Papers

Scalable quantum computing requires architectural solutions beyond monolithic processors. Distributed quantum computing (DQC) addresses this challenge by interconnecting smaller quantum nodes through quantum communication protocols, enabling collaborative computation. While several experimental and theoretical proposals for DQC exist, emulator platforms are essential tools for exploring their feasibility under realistic conditions. In this work, we introduce a benchmarking framework to evaluate DQC emulators using a distributed implementation of the inverse Quantum Fourier Transform ($\mathrm{QFT}^{\dagger}$) as a representative test case, which enables efficient phase recovery from pre-encoded Fourier states. The QFT is partitioned across nodes using teleportation-based protocols, and performance is analyzed in terms of execution time, memory usage, and fidelity with respect to a monolithic baseline. As part of this work, we review a broad range of emulators, identifying their capabilities and limitations for programming distributed quantum algorithms. Many platforms either lacked support for teleportation protocols or required complex workarounds. Consequently, we select and benchmark four representative emulators: Qiskit Aer, SquidASM, Interlin-q, and SQUANCH. They differ significantly in their support for discrete-event simulation, quantum networking, noise modeling, and parallel execution. Our results highlight the trade-offs between architectural fidelity and simulation scalability, providing a foundation for future emulator development and the validation of distributed quantum protocols. This framework can be extended to support additional algorithms and emulators.

A qubit is an exhibition of quantum entanglement and a key element in the quantum teleportation process. In this paper, we review the role of qubits in quantum entanglement and quantum teleportation.

Quantum state preparation (QSP) for a general $n$-qubit state requires $O(2^n)$ CNOT gates and circuit depth, making exact amplitude encoding (EAE) impractical for near-term quantum hardware. We introduce an ancilla-free hybrid classical-quantum strategy that reduces this cost to $O(poly(n))$ for a broad class of compressible data. The method first applies a classical compression step to obtain a $d$-sparse representation of the input, loads this sparse vector using a sparse-state preparation routine, and then reconstructs the target state through a polynomial-depth quantum inverse transform. We evaluate the framework on synthetic benchmark signals and real biomedical time series using Fourier and Haar transforms, demonstrating substantial reductions in CNOT counts and circuit depth compared to EAE, together with competitive performance relative to the Fourier Series Loader (FSL). The quantum simulation results show that combining classical data compression with quantum decompression provides a scalable framework for efficient QSP, reducing quantum overhead without requiring variational training or ancillary registers.

Simulating many-body open quantum systems is an extremely challenging problem, with methods often restricted to either models with nearest-neighbor interactions or semi-classical approximations. In particular, modeling two-dimensional systems with realistic long-range interactions, in addition to dissipation, is of vital importance to the development of modern quantum computing and simulation platforms. In this paper, we present a construction of the time-evolution operator, as a projected entangled pair operator (denoted tePEPO), that can be used to evolve a tensor network ansatz through time. Interactions beyond nearest-neighbor, including interactions between sites not collinear in the lattice, can be represented efficiently as a tePEPO. Furthermore, we obtain approximations to realistic radial long-range interactions decaying with a power-law, that give accurate results with small tePEPO bond dimension. Finally, we consider a physical example of a Rydberg atom Hamiltonian with long-range dipolar interactions, and show evidence of a dipole-dipole blockading effect in presence of dissipation. This work demonstrates the applicability of tensor networks to two-dimensional systems widely studied in experiments, but previously inaccessible to non-semi-classical methods.

Optically levitated particles are used in a wide range of experiments to explore both fundamental physics and to act as sensors to a variety of external forces. One field of particular interest that these particles can be used to investigate is quantum mechanics. Previous research has yet to set an absolute upper bound on the size of objects that can be prepared in a quantum superposition. Exploring this limit involves allowing ever-larger objects to freely and coherently evolve to assess if their behaviour matches quantum or classical theoretical predictions. However, the long free evolution times required for these behaviours to be visible result in the experiments being gravitationally limited. Space based platforms therefore become the next key step in these investigations. In this white paper, we shall discuss our proposal for an optical levitation experiment in space that will explore the fundamental upper size limits of quantum mechanics. We shall cover the scientific motivation behind these investigations, then summarize the current status of our designs for the satellite. We will then review the aspects of the payload that require further development, then summarize the current estimates of the payload's requirements.

