We reveal strong and weak inequalities relating two fundamental macroscopic quantum geometric quantities, the quantum distance and Berry phase, for closed paths in the Hilbert space of wavefunctions. We recount the role of quantum geometry in various quantum problems and show that our findings place new bounds on important physical quantities.

We investigate the photon statistics of light emitted from a system of collectively interacting dipoles in the low-intensity regime, incorporating double-excitation states to capture beyond-single-excitation effects. By analyzing the eigenstates of the double-excitation manifold, we establish their connection to the accessible single-excitation eigenmodes and investigate the role of decay rates in shaping the initial-time photon correlation function $g^{(2)}(\tau = 0)$ under different detection schemes. By interfering two beams of light that selectively address orthogonal eigenmodes, the photon emission statistics can be arbitrarily controlled. This can act as a tunable nonlinearity that enables both the enhancement and suppression of photon correlations, extending the operational intensity range for single-photon applications.

Quantum mechanics involves two primary factors that dictate the limitations and potential for information encoding and decoding: coherence and overlap. The first factor imposes constraints on the amount of information that can be copied from one system to another, as governed by the no-cloning theorem, which forbids the perfect replication of unknown quantum states. The second limits the accessibility of information encoded in a physical system due to the indistinguishability of non-orthogonal quantum states. This work addresses these constraints and introduces a third factor: the limitations imposed by the laws of thermodynamics. We demonstrate that the preparation of the system for encoding and measuring information constrains the ability to distinguish among states of the resulting ensemble and influences the purity of these states. Furthermore, we explore a noisy communication channel and propose an optimal protocol for encoding and decoding the transmitted information. Based on this protocol, we highlight the thermodynamic interplay between heat and Holevo information, which quantifies the capacity of the transmitted information and establishes a fundamental limit on its retrieval in thermodynamically constrained scenarios.

Nonlocal correlations created in networks with multiple independent sources enable surprising phenomena in quantum information and quantum foundations. The presence of independent sources, however, makes the analysis of network nonlocality challenging, and even in the simplest nontrivial scenarios a complete characterization is lacking. In this work we study one of the simplest of these scenarios, namely that of distributions invariant under permutations of parties in the minimal triangle network, which features no inputs and binary outcomes. We perform an exhaustive search for triangle-local models, and from it we infer analytic expressions for the boundaries of the set of distributions that admit such models, which we conjecture to be all the tight Bell inequalities for the scenario. Armed with them and with improved outer approximations of the set, we provide new insights on the existence of a classical-quantum gap in the triangle network with binary outcomes.

In certain scenarios, quantum annealing can be made more efficient by additional $XX$ interactions. It has been shown that the additional interactions can reduce the scaling of perturbative crossings. In traditional annealing devices these couplings do not exist natively. In this work, we develop two gadgets to achieve this: a three-body gadget that requires a strong $ZZZ$ interaction; and a one-hot gadget that uses only local $X$ drives and two-body $ZZ$ interactions. The gadgets partition the Hilbert space to effectively generate a limited number of $XX$ interactions in the low-energy subspace. We numerically verify that the one-hot gadget can mitigate a perturbative crossing on a toy problem. These gadgets establish new pathways for implementing and exploiting $XX$ interactions, enabling faster and more robust quantum annealing.

Hybrid quantum-classical neural network methods represent an emerging approach to solving computational challenges by leveraging advantages from both paradigms. As physics-informed neural networks (PINNs) have successfully applied to solve partial differential equations (PDEs) by incorporating physical constraints into neural architectures, this work investigates whether quantum-classical physics-informed neural networks (QCPINNs) can efficiently solve PDEs with reduced parameter counts compared to classical approaches. We evaluate two quantum circuit paradigms: continuous-variable (CV) and qubit-based discrete-variable (DV) across multiple circuit ansatze (Alternate, Cascade, Cross mesh, and Layered). Benchmarking across five challenging PDEs (Helmholtz, Cavity, Wave, Klein-Gordon, and Convection-Diffusion equations) demonstrates that our hybrid approaches achieve comparable accuracy to classical PINNs while requiring up to 89% fewer trainable parameters. DV-based implementations, particularly those with angle encoding and cascade circuit configurations, exhibit better stability and convergence properties across all problem types. For the Convection-Diffusion equation, our angle-cascade QCPINN achieves parameter efficiency and a 37% reduction in relative L2 error compared to classical counterparts. Our findings highlight the potential of quantum-enhanced architectures for physics-informed learning, establishing parameter efficiency as a quantifiable quantum advantage while providing a foundation for future quantum-classical hybrid systems solving complex physical models.

