Papers

Understanding the theoretical capabilities and limitations of quantum machine learning (QML) models to solve machine learning tasks is crucial to advancing both quantum software and hardware developments. Similarly to the classical setting, the performance of QML models can be significantly affected by the limited access to the underlying data set. Previous studies have focused on proving generalization error bounds for any QML models trained on a limited finite training set. We focus on the optimal QML models obtained by training them on a finite training set and establish a tight prediction error bound in terms of the number of trainable gates and the size of training sets. To achieve this, we derive covering number upper bounds and packing number lower bounds for the data re-uploading QML models and linear QML models, respectively, which may be of independent interest. We support our theoretical findings by numerically simulating the QML strategies for function approximation and quantum phase recognition.

We study a phase-tunable coherent device that reproduces the matrix-valued braid statistics associated with non-Abelian anyons without assuming pre-existing anyonic quasiparticles. The construction uses the reduced Burau representation, made unitary by Squier's Hermitian form, to implement two noncommuting braid generators on a two-dimensional encoded space. A controlled-order circuit applies the generators in both orders, and optimal single-use discrimination gives a closed-form witness for their order dependence. Together with the braid relation, this certifies the implemented non-Abelian braid action. It does not by itself demonstrate the existence of anyonic quasiparticles.

In this paper, we present a Monte-Carlo Compressive Optimization algorithm, a new method for black-box combinatorial optimization, which only assumes that the objective is compressible. The method relies on random queries to the objective function in order to estimate generalized moments. Next, a greedy algorithm from compressive sensing is repurposed to find the global optimum when not overfitting to the samples. We provide numerical results giving evidence that our method is competitive by comparing it with digital annealing. Moreover, we give theoretical justification for the success of the algorithm and analyze its properties. The practicality of our algorithm is enhanced by the ability to tune the heuristic parameters to the available computational resources. An end-to-end open-source implementation is available to use our method.

We demonstrate that non-chiral nanoparticles can produce chiral light when point emitters are coupled to their surface plasmon modes (SPMs) under certain conditions. Chiral emission arises from asymmetrical plasmon mode propagation from the source combined with the spin-momentum locked nature of the SPMs. The Purcell regime of cavity quantum electrodynamics (QED) ensures that radiation from the coupled mode dominates over that from the emitter itself, giving rise to photons with a circularly polarized component – i.e. chiral light. We experimentally demonstrate this effect using electron beam-induced cathode luminescence from a gold nanorod, coupling it evanescently to a nanofiber probe which also supports spin-momentum locked light. This converts the net spin of the emission into a net directionality of propagation in the fiber modes.

The finite element method (FEM) is a cornerstone numerical technique for solving partial differential equations (PDEs). Here, we present $\textbf{Qu-FEM}$, a fault-tolerant era quantum algorithm for the finite element method. In contrast to other quantum PDE solvers, Qu-FEM preserves the geometric flexibility of FEM by introducing two new primitives, the unit of interaction and the local-to-global indicator matrix, which enable the assembly of global finite element arrays with a constant-size linear combination of unitaries. We study the modified Poisson equation as an elliptic problem of interest, and provide explicit circuits for Qu-FEM in Cartesian domains. For problems with constant coefficients, our algorithm admits block-encodings of global arrays that require only $\tilde{\mathcal{O}}\left(d^2 p^2 n\right)$ Clifford+$T$ gates for $d$-dimensional, order-$p$ tensor product elements on grids with $2^n$ degrees of freedom in each dimension, where $n$ is the number of qubits representing the $N=2^n$ discrete grid points. For problems with spatially varying coefficients, we perform numerical integration directly on the quantum computer to assemble global arrays and force vectors. Dirichlet boundary conditions are enforced via the method of Lagrange multipliers, eliminating the need to modify the block-encodings that emerge from the assembly procedure. This work presents a framework for extending the geometric flexibility of quantum PDE solvers while preserving the possibility of a quantum advantage.

