling Layers, the QP-Head projects 4096-dimensional pair features into a 16-dimensional quantum input while using 96 trainable quantum parameters. Operating within this compressed representation space under WCE training, it achieves strong long-tail recognition, reaching a peak mR@100 of 57.25% compared to the 41.1% mR@100 reported in the CFEN baseline under standard training. Protocol-matched ablations indicate that the primary advantage of the quantum head lies in extreme representational compactness rather than an accuracy gain over full-dimensional weighted classical heads: it achieves global recall within 0.19pp of the 2048-dimensional WCE classical baseline while operating from a 128x smaller head feature space. Scaling to 8 qubits maintains robust long-tail performance (55.38% mR@100 with 384 quantum parameters), while depth analysis highlights expressibility-latency trade-offs.

We investigate a digital quantum reservoir computing (QRC) framework for multi-step forecasting of automated teller machine (ATM) cash demand time series on near-term quantum devices. The proposed approach uses parametrized four-qubit reservoirs with a fixed structure exploiting partial measurement and reset, where temporal data is encoded in rotation angles. Training is restricted to a classical Ridge-regression readout. We systematically analyze the impact of the circuit ansatzë, reservoir memory, measurement-derived observables, and the execution backend on the forecasting performance. Experiments are performed with noiseless simulation, noise-aware emulation, and a real IQM Spark quantum processor. Although the QRC models do not outperform the classical Prophet benchmark in terms of Mean Absolute Error and Normalized Mean Squared Error metrics, they achieve more competitive results in Dynamic Time Warping metric, indicating a partial ability to capture temporal structure. These findings provide an empirical assessment of digital QRC for realistic financial forecasting and highlight both its current limitations and its potential on near-term quantum hardware.

We consider a spin system coupled to a noisy environment via an interaction Hamiltonian $H$ which is a polynomial of degree $n$ in the spin operators $S_i$ ($n$-magnetic Hamiltonian), and show that the use of the quantum super-Zeno effect in conjunction with a spin-anticoherent subspace, is highly effective in protecting the spin from the noise. When $n=1$, as in the case of unwanted rotations induced by stray magnetic fields, our scheme provides a quantum gyroscope, a concept that generalizes naturally to higher $n$ values (a quantum $n$-gyroscope). The gist of the proposed protocol is to use the quantum super-Zeno effect to confine a state to the subspace, and then exploit anticoherence to freeze its evolution within it.

Physics-informed neural networks (PINNs) often struggle on nonlinear partial differential equations (PDEs) with sharp gradients, stiff dynamics, high-frequency content, or multiscale structure. Such limitations, rooted in spectral bias, ill-conditioned optimization, and unstable convergence, restrict PINN accuracy in regimes where advanced solvers are most needed. In this work, we develop a hybrid quantum-classical physics-informed neural network (HQPINN) that integrates a classical neural-network backbone with a parameterized quantum circuit (PQC) to enrich the solution representation. The framework is benchmarked against a classical PINN on three representative nonlinear PDEs: Burgers' equation, the Allen-Cahn equation, and the Korteweg-de Vries (KdV) equation. The framework is further examined through a systematic sensitivity analysis of qubit count, circuit depth, PQC placement, collocation density, and classical-network width. Across all benchmarks, HQPINNs exhibit smoother training dynamics, reduced loss oscillations, and improved final accuracy, with the largest gains occurring in stiff and multiscale regimes. Relative L2 error decreases by about fourfold for Burgers' equation and fivefold for the Allen-Cahn equation, while improvements for the KdV equation are more moderate. Overall, the results demonstrate that carefully co-designed hybrid quantum-classical architectures can mitigate key limitations of classical PINNs and provide practical design guidance for near-term quantum-enhanced PDE solvers.