The current state of Quantum computing (QC) is extremely optimistic, and we are at a point where researchers have produced highly sophisticated quantum algorithms to address far reaching problems. However, it is equally apparent that the noisy quantum environment is a larger threat than many may realize. The noisy intermediate scale quantum (NISQ) era can be viewed as an inflection point for the enterprise of QC where decoherence could stagnate progress if left unaddressed. One tactic for handling decoherence is to address the problem from a hardware level by implementing topological materials into the design. In this work, we model several Josephson junctions that are modified by the presence of topological superconducting nanowires in between the host superconductors. Our primary result is a numerical calculation of the energy-phase relationship for topological nanowire junctions which are a key parameter of interest for the dynamics of qubit circuits. In addition to this, we report on the qualitative physical behavior of the bound states as a function of superconducting phase. These results can be used to further develop and inform the construction of more complicated systems, and it is hopeful that these types of designs could manifest as a fault tolerant qubit.

In this work, we consider a three-level ladder-type atom driven by a coherent field, inspired by the experimental work of Gasparinetti et al. [Phys. Rev. A 100, 033802 (2019)]. When driven on two-photon resonance, the atom is excited into its highest energy state $| f \rangle$ by absorbing two photons simultaneously. The atom then de-excites via a cascaded decay $| f \rangle \rightarrow | e \rangle \rightarrow | g \rangle$. Here we present a theoretical study of the atomic fluorescence spectrum where, upon strong coherent driving, the spectrum exhibits seven distinct frequencies corresponding to transitions amongst the atomic dressed states. We characterize the quantum statistics of the emitted photons by investigating the second-order correlation functions of the emitted field. We do so by considering the total field emitted by the atom and focusing on each of the dressed-state components, taking in particular a secular-approximation and deriving straightforward, transparent analytic expressions for the second-order auto- and cross-correlations.

The growth of high-quality superconducting thin film on silicon substrates is essential for quantum computing, and low signal interconnects with industrial compatibility. Recently, the growth of $\alpha$-Ta (alpha-phase tantalum) thin films has gained attention over conventional superconductors like Nb and Al due to their high-density native oxide ($Ta_2O_5$), which offers excellent chemical resistance, superior dielectric properties, and mechanical robustness. The growth of $\alpha$-Ta thin films can be achieved through high-temperature/cryogenic growth, ultra-thin seed layers, or thick films (>300 nm). While high-temperature deposition produces high-quality films, it can cause thermal stress, silicide formation at the interface, and defects due to substrate-film mismatch. Room-temperature deposition minimizes these issues, benefiting heat-sensitive substrates and device fabrication. Low-temperature growth using amorphous (defective) seed layers such as TaN and TiN has shown promise for phase stabilization. However, nitrogen gas, used as a source of metallic nitride, can introduce defects and lead to the formation of amorphous seed layers. This study explores using crystalline seed layers to optimize $\alpha$-Ta thin films, demonstrating improved film quality, including reduced surface roughness, enhanced phase orientation, and higher transition temperatures compared to amorphous seed layers like metal nitrides. These advancements could interest the superconducting materials community for fabricating high-quality quantum devices.

Photonically-interconnected matter qubit systems have wide-ranging applications across quantum science and technology, with entanglement between distant qubits serving as a universal resource. While state-of-the-art heralded entanglement generation performance thus far has been achieved in trapped atomic systems modelled as stationary emitters, the improvements to fidelities and generation rates demanded by large-scale applications require taking into account their motional degrees of freedom. Here, we derive the effects of atomic motion on spontaneous emission coupled into arbitrary optical modes, and study the implications for commonly-used atom-atom entanglement protocols. We arrive at a coherent physical picture in the form of "kick operators" associated with each instant in the photonic wavepackets, which also suggests a method to mitigate motional errors by disentangling qubit and motion post-herald. This proposed correction technique removes overheads associated with the thermal motion of atoms, and may greatly increase entanglement rates in long-distance quantum network links by allowing single-photon-based protocols to be used in the high-fidelity regime.

In this paper, we investigate the dynamics of two two-level atoms interacting with a two-mode field inside an optical cavity, in presence of a nonlinear Kerr-like medium as well as the Stark shift. We derive the exact analytical solution of the time-dependent Schr\"odinger equation that provides a comprehensive framework for analyzing the system's quantum properties. To characterize the nonclassical features of the radiation field, we examine photon number distribution, second-order correlation function $g^2(0)$, squeezing properties, and Mandel's $Q_M$ parameter. These properties reveal significant insights into the quantum statistical behaviour of the field and its deviation from classicality under different interaction regimes. In addition, we quantify the atom-atom entanglement using linear entropy which captures the mixedness of the atomic subsystem and elucidates the interplay between atom-atom interactions. The results highlight the crucial role of nonlinear interactions and the Stark shift in shaping the quantum correlations and nonclassical phenomena of the system.

This paper introduces the indigenous Quantum Network Simulator developed to simulate various quantum network protocols on classical machines. The paper specifically focuses on the simulation of entanglement generation between two quantum memories using the Barrett-Kok protocol, as well as the teleportation of a single qubit utilizing the produced entangled states. Additionally, the impact of error manipulation and other factors on the fidelity of the teleported states is analyzed, revealing strategies for enhancing teleported state fidelity. The existing few quantum network simulators, allow the simulation of teleportation protocol, but they do not account for the experimental aspects of entanglement generation and distribution, which are crucial for achieving any practical implementation. This work aims to bridge this gap and provide a realistic fidelity of the teleported states.

