Papers

An important task in multi-objective optimization is generating the Pareto front – the set of all Pareto-optimal compromises among multiple objective functions applied to the same set of variables. Since this task can be computationally intensive even for small problems, it is a natural target for quantum optimization. Indeed, this problem was recently approached using the quantum approximate optimization algorithm (QAOA) on an IBM gate-model processor. Here we compare these QAOA results with quantum annealing on the same two input problems, using the same methodology. We find that quantum annealing vastly outperforms not just QAOA run on the IBM processor, but all classical and quantum methods analyzed in the previous study. On the harder problem, quantum annealing improves upon the best known Pareto front. This small study reinforces the promise of quantum annealing in multi-objective optimization.

We theoretically analyse the relation between non-Gaussianity and security of entanglement-based quantum key distribution (QKD) protocols, namely device-independent (DI) and entanglement-based BB$84$. A similar analysis has already been made for prepare-and-measure (P&M) protocols \cite{Lasota2017}. In addition, we consider imperfect detection with dark counts and limited efficiency. We assume a perfect source of entangled Bell states as produced by quantum-dot type sources, depolarisation in the channel and different noise statistics, namely thermal and Poissonian. We consider single-photon avalanche photodiodes (SPAD) and photon number resolving detectors (PNRD) and use their respective criteria for non-Gaussianity. The results show cross-regions for both security and non-Gaussianity, hence, the possibility to conclude about the suitability of a given channel for secret key distribution. Our results can be useful as a pre-check for the implementation of QKD protocols.

Quantum computing requires effective error correction strategies to mitigate noise and decoherence. Quantum Low-Density Parity-Check (QLDPC) codes have emerged as a promising solution for scalable Quantum Error Correction (QEC) applications by supporting constant-rate encoding and a sparse parity-check structure. However, decoding QLDPC codes via traditional approaches such as Belief Propagation (BP) suffers from poor convergence in the presence of short cycles. Machine learning techniques like Graph Neural Networks (GNNs) utilize learned message passing over their node features; however, they are restricted to pairwise interactions on Tanner graphs, which limits their ability to capture higher-order correlations. In this work, we propose HyperNQ, the first Hypergraph Neural Network (HGNN)- based QLDPC decoder that captures higher-order stabilizer constraints by utilizing hyperedges-thus enabling highly expressive and compact decoding. We use a two-stage message passing scheme and evaluate the decoder over the pseudo-threshold region. Below the pseudo-threshold mark, HyperNQ improves the Logical Error Rate (LER) up to 84% over BP and 50% over GNN-based strategies, demonstrating enhanced performance over the existing state-of-the-art decoders.

Quantum algorithms for computational linear algebra promise up to exponential speedups for applications such as simulation and regression, making them prime candidates for hardware realization. But these algorithms execute in a model that cannot efficiently store matrices in memory like a classical algorithm does, instead requiring developers to implement complex expressions for matrix arithmetic in terms of correct and efficient quantum circuits. Among the challenges for the developer is navigating a cost model in which conventional optimizations for linear algebra, such as subexpression reuse, can be inapplicable or unprofitable. In this work, we present Cobble, a language for programming with quantum computational linear algebra. Cobble enables developers to express and manipulate the quantum representations of matrices, known as block encodings, using high-level notation that automatically compiles to correct quantum circuits. Cobble features analyses that compute the time and space usage of programs, as well as optimizations that reduce overhead and generate efficient circuits using state-of-the-art techniques such as the quantum singular value transformation. We evaluate Cobble on benchmark kernels for simulation, regression, search, and other applications, showing 2.6x-25.4x speedups on these benchmarks compared to the unoptimized baseline.

Resonant coupling of a vibration to a cavity mode has been reported to dramatically modify spontaneous Raman scattering, but subsequent studies have produced conflicting results. In this Letter, we develop a microscopic quantum framework that captures the spatial structure of polaritonic modes. In a homogeneously filled cavity, spatial overlap between polaritons and cavity resonances enforces selection rules that suppress the initially reported polaritonic Raman peaks, consistent with most experiments. In contrast, for a quasi-two-dimensional (2D) molecular layer, these rules are lifted, yielding Raman peaks at the polariton energies. Our Letter clarifies that the Raman response under vibrational strong coupling is determined by cavity-vibration spatial mode overlap and offers a framework for Raman studies of strongly coupled quasi-2D systems.