We propose a quantum-assisted framework for solving constrained finite-horizon nonlinear optimal control problems using a barrier Sequential Quadratic Programming (SQP) approach. Within this framework, a quantum subroutine is incorporated to efficiently solve the Schur complement step using block-encoding and Quantum Singular Value Transformation (QSVT) techniques. We formally analyze the time complexity and convergence behavior under the cumulative effect of quantum errors, establishing local input-to-state stability and convergence to a neighborhood of the stationary point, with explicit error bounds in terms of the barrier parameter and quantum solver accuracy. The proposed framework enables computational complexity to scale polylogarithmically with the system dimension demonstrating the potential of quantum algorithms to enhance classical optimization routines in nonlinear control applications.

This article proposes a reading of quantum metaphysical indeterminacy from the perspective of Parsons' Nuclear Meinongianism. In doing so, we identify a fundamental incompatibility between a key feature of Parsons' theory and standard quantum mechanics. Our approach interprets quantum indeterminacy as property incompleteness. However, this move, when combined with Parsons' framework, leads to what we term the “Incompleteness-Entails-Nonexistence Principle” (IENP), which implausibly entails the nonexistence of quantum objects. For Meinongianism to be a suitable tool for the metaphysics of quantum mechanics, this principle must be addressed. We argue for dropping the IENP and discuss the resulting metaphysical and metametaphysical consequences.

Recent advances in quantum communication have enabled long-distance secure information transfer through quantum channels, giving rise to quantum networks with unique physical and statistical properties. However, as in classical networks, the propagation of viruses in these systems could have severe consequences. Here, we investigate the critical problem of virus spreading in quantum networks. We develop quantitative tools, particularly a modified nonlinear dynamical system model, for performing epidemiological analyses on quantum networks. Our results show that quantum networks tend to be more resilient to viral infections, exhibiting higher epidemic thresholds than classical networks with identical graph topologies. This apparent robustness, however, arises primarily from the sparser connectivity inherent to the quantum networks. When the comparison is made at a fixed average connectivity, classical and quantum networks display comparable epidemic thresholds. These findings provide key insights into the security and reliability of future large-scale quantum communication systems. Our work bridges the fields of quantum information science, network theory, and epidemiology, paving the way for future studies of quantum epidemiological dynamics.

We present high-precision quantum computing simulations of three-body atoms (He, H$^-$) and molecules (H$_2^+$, HD$^+$), the latter being studied beyond the Born-Oppenheimer approximation. The Non-Iterative Disentangled Unitary Coupled Cluster Variational Quantum Eigensolver (NI-DUCC-VQE) [M. Haidar et al., Quantum Sci. Technol. 10, 025031 (2025)] is used. By combining a first-quantized Hamiltonian with a Minimal Complete Pool (MCP) of Lie-algebraic excitations, we construct a compact ansatz with a gradient-independent construction, avoiding costly gradient evaluations and yielding efficient computational scaling with both basis size and electron number. It avoids barren plateaus and enables rapid convergence, achieving energy errors as low as 10$^{-11}$ a.u. with state fidelities only limited by arithmetic precision in only a few thousand function evaluations in all four systems. These results make three-body atoms and molecules excellent candidates for benchmarking and testing on current Noisy Intermediate-Scale Quantum (NISQ) devices. Further, our approach can be extended to more complex systems with larger basis sets, taking advantage of the efficient scaling of qubit requirements to study electronic correlations and non-adiabatic effects with high precision. We also demonstrate the applicability of NI-DUCC-VQE for simulating higher-order effects such as relativistic corrections and hyperfine interactions.