In operational quantum mechanics two measurements are called operationally equivalent if they yield the same distribution of outcomes in every quantum state and hence are represented by the same operator. In this paper, I will show that the ontological models for quantum mechanics and, more generally, for any operational theory sensitively depend on which measurement we choose from the class of operationally equivalent measurements, or more precisely, which of the chosen measurements can be performed simultaneously. To this goal, I will take first three examples -- a classical theory, the EPR-Bell scenario and the Popescu-Rochlich box; then realize each example by two operationally equivalent but different operational theories -- one with a trivial and another with a non-trivial compatibility structure; and finally show that the ontological models for the different theories will be different with respect to their causal structure, contextuality, and fine-tuning.

Current algorithms for large-scale industrial optimization problems typically face a trade-off: they either require exponential time to reach optimal solutions, or employ problem-specific heuristics. To overcome these limitations, we introduce SPLIT, a general-purpose quantum-inspired framework for decomposing large-scale quadratic programs into smaller subproblems, which are then solved in parallel. SPLIT accounts for cross-interactions between subproblems, which are usually neglected in other decomposition techniques. The SPLIT framework can integrate generic subproblem solvers, ranging from standard branch-and-bound methods to quantum optimization algorithms. We demonstrate its effectiveness through comparisons with commercial solvers on the MaxCut and Antenna Placement Problems, with up to 20,000 decision variables. Our results show that SPLIT is capable of providing drastic reductions in computational time, while delivering high-quality solutions. In these regards, the proposed method is particularly suited for near real-time applications that require a solution within a strict time frame, or when the problem size exceeds the hardware limitations of dedicated devices, such as current quantum computers.

Quantum kernels quantify similarity between data points by measuring the inner product between quantum states, computed through quantum circuit measurements. By embedding data into quantum systems, quantum kernel feature maps, that may be classically intractable to compute, could efficiently exploit high-dimensional Hilbert spaces to capture complex patterns. However, designing effective quantum feature maps remains a major challenge. Many quantum kernels, such as the fidelity kernel, suffer from exponential concentration, leading to near-identity kernel matrices that fail to capture meaningful data correlations and lead to overfitting and poor generalization. In this paper, we propose a novel strategy for constructing quantum kernels that achieve good generalization performance, drawing inspiration from benign overfitting in classical machine learning. Our approach introduces the concept of local-global quantum kernels, which combine two complementary components: a local quantum kernel based on measurements of small subsystems and a global quantum kernel derived from full-system measurements. Through numerical experiments, we demonstrate that local-global quantum kernels exhibit benign overfitting, supporting the effectiveness of our approach in enhancing quantum kernel methods.

We demonstrate focal plane array beamforming for semi-blind deployments of free-space optical QKD links. We accomplish a secure-key rate of 1.2 kb/s at a QBER of 9.1% over a 63-m out-door link during full sunshine.

This study proposes a hybrid quantum-classical approach to solving the Capacitated Vehicle Routing Problem (CVRP) by integrating the Column Generation (CG) method with the Quantum Alternating Operator Ansatz (QAOAnsatz). The CG method divides the CVRP into the reduced master problem, which finds the best combination of the routes under the route set, and one or more subproblems, which generate the routes that would be beneficial to add to the route set. This method is iteratively refined by adding new routes identified via subproblems and continues until no improving route can be found. We leverage the QAOAnsatz to solve the subproblems. Our algorithm restricts the search space by designing the QAOAnsatz mixer Hamiltonian to enforce one-hot constraints. Moreover, to handle capacity constraints in QAOAnsatz, we employ an Augmented Lagrangian-inspired method that obviates the need for additional slack variables, reducing the required number of qubits. Experimental results on small-scale CVRP instances (up to 6 customers) show that QAOAnsatz converges more quickly to optimal routes than the standard QAOA approach, demonstrating the potential of this hybrid framework in tackling real-world logistical optimization problems on near-term quantum hardware.

We investigate the coherent single photon scattering process in a topological waveguide coupled with a driven $\Lambda$ system. We derive an analytical expression for transmittance by using the scattering formalism for three different sublattice sites (A, B, and AB), which couples to the $\Lambda$ system. We have demonstrated that the system's response is topology-independent for A and B sublattice-site coupling and becomes topology-dependent for AB sublattice-site coupling. In a weak control field regime, the system behaves as a perfect mirror in all of these configurations. Upon the control field strength enhancement, the transmission spectrum evolves from Electromagnetically Induced Transparency (EIT) to Autler-Townes splitting (ATS) in A and B sublattice-site coupling. The manipulation of transmission from opaque to transparent holds the key mechanism of a single photon switch. Further, the topology-dependent AB sublattice configuration allows the sharper Fano line shape that is absent in topology-independent A and B sublattice configurations. This characteristic of the Fano line can be used as a tunable single-photon switch and for sensing external perturbations. Furthermore, our study paves the way for the robustness and tunability of systems with applications in quantum technologies such as quantum switches, sensors, and communication devices.