Relaxation in open quantum systems is fundamental to quantum science and technologies. Yet, the influence of the initial state on relaxation remains a central, largely unanswered question. Here, by systematically characterizing the relaxation behavior of generic initial states, we uncover a typicality phenomenon in high-dimensional open quantum systems: relaxation becomes nearly initial-state-independent as system size increases under verifiable conditions. Crucially, we prove this typicality for thermalization processes above a size-independent temperature. Our findings extend the typicality to open quantum dynamics, in turn identifying a class of systems where two widely used quantities – the Liouvillian gap and the maximal relaxation time – merit re-examination. We formalize this with two new concepts: the 'typical strong Mpemba effect' and the 'typical relaxation time'. Beyond these conceptual advances, our results provide practical implications: a scalable route to accelerating relaxation and a typical mixing-time benchmark that complements conventional worst-case metrics for quantum simulations and state preparation.

This work investigates measurement strategies for link parameter estimation in Quantum Network Tomography (QNT), where network links are modeled as depolarizing quantum channels distributing Werner states. Three distinct measurement schemes are analyzed: local Z-basis measurements (LZM), joint Bell-state measurements (JBM), and pre-shared entanglement-assisted measurements (PEM). For each scheme, we derive the probability distributions of measurement outcomes and examine how noise in the distributed states influences estimation precision. Closed-form expressions for the Quantum Fisher Information Matrix (QFIM) are obtained, and the estimation precision is evaluated through the Quantum Cramer-Rao Bound (QCRB). Numerical analysis reveals that the PEM scheme achieves the lowest QCRB, offering the highest estimation accuracy, while JBM provides a favorable balance between precision and implementation complexity. The LZM method, although experimentally simpler, exhibits higher estimation error relative to the other schemes; however, it outperforms JBM in high-noise regimes for single-link estimation. We further evaluate the estimation performance on a four-node star network by comparing a JBM-only configuration with a hybrid configuration that combines JBM and LZM. When two monitors are used, the JBM-only strategy outperforms the hybrid approach across all noise regimes. However, with three monitors, it achieves a lower QCRB only in low-noise regimes with heterogeneous links. The results establish a practical basis for selecting measurement strategies in experimental quantum networks, enabling more accurate and scalable link parameter estimation under realistic noise conditions.

This work presents an exact microcanonical combinatorial analysis of the one-dimensional antiferromagnetic Ising model. At the primary ground-state level crossing $B/J=2$, degeneracies follow the Fibonacci and Lucas sequences for open chains and periodic rings, respectively. We extend this framework to the complete excitation spectrum, demonstrating that the density of states is constructed from topological defects governed by linear Diophantine equations and $p$-fold Fibonacci convolutions. Open boundaries act as fractional defects, densifying the chain spectrum into energy steps of $2J$, whereas the closed ring remains quantized in units of $4J$. Notably, this exact topological counting exposes non-trivial spectral gaps near the fully polarized limit, strictly forbidding the penultimate macroscopic energy levels in both topologies. Through the transfer matrix formalism, we derive exact closed-form expressions for the critical degeneracies at all energy levels. These results provide a rigorous analytical foundation for extracting exact residual entropies and exposing the intrinsic number-theoretic architecture of quantum critical manifolds.

Estimating the fidelity between an unknown quantum state and a fixed target is a fundamental task in quantum information science. Direct fidelity estimation (DFE) enables this without full tomography by sampling observables according to a target-dependent distribution. However, existing approaches face notable trade-offs. Grouping-based DFE achieves strong accuracy for small systems but suffers from exponential scaling, and its applicability is restricted to Pauli measurements. In contrast, classical-shadow-based DFE offers scalability but yields lower accuracy on structured states. In this work, we address these limitations by developing two classes of operator-aware shadow importance sampling algorithms using informationally overcomplete positive operator-valued measures. Instantiated with local Pauli measurements, our algorithm improves upon the grouping-based algorithms for Haar-random states. For structured states such as the GHZ and W states, our algorithm also eliminates the exponential memory requirements of previous grouping-based methods. Numerical experiments confirm that our methods achieve state-of-the-art performance across Haar-random, GHZ, and W targets.