Compilation and optimization of quantum circuits are critical components in the execution of algorithms on quantum computers. These components must successfully balance two competing priorities: minimizing the number of expensive resources, such as two-qubit gates or arbitrary angle single-qubit rotations, and minimizing the approximation error of the compiled circuit to the ideal target unitary describing the quantum algorithm. We develop a practical workflow for managing and optimizing this tradeoff, which enables quantum circuit compilation and optimization at scales of hundreds of qubits. Our workflow is able to tackle circuits at such large scales while providing rigorous guarantees on circuit output error by leveraging circuit partitioning and the notion of averaging over circuit ensembles. We demonstrate our workflow on several benchmark algorithmic circuits acting on up to 380 qubits, and show that it can simultaneously achieve substantial reductions in resource-intensive gates and control output errors, offering a practical and scalable strategy for both near-term and fault-tolerant quantum computing.

Classical deep neural networks can learn rich multi-particle correlations in collider data, but their inductive biases are rarely anchored in physics structure. We propose quantum-informed neural networks (QINNs), a general framework that brings quantum information concepts and quantum observables into purely classical models. While the framework is broad, in this paper, we study one concrete realisation that encodes each particle as a qubit and uses the Quantum Fisher Information Matrix (QFIM) as a compact, basis-independent summary of particle correlations. Using jet tagging as a case study, QFIMs act as lightweight embeddings in graph neural networks, increasing model expressivity and plasticity. The QFIM reveals distinct patterns for QCD and hadronic top jets that align with physical expectations. Thus, QINNs offer a practical, interpretable, and scalable route to quantum-informed analyses, that is, tomography, of particle collisions, particularly by enhancing well-established deep learning approaches.

Persistent current is a hallmark of quantum phase coherence. We study the fate of the persistent current in a non-equilibrium setting, where a tight-binding ring is subjected to stochastic disorder as well as a fermionic reservoir attached to each site. We evaluate the current using Keldysh technique and find that it exhibits non-monotonic behavior, suggesting two distinct mechanisms of decoherence. While coupling to the reservoirs introduces a coherence length scale given by the inverse of the coupling strength, the other mechanism is more subtle and driven by the ratio of noise strength to reservoir coupling. The interplay of noise and reservoir constitutes a purely non-equilibrium steady state with a flatter distribution function that we effectively describe using classical rate equations. We discuss possibilities of realizing our findings in ultracold-atom experiments.

Quantum computing promises exponential improvements in solving large systems of partial differential equations (PDE), which forms a bottleneck in high-resolution simulations of, among others, computational fluid dynamics (CFD) in aerospace applications and weather forecasting. One approach is via mapping classical PDE problems to a quantum Hamiltonian evolution, for which recently an explicit quantum circuit construction has been shown in simple cases, allowing proof-of-concept execution on quantum processors. Here we extended this method to more complex and practically relevant cases. We first demonstrate how arbitrary complex-shaped geometrical obstacles with Dirichlet, Neumann or mixed boundary conditions can be introduced in the quantum representations of elementary difference operators used to implement the PDE, either directly or using Linear Combination of Unitaries (LCU). We provide their explicit and efficient circuit constructions, and analyze the Trotter errors and asymptotic gate complexities, which in the Dirichlet case do not grow compared to the free space equation. Using these methods we then derive quantum circuits for the linearized Euler equations in the presence of a background fluid flow and obstacles, both in the conservative and non-conservative regimes. We illustrate our results by simulating the obtained quantum circuits for different boundary conditions and geometries, comparing their error to a classical finite difference scheme.

While the last few decades have seen a proliferation of experimental demonstrations of entanglement sources, practicality of deployment has been a secondary concern. Recently, the ZALM source was introduced, as a well-engineered functional device, easily integrated within a complete networking system. It addresses numerous concerns which make typical academic demonstrations less practical: reliable heralding signals, multiplexing across multiple dimensions, and efficient use of input power. We present a stack of tools for modeling mode-by-mode a ZALM source under realistic conditions, in isolation or as a part of a complete network testbed. Our modeling formalism builds upon a hybrid Gaussian and non-Gaussian representation, providing a flexible tradeoff between performance and accuracy, while also greatly simplifying the exact calculation of otherwise expensive scalar figures of merit. This toolkit, implemented in the Python package called "genqo", is integrated within the QuantumSavory full-stack simulator and the QuantumSymbolics computer algebra system. We use this software stack to demonstrate a number of complete networking protocols built upon the ZALM source.