We provide a generalization of the idea of unitary designs to cover finite averaging over much more general operations on quantum states. Namely, we construct finite averaging sets for averaging quantum states over arbitrary reductive Lie groups, on condition that the averaging is performed uniformly over the compact component of the group. Our construction comprises probabilistic mixtures of unitary 1-designs on specific operator subspaces. Provided construction is very general, competitive in the size of the averaging set when compared to other known constructions, and can be efficiently implemented in the quantum circuit model of computation.

We present an improved version of a quantum amplitude encoding scheme that encodes the $N$ entries of a unit classical vector $\vec{v}=(v_1,..,v_N)$ into the amplitudes of a quantum state. Our approach has a quadratic speed-up with respect to the original one. We also describe several generalizations, including to complex entries of the input vector and a parameter $M$ that determines the parallelization. The number of qubits required for the state preparation scales as $\mathcal{O}(M\log N)$. The runtime, which depends on the data density $\rho$ and on the parallelization paramater $M$, scales as $\mathcal{O}(\frac{1}{\sqrt{\rho}}\frac{N}{M}\log (M+1))$, which in the most parallel version ($M=N$) is always less than $\mathcal{O}(\sqrt{N}\log N)$. By analysing the data density, we prove that the average runtime is $\mathcal{O}(\log^{1.5} N)$ for uniformly random inputs. We present numerical evidence that this favourable runtime behaviour also holds for real-world data, such as radar satellite images. This is promising as it allows for an input-to-output advantage of the quantum Fourier transform.

Neutral-atom arrays have recently attracted attention as a versatile platform to implement coherent quantum annealing as an approach to hard problems, including combinatorial optimisation. Here we present an efficient scheme based on chains of Rydberg-blockaded atoms, which we call quantum wires, to embed certain weighted optimisation problems into a layout compatible with the neutral atom architecture. For graphs with quasi-unit-disk connectivity, in which only a few non-native long-range interactions are present, our approach requires a significantly lower overhead in the number of ancilla qubits than previous proposals, facilitating the implementation in currently available hardware. We perform simulations of realistic annealing ramps to find the solution of combinatorial optimisation problems within our scheme, demonstrating high success probability for problems of moderate size. Furthermore, we provide numerical evidence of a favourable scaling of the minimum gap along the annealing path with increasing wire length and of the robustness of the encoding to experimental imperfections. Our work expands the existing toolkit to explore the potential use of neutral atom arrays to solve large scale optimisation problems.

The bottom-up design of strongly interacting quantum materials with prescribed ground state properties is a highly nontrivial task, especially if only simple constituents with realistic two-body interactions are available on the microscopic level. Here we study two- and three-dimensional structures of two-level systems that interact via a simple blockade potential in the presence of a coherent coupling between the two states. For such strongly interacting quantum many-body systems, we introduce the concept of blockade graph automorphisms to construct symmetric blockade structures with strong quantum fluctuations that lead to equal-weight superpositions of tailored states. Drawing from these results, we design a quasi-two-dimensional periodic quantum system that - as we show rigorously - features a topological $\mathbb{Z}_2$ spin liquid as its ground state. Our construction is based on the implementation of a local symmetry on the microscopic level in a system with only two-body interactions.

By applying phase modulation across different frequencies, metasurfaces possess the ability to manipulate the temporal dimension of photons at the femtosecond scale. However, there remains a fundamental challenge to shape the single wavepacket at the nanosecond scale by using of metasurfaces. Here, we propose that the single photon temporal shape can be converted through the multi-photon wavepacket interference on a single metasurface. By selecting appropriate input single-photon temporal shapes and metasurfaces beam splitting ratio, controllable photon shape conversion can be achieved with high fidelity. For examples, photons with an exponentially decaying profile can be shaped into a Gaussian profile; by tuning the relative time delays of input photons, Gaussian-shaped photons can be transformed into exponentially decaying or rising profiles through the same metasurface. The proposed mechanism provides a compact way for solving the temporal shape mismatch issues in quantum networks, facilitating the realization of high-fidelity on-chip quantum information processing.

Neural quantum states (NQS) have emerged as a powerful tool for approximating quantum wavefunctions using deep learning. While these models achieve remarkable accuracy, understanding how they encode physical information remains an open challenge. In this work, we introduce adiabatic fine-tuning, a scheme that trains NQS across a phase diagram, leading to strongly correlated weight representations across different models. This correlation in weight space enables the detection of phase transitions in quantum systems by analyzing the trained network weights alone. We validate our approach on the transverse field Ising model and the J1-J2 Heisenberg model, demonstrating that phase transitions manifest as distinct structures in weight space. Our results establish a connection between physical phase transitions and the geometry of neural network parameters, opening new directions for the interpretability of machine learning models in physics.