We systematically investigate the directional dependence of Gaussian quadripartite quantum steering and its redistribution among different modes in the background of a Schwarzschild black hole. For physically accessible sectors, we identify three distinct behaviors: (i) steering from non-gravitational to gravitational observers undergoes sudden death at maximal asymmetry with the Hawking temperature, marking the crossover from two-way to one-way steerability; (ii) steering in the opposite direction decays monotonically and vanishes only in the extreme black hole limit, highlighting its directional sensitivity to spacetime curvature; (iii) steering from hybrid gravitational-non-gravitational partitions to non-gravitational mode persists at a finite asymptotic value set by the initial squeezing parameter. Moreover, all inaccessible steerings generated by the Hawking effect exhibit an intrinsic asymmetry, with their specific behavior being strongly dependent on the steering direction.

The weak values and weak measurement formalism were initially limited to pure states, which were later extended to mixed states, leading to intriguing applications in quantum information processing tasks. Weak values are considered to be abstract properties of systems describing a complete picture between successive measurements in the two-state vector formalism (TSVF). The remarkable achievements of the weak value formalism in experimental quantum mechanics have persuaded most quantum physicists that it is impeccable. However, we explore a scenario where the formalism of weak values for mixed states is employed in a quantum communication protocol, but discover that it generates inaccurate outcomes. This reinforces our previous conclusion that the weak values may not be elements of the reality of weak measurements, contrary to what the proponents of weak values proposed.

Recent advances in Kitaev materials have highlighted their potential to host Majorana fermions without or high-temperature of superconductivity. In this research, we propose $Li_2IrO_3$ as a promising High-temperature superconducting platform supporting Majorana edge modes due to its strong spin-orbit coupling, honeycomb lattice structure, and proximity to a quantum spin liquid (QSL) phase. A theoretical and numerical framework based on the Kitaev-Heisenberg Hamiltonian is developed to model spin interactions in $Li_2IrO_3$. Here, the existence of topological zero-energy states is demonstrated, and their signatures in the edge-localized spectral weight are identified. A device concept based on this material is also proposed with potential industrial applications in spintronics, magnetic field sensing, and topological quantum memory.

We establish operator norm bounds for bipartite tensor sums of self-adjoint contractions. The inequalities generalize the analytic structure underlying the Tsirelson and CHSH bounds, giving dimension-free estimates expressed through commutator and anticommutator norms. A graph based formulation captures sparse interaction patterns via constants depending only on graph connectivity. The results link analytic operator inequalities with quantum information settings such as Bell correlations and network nonlocality, offering closed-form estimates that complement semidefinite and numerical methods.

Quantum energy teleportation (QET), implemented via local operations and classical communication, enables carrier-free energy transfer by exploiting quantum resources. While QET has been extensively studied theoretically and validated experimentally in various quantum platforms, enhancing energy output for mixed initial states, as the system inevitably interacts with environments, remains a significant challenge. In this work, we study QET performance in a two-qubit system coupled to equilibrium or nonequilibrium reservoirs. We derive an analytical expression for the energy output in terms of the system Hamiltonian eigenstates, enabling analysis of energy output for mixed states. Using the Redfield master equation, we systematically examine the effects of qubit detuning, nonequilibrium temperature difference, and nonequilibrium chemical potential difference on the energy output. We find that the energy output for mixed states often follows that of the eigenstate with the highest population, and that nonequilibrium environments can enhance the energy output in certain parameter regimes.

Physical Unclonable Functions (PUFs) provide hardware-level security by exploiting intrinsic randomness to produce device-unique responses. However, machine learning and side-channel attacks increasingly undermine their classical assumptions, calling for new approaches to ensure unforgeability. Quantum mechanics naturally supports this goal through intrinsic randomness and the no-cloning theorem, motivating the study of Quantum Physical Unclonable Functions (QPUFs). Yet, existing QPUF models often assume ideal unitary dynamics, neglecting non-unitary effects such as decoherence and dissipation that arise in real quantum devices. This work introduces a new class of non-unitary QPUFs that leverage open quantum system dynamics as a foundation for security. Three architectures are proposed: the Dissipative QPUF (D-QPUF), which uses amplitude damping as an entropy source; the Measurement-Feedback QPUF (MF-QPUF), which employs mid-circuit measurements and conditional unitaries; and the Lindbladian QPUF (L-QPUF), which models Markovian noise via the Lindblad master equation and Trotter-Suzuki decomposition. Simulation results show that these non-unitary designs achieve strong uniqueness, uniformity, and unforgeability, with controllable reliability trade-offs from stochastic noise. The L-QPUF, in particular, exhibits exponential modeling resistance under limited challenge-response access. By reframing environmental noise as a constructive resource, this work establishes a framework for noise-aware quantum hardware authentication and highlights non-unitary evolution as a viable foundation for post-quantum security.