Continuous-variable quantum key distribution (CV-QKD) has emerged as a promising approach for secure quantum communication, offering advantages such as high key generation rates, compatibility with standard telecommunication infrastructure, and potential for integration on photonic chips. This review provides an accessible introduction to the theory of CV-QKD, aimed at researchers entering this rapidly developing field. We focus on fundamental concepts, key protocols, and security analysis essential for understanding CV-QKD systems, with a special emphasis on prepare-and-measure protocols using coherent states under asymptotic security conditions. We explain their equivalence to entanglement-based protocols and detail the security proof framework against collective attacks, encompassing both Gaussian and discrete modulation schemes. We also briefly address more advanced topics, including measurement-device-independent CV-QKD and finite-size security analysis. This work is motivated by Brazil's growing investment in quantum communication technologies. By presenting a clear learning path from basic concepts to advanced topics, this work aims to equip newcomers with the essential tools to engage with current research in CV-QKD, thereby supporting the training of a new generation of researchers in this strategic field.

For the sandwiched Rényi entropy the conditional entropy can be defined two ways: $\widetilde{H}^\downarrow_\alpha(A|B)_\rho, \widetilde{H}^\uparrow_\alpha(A|B)_\rho$. In the limiting case, $\alpha=1$, both definitions consolidate into conditional entropy $H(A|B)=S(AB)-S(B)$. The continuity inequality for conditional entropy $H(A|B)$, called the Alicki-Fannes-Winter (AWF) inequality, shows that if the states are close in trace-distance, then the conditional entropies are also close. Having the AWF inequality for conditional entropy, we show that the channel entropy defined through the relative entropy is continuous with respect to the diamond-distance between channels. Inspired by this, similar continuity inequalities for the Rényi conditional entropy $\widetilde{H}^\uparrow_\alpha$ were obtained in the work [A. Marwah and F. Dupuis, J. Math. Phys. 63, 052201 (2022)]. We provide continuity bounds for the sandwiched Rényi and Tsallis conditional entropies $\widetilde{H}^\downarrow_\alpha(A|B)_\rho, \widetilde{T}^\downarrow_\alpha(A|B)_\rho$ for states with the same marginal on the conditioning system. Similar to the previous bounds, our bound depends only on the dimension of the conditioning system. We apply this result to prove continuity of the channel entropy for Rényi and Tsallis channel entropies defined through the sandwiched Rényi and Tsallis relative entropies.

Convex roofs are natural ways to extend a scalar function defined on the extreme points of a convex set $K$ to the whole set. In this work we focus on the case $K = C \times C$ for some convex set $C$, and the extension of a distance $d$ defined on the extreme points of $C$. In general, the triangle inequality may not survive the extension process. We therefore consider the metric envelope of such extensions and study the metric properties they endow $C$ with. More generally, we also consider products of different convex sets $C_1 \times C_2$ and costs defined on their extreme points, in which case no metric envelope is involved. We then show a two-way correspondence between convex extensions and Kantorovich optimal transport formulations. On the one hand, these extensions can be recovered as quotients of standard, classical Kantorovich costs or Wasserstein distances by the barycenter map. On the other hand, various Kantorovich formulations re-write as such convex extensions. Classical optimal transport is recovered as the case of the simplex, and both the semiclassical optimal transport cost of Golse and Paul and the entanglement-free quantum Wasserstein objects of Beatty and Stilck França are recovered by considering the non-simplex convex set of quantum states. This work provides a clear bridge between the theory of convex extensions and that of optimal transport, with a unifying framework for classical, semiclassical and entanglement-free quantum instances of Kantorovich formulations.