$W$ states are a central class of multipartite entangled states with applications in quantum information processing, yet their scalable and deterministic preparation remains challenging. Here we propose a protocol based on topological ring frustration, where an antiferromagnetic ring with an odd number of sites hosts a delocalized excitation corresponding to a $W$ state. We implement this protocol on a Rydberg atom array – a programmable quantum simulator – generating $W$ states of up to 11 atoms. Our results demonstrate a fidelity of $\mathcal{F} \approx 0.77$, and numerical simulations indicate scalability to larger system sizes accessible with near-term hardware improvements. To enable certification of these many-body entangled states, we introduce a novel and efficient Bayesian tomography method that, leveraging on classical simulations, enables their certification with a cost that avoids the exponential scaling of full tomography. These results establish topological frustration as a practical mechanism for engineering multipartite entanglement and provide a scalable route toward the certification of correlated quantum many-body states in quantum simulators.

A quantum message is encoded into $N$ storage nodes (quantum systems $Q_1\dots Q_N$) with assistance from $N_B$ maximally entangled bi-partite quantum systems $A_1B_1, \dots, A_{N_B}B_{N_B}$, that are prepared in advance such that $B_1\dots B_{N_B}$ are stored separately as entanglement assistance (EA) nodes, while $A_1\dots A_{N_B}$ are made available to the encoder. Both the storage nodes and EA nodes are erasure-prone. The quantum message must be recoverable given any $K$ of the $N$ storage nodes along with any $K_B$ of the $N_B$ EA nodes. The capacity for this setting is the maximum size of the quantum message, given that the size of each EA node is $\lambda_B$. All node sizes are relative to the size of a storage node, which is normalized to unity. The exact capacity is characterized as a function of $N,K,N_B,K_B, \lambda_B$ in all cases, with one exception. The capacity remains open for an intermediate range of $\lambda_B$ values when a strict majority of the $N$ storage nodes, and a strict non-zero minority of the $N_B$ EA nodes, are erased. As a key stepping stone, an analogous classical storage (with shared-randomness assistance) problem is introduced. A set of constraints is identified for the classical problem, such that classical linear code constructions translate to quantum storage codes, and the converse bounds for the two settings utilize similar insights. In particular, the capacity characterizations for the classical and quantum settings are shown to be identical in all cases where the capacity is settled.

Systematic pulse-area errors limit the fidelity of quantum control across many qubit platforms. We introduce twinned dynamical decoupling (TDD), an analytic family of sequences $T2n$ in which a pulse sequence is paired with its $\pi$-phase-shifted twin. This $\pi$-phase step cancels common-mode systematic pulse-area errors to all orders on exact resonance. Then the phases of the pulses in each of the constituent twins are determined in such a manner that detuning errors are suppressed to the highest possible order as well. We have derived a simple analytic formula for these phases applicable to arbitrary sequence length. We demonstrate the sequences with superconducting transmon qubits on the IBM Quantum processor ibm$\_$torino and the IQM Quantum processor Garnet. The measured population plateaus agree closely with theory and show enhanced robustness compared to the most frequently used dynamical decoupling protocols.

As quantum computers scale toward millions of physical qubits, it becomes essential to robustly encode individual logical qubits to ensure fault tolerance under realistic noise. A high-quality foundational encoding allows future compilation techniques and heuristics to build on optimal or near-optimal layouts, improving scalability and error resilience. In this work, we synthesize a one-dimensional shuttling bus architecture for the rotated surface code, leveraging coherent spin-qubit shuttling, following a novel methodology we name Quantum Reverse Mapping. We formulate a mixed-integer optimization model that yields optimal solutions with relatively low execution time for small code distances, and propose a scalable heuristic that matches optimal results while maintaining linear computational complexity. We evaluate the synthesized architecture using architectural metrics, such as shuttling distance and cycle time, and full quantum simulations under realistic noise models, showing that the proposed design can sustain logical error rates as low as $2\times 10^{-10}$ per round at code distance 21, showcasing its feasibility for scalable quantum error correction in spin-based quantum processors.