We study quantum differential privacy (QDP) by defining a notion of the order of informativeness between two pairs of quantum states. In particular, we show that if the hypothesis testing divergence of the one pair dominates over that of the other pair, then this dominance holds for every $f$-divergence. This approach completely characterizes $(\varepsilon,\delta)$-QDP mechanisms by identifying the most informative $(\varepsilon,\delta)$-DP quantum state pairs. We apply this to study precise limits for privatized hypothesis testing and privatized quantum parameter estimation, including tight upper-bounds on the quantum Fisher information under QDP. Finally, we establish near-optimal contraction bounds for differentially private quantum channels with respect to the Hockey-Stick divergence.

Stimulated Raman scattering (SRS) microscopy has emerged as a powerful technique for probing the spatiotemporal dynamics of molecular bonds with exceptional sensitivity, resolution, and speed. However, classically, its performance remains fundamentally constrained by optical shot noise, which imposes a strict limit on detection sensitivity and speed. Here, we demonstrate a quantum-enhanced SRS microscopy platform that circumvents this barrier by harnessing amplitude-squeezed light. Specifically, we generate a Stokes beam with $5.2~\mathrm{dB}$ of amplitude squeezing using traveling-wave optical parametric amplification in second-order nonlinear waveguides, and combine it with a tunable coherent pump to access vibrational modes spanning from $1000$ to $3100~\mathrm{cm}^{-1}$. Applied to quantum imaging of metabolites in biological tissue (pork muscle), our quantum-enhanced Raman microscope achieves an average noise suppression of $3.6~\mathrm{dB}$ and a $51\%$ enhancement in signal-to-noise ratio (SNR) – to the best of our knowledge, the largest improvement reported to date in quantum-enhanced SRS microscopy of biological samples.

Distributed quantum computing offers a potential solution to the complexity of superconducting chip hardware layouts and error correction algorithms. High-quality gates between distributed chips enable the simplification of existing error correction algorithms. This article proposes and demonstrates a remote quantum geometric gate scheme via parametric modulation. Our scheme inherits the intrinsic robustness of geometric phases. Meanwhile, by employing gradient-based optimization algorithms(Adaptive Moment Estimation) from deep learning, we design control waveforms that significantly suppress population leakage. We experimentally realize the rapid remote SWAP and $\sqrt{\text{SWAP}}$ gates with high fidelity, completing operation in about 30 ns. The gate error of SWAP ($\sqrt{\text{SWAP}}$) is 1.16% (0.91%) after excluding the effect of energy relaxation. The simulation demonstrate that this scheme can be implemented in the distributed chips connected by cables extending several meters. Our results highlight the effectiveness of the proposed protocol in enabling modular quantum processors, offering a promising path toward the realization of fault-tolerant quantum computation.

Quantum Extreme Learning Machines (QELMs) have emerged as a potent tool for various quantum information processing tasks. We present a QELM protocol for estimating the amount of entanglement in Werner states. The protocol requires the generation of a sequence of random Werner states, which are then combined with a reservoir state and evolved using an Ising Hamiltonian. A set of observables based on the Bloch basis is constructed and employed to train the system to recognize unseen features. To assess the protocol's robustness, noise is introduced into the input states, and the system's performance under these noisy conditions is analyzed. Additionally, the influence of the magnetic field parameter within the Ising Hamiltonian on the estimation accuracy is investigated.

In some cases in two and three bulk dimensions without bulk local degrees of freedom, I look for area operators in a fixed boundary theory. In each case, I define an exact quantum error-correcting code (QECC) and show that it admits a central decomposition. However, the area operator that arises from this central decomposition vanishes. A non-zero area operator, however, emerges after coarse-graining. The expectation value of this operator approximates the actual entanglement entropy for a class of states that do not form a linear subspace. These non-linear constraints can be interpreted as semiclassicality conditions. The coarse-grained area operator is ambiguous, and this ambiguity can be matched with that in defining fixed-area states.

Building on the standard hierarchy of pure states (HOPS) approach, we construct a generalized formulation suitable for open quantum systems interacting with nonstationary Gaussian baths, potentially extending its applicability to nonequilibrium baths. This is achieved by extending the conventional exponential decomposition of a bath correlation function (BCF) for nonstationary cases. Using our formulation of HOPS, we derive the corresponding hierarchy of master equations and, when each term in the BCF expansion can be associated with an independent physical bath, we show how the formalism connects to the well-known pseudomode representation. We demonstrate the method's performance on two examples of nonstationary squeezed reservoirs generated via uniform squeezing and degenerate parametric amplification in a one-sided cavity. Benchmarking against the hierarchy of master equations shows that HOPS is more efficient under hierarchy truncation. The pseudomode representation is shown to be more efficient in the strongly non-Markovian regime. Our results highlight HOPS as a versatile and powerful tool for simulating open quantum systems in nonstationary baths, with potential applications ranging from squeezed light-matter interactions to driven quantum materials and dissipative phase transitions.