By analyzing the physics of multi-photon absorption in superconducting nanowire single-photon detectors (SNSPDs), we identify physical components of jitter. From this, we formulate a quantitative physical model of the multi-photon detector response which combines local detection mechanism and local fluctuations (hotspot formation and intrinsic jitter) with thermoelectric dynamics of resistive domains. Our model provides an excellent description of the arrival-time histogram of a commercial SNSPD across several orders of magnitude, both in arrival-time probability and across mean photon number. This is achieved with just three fitting parameters: the scaling of the mean arrival time of voltage response pulses, as well as the Gaussian and exponential jitter components. Our findings have important implications for photon-number-resolving detector design, as well as applications requiring low jitter such as light detection and ranging (LIDAR).

We consider entangling operations in a single nitrogen-vacancy (NV) center in diamond where the hyperfine-coupled nuclear spin qubits are addressed with radio-frequency (rf) pulses conditioned on the state of the central electron spin. Limiting factors for the gate fidelity are coherent errors due to off-resonant driving of neighboring transitions in the dense, hyperfine-split energy spectrum of the defect and non-negligible perpendicular hyperfine tensor components that narrow the choice of $^{13}\rm C$ nuclear spin qubits. We address these issues by presenting protocols based on synchronization effects that allow for a complete suppression of both error sources in state-of-the-art CNOT gate schemes. This is possible by a suitable choice of parameter sets that incorporate the error into the scheme instead of avoiding it. These results contribute to the recent progress toward scalable quantum computation with defects in solids.

We derive a Markovian master equation for a linearly driven dissipative quantum harmonic oscillator, valid for generic driving beyond the adiabatic limit. We solve this quantum master equation for arbitrary Gaussian initial states and investigate its departure from the adiabatic master equation in the regime of fast driving. We concretely examine the behavior of dynamical variables, such as position and momentum, as well as of thermodynamic quantities, such as energy and entropy. We additionally study the influence of the nonequilibrium driving on the quantum coherence of the oscillator in the instantaneous energy eigenbasis. We further analyze the approach to the adiabatic limit and the relaxation to the instantaneous steady state as a function of the driving speed.

A defining feature of quantum many-body systems is the exponential scaling of the Hilbert space with the number of degrees of freedom. This exponential complexity na\"ively renders a complete state characterization, for instance via the complete set of bipartite Renyi entropies for all disjoint regions, a challenging task. Recently, a compact way of storing subregions' purities by encoding them as amplitudes of a fictitious quantum wave function, known as entanglement feature, was proposed. Notably, the entanglement feature can be a simple object even for highly entangled quantum states. However the complexity and practical usage of the entanglement feature for general quantum states has not been explored. In this work, we demonstrate that the entanglement feature can be efficiently learned using only a polynomial amount of samples in the number of degrees of freedom through the so-called tensor cross interpolation (TCI) algorithm, assuming it is expressible as a finite bond dimension MPS. We benchmark this learning process on Haar and random MPS states, confirming analytic expectations. Applying the TCI algorithm to quantum eigenstates of various one dimensional quantum systems, we identify cases where eigenstates have entanglement feature learnable with TCI. We conclude with possible applications of the learned entanglement feature, such as quantifying the distance between different entanglement patterns and finding the optimal one-dimensional ordering of physical indices in a given state, highlighting the potential utility of the proposed purity interpolation method.

We study the hypothesis testing problem of detecting the presence of a thermal source emitting coherent quantum states towards an arbitrary but fixed number $K$ of detectors versus the situation where the detectors are presented uncorrelated thermal noise of the same average energy in the setting of asymmetric hypothesis testing. We compare two variations of this theme: In the first one the detectors perform heterodyne or homodyne detection and then transmit their measured results to a central processing unit with unlimited computational resources. In the second one the detectors are able to teleport the quantum states to the central unit, which acts on the received quantum states with unlimited quantum computational resources. We find that when the average received energy per detector goes to zero, the ratio of the error exponents goes to infinity, indicating an infinite-fold quantum advantage.

Quantum error mitigation (EM) is a family of hybrid quantum-classical methods for eliminating or reducing the effect of noise and decoherence on quantum algorithms run on quantum hardware, without applying quantum error correction (EC). While EM has many benefits compared to EC, specifically that it requires no (or little) qubit overhead, this benefit comes with a painful price: EM seems to necessitate an overhead in quantum run time which grows as a (mild) exponent. Accordingly, recent results show that EM alone cannot enable exponential quantum advantages (QAs), for an average variant of the expectation value estimation problem. These works raised concerns regarding the role of EM in the road map towards QAs. We aim to demystify the discussion and provide a clear picture of the role of EM in achieving QAs, both in the near and long term. We first propose a clear distinction between finite QA and asymptotic QA, which is crucial to the understanding of the question, and present the notion of circuit volume boost, which we claim is an adequate way to quantify the benefits of EM. Using these notions, we can argue straightforwardly that EM is expected to have a significant role in achieving QAs. Specifically, that EM is likely to be the first error reduction method for useful finite QAs, before EC; that the first such QAs are expected to be achieved using EM in the very near future; and that EM is expected to maintain its important role in quantum computation even when EC will be routinely used - for as long as high-quality qubits remain a scarce resource.