The response of a ferrimagnetic sphere resonator to an externally applied parametric excitation is experimentally studied. Measurement results are compared with predictions derived from a theoretical model, which is based on the hypothesis that disentanglement spontaneously occurs in quantum systems. According to this hypothesis, time evolution is governed by a modified master equation having an added nonlinear term that deterministically generates disentanglement. It is found that the disentanglement–based model is compatible with the experimental results. In particular, the model can qualitatively account for an experimentally observed instability in the system under study, which cannot be derived from any theoretical model that is based on a linear master equation.

We analyze the formal equivalence between the electromagnetic energy conservation law derived from Maxwell's equations in an optical microcavity and the conservation of a probability fluid associated with the de Broglie-Bohm theory for an effective massive particle describing a photon in this cavity. This work is part of a critical analysis of recent experiments [Nature 643, 67-72 (2025)] carried out with a view to refuting the de Broglie-Bohm theory. Furthermore, the consequences of our analysis for microphotonics go far beyond these experiments. In particular, extensions that take into account photon spin and stochastic aspects associated with radiative or absorption losses are considered.

In Lieb lattices, geometric frustration and destructive interference of hopping cancels the occupation of certain sites, leading to flat-band physics. Here, we show numerically how, in the driven-dissipative Bose-Hubbard (DDBH) model arranged into Lieb lattices and related geometries, specific localised driving schemes can repurpose this interference to generate enhanced antibunching via a mechanism similar to the so-called unconventional photon blockade. Stochastic simulations using the positive-P method allow us to calculate occupations and second order correlations exactly for extended lattices. We use this to optimise the parameters for the possible observation of this effect in polariton micropillar experiments. This work demonstrates the possibility of using localised driving and interference effects to generate non-trivial quantum correlations in open quantum lattice systems. Specifically, producing antibunching in the dark sites of the flat band system rather than the usual and less useful bunching.

Quantum dots can confine single electrons or holes to define spin qubits that can be operated with high fidelity. Experimental work has progressed from linear to two-dimensional arrays of quantum dots, enabling qubit interactions that are essential for quantum simulation and computation. Here, we explore architectures beyond planar geometries by constructing quantum dot arrays in three dimensions. We realize an eight-quantum dot system in a silicon-germanium heterostructure comprising two stacked germanium quantum wells, where quantum dots are positioned at the vertices of a cuboid. Using electrostatic gate control, we load a single hole into any of the eight quantum dots. To demonstrate the potential of multilayer quantum dot systems, we show coherent spin control and hopping-induced spin rotations by shuttling between the quantum wells. The ability to extend quantum dot arrays in three dimensions provides opportunities for novel quantum hardware and high-connectivity quantum circuits.

Hyperentanglement, which refers to entanglement encoded in two or more independent degrees of freedom (DOFs), is a valuable resource for the future high-capacity quantum network. Certifying hyperentanglement sources work as intended is critical for the hyperentanglement-based quantum information tasks. Self testing is the strongest certification method for quantum state and measurement under minimal assumptions, even without any knowledge of the devices' inner workings. However, the existing self testing protocols all focus on one-DOF entanglement, which cannot self test the multi-DOF entanglement. In the paper, we propose a hyperentanglement self testing framework. We take the self testing for the polarization-spatial-mode hyperentangled Bell states as an example. The self testing is based on the violation of two-dimension CHSH test in each DOF independently. The two-step swap isometry circuits are proposed for self testing the entanglement in spatial-mode and polarization DOFs, respectively. All the sixteen polarization-spatial-mode hyperentangled Bell states can be self tested. Our hyperentanglement self testing framework has three advantages. First, it is a general hyperentanglement self testing framework, and can be extended to self test multi-DOF hyperentanglement and multipartite hyperentanglement. Second, it can provide the robust hyperentanglement self testing and establish the relation between the lower bound of fidelity and the imperfect violation of Bell-like inequality in each DOF. Third, it is feasible with current experimental technology. Our hyperentanglement self testing framework provides a promising way to certify complex hyperentanglement sources, and has potential application in future high-capacity quantum network.