Critical properties of a quantum system are recognized as valuable resources for quantum metrology. In this work, we investigate the criticality-enhanced sensing in a quantum Rabi triangle system, which exhibits multiple phases. Around the phase boundary, enhanced parameter estimation precision can be achieved by tuning either the scaled coupling strength or the hopping phase controlled by an artificial magnetic field. We observe that the quantum Fisher information shows divergent scaling near different quantum phase transition points, characterized by distinct critical exponents. When the resource consumption is taken into account, we find that the divergent quantum Fisher information can reach the Heisenberg limit. Furthermore, we propose a measurement scheme of the average photon number and the quantum Cramér-Rao bound can be saturated.

Metastability-exchange collisions (MECs) lie at the heart of metastability-exchange optical pumping (MEOP) in 3He, enabling the transfer of polarization from the metastable state to the ground state, as well as the optical detection of nuclear magnetic resonance. Leveraging MECs, optically pumped 3He nuclear magnetometers have been developed since the earliest demonstrations of MEOP. However, it also induces an additional frequency shift and relaxation of the nuclear spin precession, thereby limiting the sensitivity of the magnetometer. In this work, we identify a new source of frequency shift and relaxation in the 3He nuclear spin, arising from the light shift. This effect arises from an MEC-mediated interaction between light and the nucleon spin. We develop a theoretical model to describe this light-induced effect and highlight its significance in low magnetic fields. This effect is experimentally demonstrated, and its dependence on various parameters – including magnetic field strength, light intensity, and wavelength – is investigated. Our result provides a better understanding of the frequency shift and relaxation of 3He spin precession under MEOP conditions. Moreover, our experiment reveals an MEC-mediated coupling between the 3He nuclear spin and light, which may indicate the feasibility of MEC-assisted optical manipulation of 3He nuclear spins at the quantum level, as proposed in several theoretical schemes.

One way to approximate a quantum annealing schedule is to use multiple quantum walks chained together, without intermediate measurements, to produce a multi-stage quantum walk (MSQW). Previous work has shown that a MSQW is better than QAOA (quantum alternating operator ansatz) for solving optimization tasks using multiple stages [Gerblich et al, arXiv:2407.06663]. In this work, we develop an efficient heuristic for choosing the free parameters in MSQW, and use it to obtain improved scaling compared to single stage quantum walks. We show numerically that the heuristic works well for problems with a large minimum energy gap, giving a polynomial scaling in the number of stages, and leading to an overall algorithm that scales polynomially in time. For problems with a small minimum gap, the scaling breaks down such that adding more stages decreases the success probability, leading to an overall scaling that is exponential in time as expected. Our methods are general and can be applied to any optimization problem to obtain good annealing schedules.

Describing systems of superconducting atoms coupled to a continuum of photonic modes at multiple separated locations in a waveguide, waveguide quantum electrodynamics (QED) with giant atoms has emerged as a promising platform for realizing quantum interconnect. Such systems have been reported to exhibit rich phenomena that differ from those of natural atoms. Going beyond the widely used Born-Markov and Wigner-Weisskopf approximations, we investigate the non-Markovian dynamics of one and two giant atoms interacting with a waveguide formed by an array of coupled resonators. We discover that the diverse dynamical behaviors of the giant atoms are intrinsically determined by the energy spectrum of the composite system consisting of the giant atoms and the photonic modes in the waveguide. As long as one and more bound states are present in the energy spectrum, their excited-state probabilities, respectively, tend to stable finite values and lossless Rabi-like oscillations with frequencies proportional to the differences of the bound-state eigenenergies. Our result provides an insightful guideline for suppressing the decoherence of giant atoms and facilitates the development of quantum interconnect devices using giant-atom waveguide QED.