For general bipartite mixed states, a sufficient and necessary mathematical condition for certifying entanglement and/or (Bell) non-locality remains unknown. In this paper, we examine this question for a broad and physically relevant class of bipartite systems, specifically those possessing an additive observable with a definite value. Such systems include, for example, final states of particle decays or bipartitions of spin chains with well-defined magnetization. We derive very simple, handy criteria for detecting entanglement or non-locality in many cases. For instance, if $\rho_{\left( m p\right)\left( nq\right)} \neq 0$, where the eigenstates $|np\rangle$ or $|mq\rangle$ do not correspond to the given definite value of the additive observable, then the state is necessarily entangled-this condition is very easy to check in practice. If, in addition, the partitioned Hilbert space has dimension 2xd, the condition becomes necessary. Furthermore, if the sectors associated with the eigenstates $|mp\rangle$ or $|nq\rangle$ are non-degenerate, there exists a CHSH inequality that is violated. We illustrate these results by analyzing the potential detection of entanglement and nonlocality in Higgs to ZZ decays at the LHC.

The N-representability problem consists in determining whether, for a given p-body matrix, there exists at least one N-body density matrix from which the p-body matrix can be obtained by contraction, that is, if the given matrix is a p-body reduced density matrix (p-RDM). The knowledge of all necessary and sufficient conditions for a p-body matrix to be N-representable allows the constrained minimization of a many-body Hamiltonian expectation value with respect to the p-body density matrix and, thus, the determination of its exact ground state. However, the number of constraints that complete the N-representability conditions grows exponentially with system size, and hence the procedure quickly becomes intractable in practice. This work introduces a hybrid quantum-stochastic algorithm to effectively replace the N-representability conditions. The algorithm consists of applying to an initial N-body density matrix a sequence of unitary evolution operators constructed from a stochastic process that successively approaches the reduced state of the density matrix on a p-body subsystem, represented by a p-RDM, to a target p-body matrix, potentially a p-RDM. The generators of the evolution operators follow the adaptive derivative-assembled pseudo-Trotter method (ADAPT), while the stochastic component is implemented using a simulated annealing process. The resulting algorithm is independent of any underlying Hamiltonian, and it can be used to decide if a given p-body matrix is N-representable, establishing a criterion to determine its quality and correcting it. We apply this hybrid ADAPT algorithm to alleged reduced density matrices from a quantum chemistry electronic Hamiltonian, the reduced BCS model with constant pairing, and the Heisenberg XXZ spin model. In all cases, the proposed method behaves as expected for 1-RDMs and 2-RDMs, evolving the initial matrices towards different targets.

Complex numbers play a crucial role in quantum mechanics. However, their necessity remains debated: whether they are fundamental or merely convenient. Recently, it was claimed that quantum mechanics based on real numbers can be experimentally falsified in the sense that any real-number formulation of quantum mechanics either becomes inconsistent with multipartite experiments or violates certain postulates. In this article we show that a physically motivated postulate about composite quantum systems allows to construct quantum mechanics based on real numbers that reproduces predictions for all multipartite quantum experiments. Thus, we argue that real-valued quantum mechanics cannot be falsified, and therefore the use of complex numbers is a matter of convenience.

With the growing interest in quantum machine learning, the perceptron -- a fundamental building block in traditional machine learning -- has emerged as a valuable model for exploring quantum advantages. Two quantum perceptron algorithms based on Grover's search, were developed in arXiv:1602.04799 to accelerate training and improve statistical efficiency in perceptron learning. This paper points out and corrects a mistake in the proof of Theorem 2 in arXiv:1602.04799. Specifically, we show that the probability of sampling from a normal distribution for a $D$-dimensional hyperplane that perfectly classifies the data scales as $\Omega(\gamma^{D})$ instead of $\Theta({\gamma})$, where $\gamma$ is the margin. We then revisit two well-established linear programming algorithms -- the ellipsoid method and the cutting plane random walk algorithm -- in the context of perceptron learning, and show how quantum search algorithms can be leveraged to enhance the overall complexity. Specifically, both algorithms gain a sub-linear speed-up $O(\sqrt{N})$ in the number of data points $N$ as a result of Grover's algorithm and an additional $O(D^{1.5})$ speed-up is possible for cutting plane random walk algorithm employing quantum walk search.