Quantum key distribution (QKD) is a cryptographic technique that uses quantum mechanical principles to enable secure key exchange. Practical deployment of QKD requires robust, cost-effective systems that can operate in challenging field environments. A major challenge is achieving reliable clock synchronization without adding hardware complexity. Conventional approaches often use separate classical light signals, which increase costs and introduce noise that degrades quantum channel performance. To address this limitation, we demonstrate a QKD system incorporating a recently proposed qubit-based distributed frame synchronization method, deployed over a metropolitan fiber network in Nanning, China. Using the polarization-encoded one-decoy-state BB84 protocol and the recently proposed qubit-based distributed frame synchronization method, our system achieves synchronization directly from the quantum signal, eliminating the need for dedicated synchronization hardware. Furthermore, to counteract dynamic polarization disturbances in urban fibers, the system integrates qubit-based polarization feedback control, enabling real-time polarization compensation through an automated polarization controller using data recovered from the qubit-based synchronization signals. During 12 hours of continuous operation, the system maintained a low average quantum bit error rate (QBER) of 1.12/%, achieving a secure key rate of 26.6 kbit/s under 18 dB channel loss. Even under a high channel loss of 40 dB, a finite-key secure rate of 115 bit/s was achieved. This study represents the first successful long-term validation of a frame-synchronization based QKD scheme in a real urban environment, demonstrating exceptional stability and high-loss tolerance, and offering an alternative for building practical, scalable, and cost-efficient quantum-secure communication networks.

Federated learning (FL) focuses on collaborative model training without the need to move the private data silos to a central server. Despite its several benefits, the classical FL is plagued with several limitations, such as high computational power required for model training(which is critical for low-resource clients), privacy risks, large update traffic, and non-IID heterogeneity. This chapter surveys a hybrid paradigm - Quantum Federated Learning (QFL), which introduces quantum computation, that addresses multiple challenges of classical FL and offers rapid computing capability while keeping the classical orchestration intact. Firstly, we motivate QFL with a concrete presentation on pain points of classical FL, followed by a discussion on a general architecture of QFL frameworks specifying the roles of client and server, communication primitives and the quantum model placement. We classify the existing QFL systems based on four criteria - quantum architecture (pure QFL, hybrid QFL), data processing method (quantum data encoding, quantum feature mapping, and quantum feature selection & dimensionality reduction), network topology (centralized, hierarchial, decentralized), and quantum security mechanisms (quantum key distribution, quantum homomorphic encryption, quantum differential privacy, blind quantum computing). We then describe applications of QFL in healthcare, vehicular networks, wireless networks, and network security, clearly highlighting where QFL improves communication efficiency, security, and performance compared to classical FL. We close with multiple challenges and future works in QFL, including extension of QFL beyond classification tasks, adversarial attacks, realistic hardware deployment, quantum communication protocols deployment, aggregation of different quantum models, and quantum split learning as an alternative to QFL.

Macroscopic quantum amplifiers maintain coherence even while strongly coupled to their surroundings, demonstrating that coherence can be preserved through architecture rather than isolation. Here we derive a finite structured-bath Hamiltonian in which dissipation and feedback originate from the same microscopic couplings. The resulting self-energy {\Sigma}({\omega}) exhibits coupled real and imaginary parts whose evolution reproduces the breathing dynamics observed in Josephson quantum amplifiers. This establishes quantum reciprocity: macroscopic coherence lives not in isolation, but in structured connection. We numerically validate this principle by engineering a six-qubit structured bath to demonstrate controllable transitions from dissipation to amplification. This architectural core serves as the foundation for a proposed multi-scale workflow to transform quantum noise into a design resource, preserving coherence not through isolation but through architectural reciprocity.