Cold-atom systems have emerged as a highly promising avenue for quantum-enhanced position, navigation, and timing applications. However, their wider adoption is currently hampered in part by the large footprint of the systems. In leveraging the miniaturisation possible through photonic integrated circuits, cold-atom sensors would be able to reach much wider commercial adoption. In this paper, we introduce a platform for evanescently trapping 87Rb using strip silicon nitride waveguides buried in silica using red- and blue-detuned fundamental and higher-order modes, providing a three-dimensional adjustable trap for BEC-based, chip-scale work in quantum science and technologies.

Accurate determination of ground-state energies for molecules remains a challenge in quantum chemistry and a cornerstone for progress in fields such as drug discovery and materials design. The Variational Quantum Eigensolver (VQE) represents a leading hybrid quantum-classical paradigm for addressing this challenge; however, its widespread realization is limited by noise and the restricted scalability of current quantum hardware. Achieving efficient simulations on Noisy Intermediate-Scale Quantum (NISQ) devices and forthcoming Fault-Tolerant Application-Scalable Quantum (FASQ) systems demands a detailed understanding of how computational resources scale with molecular complexity and fermion-to-qubit encodings. In this study, resource requirements for VQE implementations employing the Unitary Coupled Cluster Singles and Doubles (UCCSD) ansatz are systematically analyzed. The molecular Hamiltonian is formulated in second quantization and mapped to qubit operators through the Jordan-Wigner (JW), Bravyi-Kitaev (BK), and Parity (Pa) transformations. Hamiltonian reduction strategies, including $\mathbb{Z}_2$ tapering and frozen-core approximations, are examined to assess their effect on quantum resource scaling. The analysis reveals that appropriate transformations, when combined with symmetry-based reductions, can substantially reduce qubit counts by up to $\approx 50\%$ and quantum gate counts by up to $\approx 27.5\times$ and Hamiltonian Pauli string counts by up to $\approx 2.75\times$, relative to the corresponding unreduced Hamiltonian representations within the same active-space configuration for the representative set of molecular systems under study. These findings provide practical circuit-level insights for executing chemically relevant simulations on NISQ hardware, while establishing physical-resource baselines that may inform future logical-level analyses targeting FASQ systems.

This PhD thesis explores the potential of quantum computing to address computational challenges in high-energy physics (HEP). As the Standard Model (SM) leaves key questions unanswered and no signs of new physics have emerged since the Higgs boson discovery, advanced experimental and computational tools become necessary. Quantum computing offers a promising alternative to classical methods, and this work investigates three main avenues where quantum algorithms can be useful in HEP research. First, we develop quantum subroutines for computing Minkowski distances and identifying maximum values in unsorted data, inserting them into clustering algorithms such as $k$-means, Affinity Propagation, and $k_T$-jet clustering. These quantum algorithms match classical performance while offering theoretical advantages when implemented on quantum hardware with qRAM. Then, we introduce a novel quantum Monte Carlo integrator, Quantum Fourier Iterative Amplitude Estimation (QFIAE), which combines a quantum neural network with amplitude estimation to integrate multivariate functions. QFIAE is executed on simulators and real quantum computers to evaluate Feynman loop integrals via Loop-Tree Duality, and is extended to compute (partially on hardware) a physical observable at NLO in perturbative quantum field theory. Finally, we present Quantum Chebyshev Probabilistic Models (QCPMs) for modeling multivariate distributions, applying them to fragmentation functions of partons fragmenting into kaons and pions. These models demonstrate accurate generative and interpolation capabilities. We also show how entanglement between variables plays a key role in training, enhancing model accuracy. Overall, these results show how quantum algorithms can already tackle relevant HEP problems on current hardware, while paving the way for future fault-tolerant applications that fully exploit quantum computational advantages.

The Wigner function is a well-known phase space distribution function with many applications in quantum mechanics. In this article, we consider an open quantum system consisting of a non-relativistic single particle interacting with a general, possibly relativistic environment. For this system, we derive an expression for directly computing the time-dependent Wigner function from its initial values. This result renders time-dependent Wigner functions more applicable without having to make additional approximations that would otherwise be required in order to solve the corresponding equation of motion. As an illustration of our findings, we discuss the example of a non-relativistic single scalar particle interacting via a Yukawa interaction with an environment comprising another type of scalar field that is treated relativistically.