We study quantum algorithms based on quantum (sub)gradient estimation using noisy evaluation oracles, and demonstrate the first dimension independent query complexities (up to poly-logarithmic factors) for zeroth-order convex optimization in both smooth and nonsmooth settings. We match the first-order query complexities of classical gradient descent, using only noisy evaluation oracles, thereby exhibiting exponential separation between quantum and classical zeroth-order optimization. Specifically, for the smooth case, zeroth-order quantum gradient descent achieves $\widetilde{\mathcal{O}}(LR^2/\varepsilon)$ and $\widetilde{\mathcal{O}} \left( \kappa \log(1/\varepsilon \right))$ query complexities, for the convex and strongly convex case respectively; for the nonsmooth case, the zeroth-order quantum subgradient method attains a query complexity of $\widetilde{\mathcal{O}}((GR/\varepsilon)^2 )$. We then generalize these algorithms to work in non-Euclidean settings by using quantum (sub)gradient estimation to instantiate mirror descent, dual averaging and mirror prox. We demonstrate how our algorithm for nonsmooth optimization can be applied to solve an SDP involving $m$ constraints and $n \times n$ matrices to additive error $\varepsilon$ using $\widetilde{\mathcal{O}} ((mn^2+n^{\omega})(Rr/\varepsilon)^2)$ gates, where $\omega \in [2,2.38)$ is the exponent of matrix-multiplication time and $R$ and $r$ are bounds on the primal and dual optimal solutions, respectively. Specializing to linear programs, we obtain a quantum LP solver with complexity $ \widetilde{\mathcal{O}}((m+\sqrt{n}) (Rr/\varepsilon)^2).$ For zero-sum games we achieve the best quantum runtimes for any $\varepsilon > 0$ when $m = \mathcal{O}(\sqrt{n})$. We obtain the best algorithm overall (quantum or classical) whenever we further impose $\varepsilon=\Omega((m+n)^{-1/4})$.

We establish the necessary and sufficient conditions for unbiased estimation in multi-parameter estimation tasks. More specifically, we first consider quantum state estimation, where multiple parameters are encoded in a quantum state, and derive two equivalent necessary and sufficient conditions for an unbiased estimation: one formulated in terms of the quantum Fisher information matrix (QFIM) and the other based on the derivatives of the encoded state. Furthermore, we introduce a generalized quantum Cram\'er-Rao bound, which provides a fundamental achievable lower bound on the estimation error even when the QFIM is non-invertible. To demonstrate the utility of our framework, we consider phase estimation under unknown Pauli noise. We show that while unbiased phase estimation is infeasible with a naive scheme, employing an entangled probe with a noiseless ancilla enables unbiased estimation. Next, we extend our analysis to quantum channel estimation (equivalently, quantum channel learning), where the goal is to estimate parameters characterizing an unknown quantum channel. We establish the necessary and sufficient condition for unbiased estimation of these parameters. Notably, by interpreting unbiased estimation as learnability, our result applies to the fundamental learnability of parameters in general quantum channels. As a concrete application, we investigate the learnability of noise affecting non-Clifford gates via cycle benchmarking.

Most observables at particle colliders involve physics at a wide variety of distance scales. Due to asymptotic freedom of the strong interaction, the physics at short distances can be calculated reliably using perturbative techniques, while long distance physics is non-perturbative in nature. Factorization theorems separate the contributions from different scales scales, allowing to identify the pieces that can be determined perturbatively from those that require non-perturbative information, and if the non-perturbative pieces can be reliably determined, one can use experimental measurements to extract the short distance effects, sensitive to possible new physics. Without the ability to compute the non-perturbative ingredients from first principles one typically identifies observables for which the non-perturbative information is universal in the sense that it can be extracted from some experimental observables and then used to predict other observables. In this paper we argue that the future ability to use quantum computers to calculate non-perturbative matrix elements from first principles will allow to make predictions for observables with non-universal non-perturbative long-distance physics.

We study the temporal, driven-dissipative dynamics of open photon Bose-Einstein condensates (BEC) in a dye-filled microcavity, taking the condensate amplitude and the noncondensed fluctuations into account on the same footing by means of a cumulant expansion within the Lindblad formalism. The fluctuations fundamentally alter the dynamics in that the BEC always dephases to zero for sufficiently long time. However, a ghost-attractor, although it is outside of the physically accessible configuration space, attracts the dynamics and leads to a plateau-like stabilization of the BEC for an exponentially long time, consistent with experiments. We also show that the photon BEC and the lasing state are separated by a true phase transition, since they are characterized by different fixed points. The ghost-attractor nonequilibrium stabilization mechanism is alternative to prethermalization and may possibly be realized on other dynamical platforms as well.