This paper proposes an intrinsic or background-independent quantum framework based on entangled state rather than absolute quantum state, it describes a quantum relative state between the under-study quantum system and the quantum measuring apparatus as a quantum reference system, without relying on any external absolute parameter. The paper focuses on a simple example, in which a quantum object's one-dimensional position as an under-study quantum system, and a quantum clock as a quantum reference system or quantum measuring apparatus. The evolution equation of the state of the quantum object's position with respect to the state of the quantum clock is given coming from the Ricci-flat Kaehler-Einstein equation. In a linear and non-relativistic approximation, the framework recovers the equation of the standard quantum mechanics, in which an intrinsic potential related to some "inertial force" is automatically incorporated in the covariant derivative. A physical relative probability interpretation and a geometric non-trivial fiber bundle interpretation of the entangled state in this intrinsic quantum framework are given. Furthermore, some non-inertial effects, such as the "inertial force", coming from the general covariance of the intrinsic quantum framework are also discussed. Compared with the functional integral approach which is more easily to generalize the quantum clock to the quantum spacetime reference frame and study quantum gravity, the relative state approach as a canonical description is more suitable for conceptually demonstrating the connections to the standard formalism and interpretation of the quantum mechanics.

The optimization of neural wave functions in variational Monte Carlo crucially relies on a robust convergence criterion. While the energy variance is theoretically a definitive measure, its practical application as a primary convergence criterion has been underexplored. In this work, we develop a lightweight, general-purpose solver that utilizes the energy variance as a convergence criterion. We apply it to several systems-including the harmonic oscillator, hydrogen atom, and charmonium hadron-for validating the variance as a reliable diagnostic, and using a empirical threshold $10^{-3}$ as the energy variance convergence values for performing rapid parameter scans to enable preliminary physical verification. To clarify the scope of our approach, we derive an inequality that delineates the limitations of variance-based optimization in nodal systems. Despite these limitations, the energy variance proves to be a highly valuable tool, guiding our solver to efficient and reliable results across a range of quantum problems.

This paper develops an agent-centric account of measurement that treats the preferred-basis problem is fundamentally perspectival. On this view, the system–apparatus–environment decomposition and the observables that are apt to become classically robust are determined by the physical constitution and epistemic constraints of an embodied class of agents. Decoherence then stabilises those agent-specified observables, yielding facts that are stable for us without positing an absolute, observer-independent basis. On this picture, `measurements' are public not because they are metaphysically privileged, but because agents like us share the relevant sensorimotor and operational structure. I motivate this account through a discussion of two recent no-go results for relational quantum mechanics (RQM) (Brukner,2021;Pienaar,2021), and a subsequent response (DiBiagio and Rovelli, 2022): my aim is not to defend RQM per se, but to refine the relational insight with a principled account of basis selection rooted in embodiment. I provide a phenomenological gloss, drawing on body-schema considerations, to argue that quantum mechanics is best understood as an idiosyncratically human description of interactions with the physical world – a structurally constrained, agent-indexed framework within which classicality emerges.

Light-matter coupled Hamiltonians are central to cavity materials engineering and polaritonic chemistry, but are challenging to simulate with classical hardware due to the scaling of the Hilbert space with the number of quantum photon modes and matter complexity. Leveraging the fact that quantum computers naturally represent photonic modes efficiently, we present a novel approach to simulate quantum-electrodynamical (QED) systems on near-term quantum hardware. After developing the bosonic and mixed operators in the Qiskit Nature framework, we employ them to simulate a first-order Trotterized Hamiltonian for a spontaneous-emission problem of a two-level system in an optical cavity. We find that using a standing-waves photonic basis approach leads to fidelity issues due to hardware connectivity constraints and two-qubits gates errors. Hence, we propose using a localized photonic basis approach that enforces nearest-neighbor couplings, thanks to which we can map the Hamiltonian as a 1D qubit chain. We significantly reduce the noise and, by applying the zero-noise extrapolation error mitigation technique, we recover the accurate quantum dynamics. Finally, we also show that this approach is resilient when relaxing the 1D qubit chain approximation.