We prove an existence and uniqueness theorem for fixed points of contraction maps in the framework of quantum metric spaces, where distinguishability is defined by the $L^2$ norm: $d_Q(\psi_1,\psi_2) = \|\psi_1 - \psi_2\|$. The result applies to normalized real-valued Gaussian wavefunctions under continuous contractive evolution preserving the functional form. In contrast, while fuzzy metric spaces admit analogous fixed point theorems, they lack interference, phase sensitivity, and topological protection. This comparison reveals a deeper structural coherence in the quantum framework – not merely technical superiority, but compatibility with the geometric richness of Hilbert space. Our work extends the critique of fuzzy logic into dynamical reasoning under intrinsic uncertainty.

Despite rapid advances in molecular and materials machine learning, most models still lack physical transferability: they fit correlations across whole molecules or crystals rather than learning the quantum interactions between atomic pairs. Yet bonding, charge redistribution, orbital hybridization, and electronic coupling all emerge from these two-body interactions that define local quantum fields in many-body systems. We introduce QuantumCanvas, a large-scale multimodal benchmark that treats two-body quantum systems as foundational units of matter. The dataset spans 2,850 element-element pairs, each annotated with 18 electronic, thermodynamic, and geometric properties and paired with ten-channel image representations derived from l- and m-resolved orbital densities, angular field transforms, co-occupancy maps, and charge-density projections. These physically grounded images encode spatial, angular, and electrostatic symmetries without explicit coordinates, providing an interpretable visual modality for quantum learning. Benchmarking eight architectures across 18 targets, we report mean absolute errors of 0.201 eV on energy gap using GATv2, 0.265 eV on HOMO and 0.274 eV on LUMO using EGNN. For energy-related quantities, DimeNet attains 2.27 eV total-energy MAE and 0.132 eV repulsive-energy MAE, while a multimodal fusion model achieves a 2.15 eV Mermin free-energy MAE. Pretraining on QuantumCanvas further improves convergence stability and generalization when fine-tuned on larger datasets such as QM9, MD17, and CrysMTM. By unifying orbital physics with vision-based representation learning, QuantumCanvas provides a principled and interpretable basis for learning transferable quantum interactions through coupled visual and numerical modalities. Dataset and model implementations are available at https://github.com/KurbanIntelligenceLab/QuantumCanvas.

Data sets that are specified by a large number of features are currently outside the area of applicability for quantum machine learning algorithms. An immediate solution to this impasse is the application of dimensionality reduction methods before passing the data to the quantum algorithm. We investigate six conventional feature extraction algorithms and five autoencoder-based dimensionality reduction models to a particle physics data set with 67 features. The reduced representations generated by these models are then used to train a quantum support vector machine for solving a binary classification problem: whether a Higgs boson is produced in proton collisions at the LHC. We show that the autoencoder methods learn a better lower-dimensional representation of the data, with the method we design, the Sinkclass autoencoder, performing 40% better than the baseline. The methods developed here open up the applicability of quantum machine learning to a larger array of data sets. Moreover, we provide a recipe for effective dimensionality reduction in this context.

Quantum reinforcement learning (QRL) aims to use quantum effects to create sequential decision-making policies that achieve tasks more effectively than their classical counterparts. However, QRL policies face uncertainty from quantum measurements and hardware noise, such as bit-flip, phase-flip, and depolarizing errors, which can lead to unsafe behavior. Existing work offers no systematic way to verify whether trained QRL policies meet safety requirements under specific noise conditions. We introduce QVerifier, a formal verification method that applies probabilistic model checking to analyze trained QRL policies with and without modeled quantum noise. QVerifier builds a complete model of the policy-environment interaction, incorporates quantum uncertainty directly into the transition probabilities, and then checks safety properties using the Storm model checker. Experiments across multiple QRL environments show that QVerifier precisely measures how different noise models influence safety, revealing both performance degradation and cases where noise can help. By enabling rigorous safety verification before deployment, QVerifier addresses a critical need: because access to quantum hardware is expensive, pre-deployment verification is essential for any safety-critical use of QRL. QVerifier targets a potential sweet spot between classical and quantum computation, where trained QRL policies could still be modeled classically for probabilistic model checking. When the policy was trained under matching noise conditions, this formal model is exact; when trained on physical hardware, it constitutes an idealized approximation, as unknown hardware noise prevents exact policy modeling.