Neutral-atom arrays are a leading platform for quantum technologies, offering a promising route toward large-scale, fault-tolerant quantum computing. We propose a novel quantum processing architecture based on dual-type, dual-element atom arrays, where individually trapped atoms serve as data qubits, and small atomic ensembles enable ancillary operations. By leveraging the selective initialization, coherent control, and collective optical response of atomic ensembles, we demonstrate ensemble-assisted quantum operations that enable reconfigurable, high-speed control of individual data qubits and rapid mid-circuit readout, including both projective single-qubit and joint multi-qubit measurements. The hybrid approach of this architecture combines the long coherence times of single-atom qubits with the enhanced controllability of atomic ensembles, achieving high-fidelity state manipulation and detection with minimal crosstalk. Numerical simulations indicate that our scheme supports individually addressable single- and multi-qubit operations with fidelities of 99.5% and 99.9%, respectively, as well as fast single- and multi-qubit state readout with fidelities exceeding 99% within tens of microseconds. These capabilities open new pathways toward scalable, fault-tolerant quantum computation, enabling repetitive error syndrome detection and efficient generation of long-range entangled many-body states, thereby expanding the quantum information toolbox beyond existing platforms.

The geometric properties of quantum states are crucial for understanding many physical phenomena in quantum mechanics, condensed matter physics, and optics. The central object describing these properties is the quantum geometric tensor, which unifies the Berry curvature and the quantum metric. In this work, we use the differential-geometric framework of vector bundles to analyze the properties of parameter-dependent quantum states and generalize the quantum geometric tensor to this setting. This construction is based on an arbitrary connection on a Hermitian vector bundle, which defines a notion of quantum state transport in parameter space, and a sub-bundle projector, which constrains the set of accessible quantum states. We show that the sub-bundle geometry is similar to that of submanifolds in Riemannian geometry and is described by a generalization of the Gauss-Codazzi-Mainardi equations. This leads to a novel definition of the quantum geometric tensor, which contains an additional curvature contribution. To illustrate our results, we describe the sub-bundle geometry arising in the semiclassical treatment of Dirac fields propagating in curved spacetime and show how the quantum geometric tensor, with its additional curvature contributions, is obtained in this case. As a concrete example, we consider Dirac fermions confined to a hyperbolic plane and demonstrate how spatial curvature influences the quantum geometry. This work sets the stage for further exploration of quantum systems in curved geometries, with applications in both high-energy physics and condensed matter systems.

When faced with overwhelming evidence supporting the reality of time dilation, confirmed in particular by the Hafele-Keating experiment, some anti-relativists reluctantly concede that time dilation applies to light clocks. However, they argue that the Theory of Relativity remains flawed, claiming that time dilation applies to light clocks only, not to massive objects. They assert that atomic clocks, which operate based on microwave radiation, merely create the illusion that the Hafele-Keating experiment confirms the theory. To refute this misconception, we introduce a thought experiment inspired by Schrodinger's cat, in which the fate of Einstein's cat depends on a "Sync-or-Die clock", an imaginary device that tests the synchronization between a light clock and a mechanical clock, potentially triggering the release of poison. By analyzing this scenario from both the inertial frame where the device is at rest and another in which it moves at constant velocity, we demonstrate that time dilation must apply to the mechanical clock in exactly the same way as it does to the light clock, highlighting the universality of relativistic time dilation.

We demonstrate the emergence and control of Floquet states and topological bound states in the continuum (TBICs) in a two-dimensional colored quantum random walk (cQRW) on a square lattice. By introducing three internal degrees of freedom-termed "colors"-and leveraging SU(3) group representations, we realize dispersive TBICs and intrinsic Floquet dynamics without the need for external periodic driving. Through Chern number calculations, we identify three distinct topological bands, revealing color-induced band mixing as a key mechanism underlying the natural formation of Floquet states. The cQRW framework enables precise tuning of quasi-energy spectra, supporting the emergence of localized edge states in topological band gaps and dispersive TBICs embedded within the bulk of other bands. These TBICs exhibit tunable group velocity, controllable excitation across energy regimes, and robustness, providing theoretical validation for their existence in a first-order Floquet system. Our findings position cQRWs as a powerful platform for investigating and harnessing TBICs and Floquet states, with potential applications in quantum information and communication technologies.

The presence of quantum effects in photosynthetic excitation energy transfer has been intensely debated over the past decade. Nonlinear spectroscopy cannot unambiguously distinguish coherent electronic dynamics from underdamped vibrational motion, and rigorous numerical simulations of realistic microscopic models have been intractable. Experimental studies supported by approximate numerical treatments that severely coarse-grain the vibrational environment have claimed the absence of long-lived quantum effects. Here, we report the first non-perturbative, accurate microscopic model simulations of the Fenna-Matthews-Olson photosynthetic complex and demonstrate the presence of long-lived excitonic coherences at 77 K and room temperature, which persist on picosecond time scales, similar to those of excitation energy transfer. Furthermore, we show that full microscopic simulations of nonlinear optical spectra are essential for identifying experimental evidence of quantum effects in photosynthesis, as approximate theoretical methods can misinterpret experimental data and potentially overlook quantum phenomena.