In Local-local Oscillator (LLO) based Continuous-Variable Quantum Key Distribution (CV-QKD), the phase reference of the transmitter and receiver, Alice and Bob, are naturally de-correlated due to their use of individual lasers. A phase reference signal is used, whose measurement is critical for estimating the phase difference and correcting the raw QKD data. We observed that asymmetry in the quadrature measurements of the shot noise-limited heterodyne detector affects the accuracy of the reference signal's phase estimation and thereby reduces the achievable transmission distance and key rate of the CV-QKD system. We quantify the effect and propose a method to counteract the effect of detection asymmetry. We also evaluate the effects of detection asymmetry using quantum optical tomography.

Theoretical research has been conducted to study how geometry affects charge and spin transport in $\beta\mathrm{12}$ borophene quantum dots, which are confined systems. The study examined two distinct central regions, which included a circular disc and a regular hexagonal area that connected to semi-infinite zigzag and armchair borophene nanoribbon leads. The system was described by a five-band tight-binding Hamiltonian parameterized using first-principles data, and the transport properties were calculated within the non-equilibrium Green's function framework. Spin resolved transmissions and spin polarization were computed for a range of lead widths and proximity-induced exchange field strengths. The analysis revealed distinct transport characteristics determined by geometry and edge configuration: armchair-connected structures exhibited broader and more stable fully spin-polarized windows compared with zigzag-connected counterparts. Furthermore, critical lead-width thresholds ($\approx 1.01$ nm for zigzag and $\approx 0.87$ nm for armchair) and a moderate exchange field above which complete spin filtering occurs were identified. The results highlight the strong influence of edge termination and confinement geometry on transport properties and provide useful design guidelines for developing borophene-based nanoscale spintronic devices.

On the basis of the non-equilibrium Green function formalism, we derived a spinor Boltzmann equation for the Bose cold atom gases with high spin, which is achieved by a quantum Wigner transformation on the equation satisfied by the lesser Green function. After a Taylor series expansion on the scattering terms, a temperature-dependent spinor damping force can be obtained, which can be related to a non-abelian thermal gauge potential. For the spin-1 Bose gas, the thermal gauge potential constitutes a SU(3) Lie algebra. As an example, we calculate the spin coherence oscillation for the spin-1 Bose cold atom gas trapped in the optical lattice. The relative populations in the Zeeman states as well as the temperature-dependent damping force are illustrated numerically.

The hybrid interferometer integrating an optical parametric amplifier and a beam splitter has the potential to outperform the SU(1,1) interferometer. However, photon loss remains a critical limitation for practical implementation. To address this challenge, we propose a quantum metrology scheme utilizing multi-photon subtraction at the output and replacing the conventional 50:50 beam splitter with a variable beam splitter to enhance robustness against photon loss. We employ a coherent state and a vacuum state as inputs and perform homodyne detection. Our results show that the selection of input modes significantly affects phase estimation, and optimizing the beam splitter's transmittance is crucial for maximizing phase sensitivity in lossy conditions. Furthermore, photon subtraction markedly improves phase sensitivity, quantum Fisher information, and robustness against noise. Our scheme achieves sensitivities beyond the Heisenberg limit even under 20% photon loss.

A generalisation is provided for the notion of tags, often found in physics notation. The properties of tags and their extractors are discussed. It leads to the definition of tagged vector spaces. It is applied in the context of quantum optics, to provide a mathematical description for Dirac notation as used in physics. The commutation relations for ladder operators and a symplectic phase space are derived as consequences of the properties of tagged vector spaces. To incorporate the spatiotemporal and spin degrees of freedom, tags are indexed by functions, leading to a functional representation.