We investigate the emergence of nonreciprocal dissipationless supercurrents in one-dimension manifested through the superconducting diode effect (SDE) and the Josephson diode effect (JDE) in the absence of any external magnetic field, where inversion symmetry (IS) and time-reversal symmetry (TRS) can be intrinsically broken by spin-orbit coupling (SOC), and altermagnetism (AM), respectively. We investigate Ising and Rashba SOC separately in two models where two-component AM, assembled with crystallographic angle, can lead to qualitatively similar indirect band-gap closing and non-reciprocal supercurrent in a Fulde-Ferrell (FF) superconductor. Interestingly, in the absence of the above SOCs, SDE persists and the sign of efficiency can be altered by tuning the angle only. Parallel spin components with complementary momentum functions, ensuring the breaking of IS and TRS, can induce SDE in the presence of FF pairing. Continuing the analysis in the context of JDE, we explore the interplay between SOCs and AM with the p-wave and Fulde-Ferrell superconductivity in three different setups. The bulk bands contributes to the non-reciprocity in the case of p-wave superconductivity while JDE is dominated by Andreev bound states for FF superconductivity. Importantly, JDE continues to exist due to finite momentum Cooper pair even without AM and SOC unlike the p-wave superconductivity. The sign of JD efficiency can be tuned with angle for p-wave superconductivity while absence of such sign reversal is a hallmark signature of FF superconductivity. Similar to SDE, parallel spin components in conjunction with p-wave superconductivity can lead to JDE that can also be mediated by only FF pairing in the absence of SOC and AM.

Network traffic anomaly detection is a critical cybersecurity challenge requiring robust solutions for complex Internet of Things (IoT) environments. We present a novel hybrid quantum-classical framework integrating an enhanced Quantum Support Vector Machine (QSVM) with the Quantum Haar Wavelet Packet Transform (QWPT) for superior anomaly classification under realistic noisy intermediate-scale Quantum conditions. Our methodology employs amplitude-encoded quantum state preparation, multi-level QWPT feature extraction, and behavioral analysis via Shannon Entropy profiling and Chi-square testing. Features are classified using QSVM with fidelity-based quantum kernels optimized through hybrid training with simultaneous perturbation stochastic approximation (SPSA) optimizer. Evaluation under noiseless and depolarizing noise conditions demonstrates exceptional performance: 96.67% accuracy on BoT-IoT and 89.67% on IoT-23 datasets, surpassing quantum autoencoder approaches by over 7 percentage points.

We present a rigorous mathematical framework for verifying effective entanglement in a Coherent One-Way (COW) quantum key distribution setup. In particular, we introduce a two-parameter family of entanglement witnesses, identify the parameter ranges where they constitute valid witnesses, and demonstrate their ability to reveal effective entanglement in the COW protocol. Additionally, we analyze previously obtained experimental data from a COW implementation and report clear signatures of effective entanglement.

Bound states in the continuum (BICs) have attracted intense interest, yet their many-particle counterparts remain largely unexplored in waveguide quantum electrodynamics. We identify and characterize a bound state embedded in the doublon continuum (BIDC) that emerges when four atoms couple to a coupled-resonator waveguide with strong on-site interaction. Exploiting this interaction-enabled BIDC, we show that (i) a distant, four-atom entangled state can be prepared with high fidelity, and (ii) quantum entangled states can be coherently transferred between spatially separated nodes. Our results establish a scalable mechanism for multi-particle state generation and routing in waveguide platforms, opening a route to interaction-protected quantum communication with many-particle BICs.

We measure the ensemble transverse relaxation time (T2*) and spin-echo coherence time (T2) of the proton spin of HD molecules trapped in solid parahydrogen. By using high-purity parahydrogen matrices, we are able to measure significantly longer T2 and T2* times than seen in prior work. We also measure the longitudinal spin relaxation time T1. We examine how these parameters scale with the matrix purity and find limits on the coherence time from the parahydrogen matrix itself.

Photon detection is a cornerstone of quantum technology, traditionally regarded as a static device-level operation constrained by the intrinsic physical properties of single-photon detectors (SPDs). Consequently, high-performance detection has been heavily reliant on superconducting technologies, whose requirement for cryogenic temperatures imposes significant infrastructure burdens and limits scalable deployment. To circumvent these constraints, we propose the Enhanced Single-Photon Detection (ESPD) framework, which shifts the photon-detection paradigm from device-centric optimization to an integrated quantum-information-processing (QIP) task. By incorporating state preparation, controlled operations, projective measurements, and multi-copy decision analysis, we establish a nonlinear dynamical model that reformulates detection as an iteratively enhanced process. This architecture enables systematic performance upgrades through structural design rather than material modification, allowing high-performance detection with exclusively room-temperature hardware. Through analytical approximations, Monte Carlo analysis, and numerical simulations, we show that the ESPD dynamics converge to a high-performance basin of attraction even when initialized by low-performance SPDs. While physical realization requires further component integration efforts, this work establishes a rigorous theoretical foundation for enhancing detection via architectural QIP principles. It provides not only a blueprint for next-generation room-temperature photon detection but also a general methodology for transcending device-level constraints in broader quantum technologies.

Weighted graph states are a natural generalization of graph states, which are generated by applying controlled-phase gates, instead of controlled-Z gates, to a separable state. In this paper, we show that uniformly weighted graph states on a suitable planar graph constitute universal resources for measurement-based quantum computation for an arbitrary nonzero constant weight. To our knowledge, this is the first example of universal resources prepared with only non-maximally entangling gates and has potential applications to weakly interacting systems, such as photonic systems.

Measurement-induced entanglement (MIE) captures how local measurements generate long-range quantum correlations and drive dynamical phase transitions in many-body systems. Yet estimating MIE experimentally remains challenging: direct evaluation requires extensive post-selection over measurement outcomes, raising the question of whether MIE is accessible with only polynomial resources. We address this challenge by reframing MIE detection as a data-driven learning problem that assumes no prior knowledge of state preparation. Using measurement records alone, we train a neural network in a self-supervised manner to predict the uncertainty metric for MIE–the gap between upper and lower bounds of the average post-measurement bipartite entanglement. Applied to random circuits with one-dimensional all-to-all connectivity, our method reveals a learnability transition with increasing circuit depth: below a threshold the MIE can be effectively learned with resources that grow only polynomially with system size, whereas above it the required resources grow exponentially. This computational phase transition coincides with the breakdown of efficient classical simulation of the underlying quantum state. We further observe signatures of this transition on current noisy quantum devices. These results highlight the power of data-driven approaches for learning MIE and delineate the practical limits of its classical learnability.

Spectral form factor (SFF), one of the key quantity from random matrix theory, serves as an important tool to probe universality in disordered quantum systems and quantum chaos. In this work, we present exact closed-form expressions for the second- and third-order SFFs in the circular unitary ensemble (CUE), valid for all real values of the time parameter, and analyze their asymptotic behavior in different regimes. In particular, for the second-order SFF, we derive an exact closed-form expression in terms of polygamma functions. In the limit of infinite matrix size, and when the time parameter is restricted to integer values, the second-order SFF reproduces the standard result established in earlier studies. When the time parameter is of order one relative to the matrix size, we demonstrate that the second-order SFF grows logarithmically with the ensemble dimension. For the third-order SFFs, a closed-form result in a special case is obtained by exploiting the translational invariance of CUE.