Single-photon sources are vital for emerging quantum technologies. In particular, Nitrogen-vacancy (NV) centers in diamond are promising due to their room-temperature stability, long spin coherence, and compatibility with nanophotonic structures. A key challenge, however, is the reliable identification of isolated NV centers, since conventional confocal microscopy is diffraction-limited and cannot resolve emitter distributions within a focal spot. Besides, the associated intensity scanning is a time-expensive procedure. Here, we introduce a raster-scanned $g^{(2)}(0)$ mapping technique combined with an inversion-based reconstruction algorithm. By directly measuring local photon antibunching across the field of view, we extract the effective emitter number within each focal spot and reconstruct occupancy maps on a sub-focal-spot grid. This enables recovery of the number and spatial distribution of emitters within regions smaller than the confocal focal spot, thereby offering possibilities of going beyond the diffraction limit. Our simulations confirm robust reconstruction of NV-center distributions. The method provides a practical diagnostic tool for locating single-photon sources in an efficient and accurate manner, at much lesser time and effort compared to conventional intensity scanning. It offers valuable feedback for nanophotonic device fabrication, supporting more precise and scalable integration of NV-based quantum photonic technologies.

Entangling gates between neighboring physical qubits are essential for quantum error correction. Implementing them in an all-microwave manner simplifies signal routing and control apparatus of superconducting quantum processors. We propose and experimentally demonstrate an all-microwave controlled-Z (CZ) gate that achieves high fidelity while suppressing residual ZZ interactions. Our approach utilizes a fixed-frequency transmon coupler and multi-path coupling, thereby sufficiently reducing the net transverse interaction between data transmons to suppress residual ZZ interactions. The controlled phase arises from the dispersive frequency shift of the $|gf\rangle$$\unicode{x2013}$$|eg\rangle$ transition between the coupler and one of the data transmons conditioned on the state of the other data transmon. Driving the transitions at the midpoint of two dispersively shifted resonance frequencies induces state-dependent geometric phases to achieve the CZ gate. Crucially, with this scheme, we can maintain a small net transverse interaction between two data transmons while increasing the coupling between the data and coupler transmons to accelerate the CZ-gate speed. Additionally, we measure the coupler state after the gate to detect a subset of decoherence-induced failures that occur during the gate operation. These events constitute erasure errors with known locations, enabling erasure-aware quantum error-correcting codes to improve future logical qubit performance.

Quantum computing technology is advancing rapidly. Yet, even accounting for these trends, a quantum leap would be needed for quantum computers to meaningfully impact deep learning over the coming decade or two. We arrive at this conclusion based on a first-of-its-kind survey of quantum algorithms and how they match potential deep learning applications. This survey reveals three important areas where quantum computing could potentially accelerate deep learning, each of which faces a challenging roadblock to realizing its potential. First, quantum algorithms for matrix multiplication and other algorithms central to deep learning offer small theoretical improvements in the number of operations needed, but this advantage is overwhelmed on practical problem sizes by how slowly quantum computers do each operation. Second, some promising quantum algorithms depend on practical Quantum Random Access Memory (QRAM), which is underdeveloped. Finally, there are quantum algorithms that offer large theoretical advantages, but which are only applicable to special cases, limiting their practical benefits. In each of these areas, we support our arguments using quantitative forecasts of quantum advantage that build on the work by Choi et al. [2023] as well as new research on limitations and quantum hardware trends. Our analysis outlines the current scope of quantum deep learning and points to research directions that could lead to greater practical advances in the field.

To fulfill their promise, quantum networks must transform from isolated testbeds into scalable infrastructures for distributed quantum applications. In this paper, we present a prototype orchestrator for the Argonne Quantum Network (ArQNet) testbed that leverages design principles of software-defined networking (SDN) to automate typical quantum communication experiments across buildings in the Argonne campus connected over deployed, telecom fiber. Our implementation validates a scalable architecture supporting service-level abstraction of quantum networking tasks, distributed time synchronization, and entanglement verification across remote nodes. We present a prototype service of continuous, stable entanglement distribution between remote sites that ran for 12 hours, which defines a promising path towards scalable quantum networks.

Electromagnetic simulations form an indispensable part of the design and optimization process for superconducting quantum devices. Although several commercial platforms exist, open-source alternatives optimized for high-performance computing remain limited. To address this gap, we introduce SQDMetal, a Python-based API that integrates Qiskit Metal (IBM), Gmsh, Palace (AWS), and Paraview (Kitware) into an open-source, highly parallel simulation workflow for superconducting quantum circuits. SQDMetal enables accurate, efficient, and scalable simulations while remaining community-driven and free from commercial constraints. In this work, we validate SQDMetal through mesh convergence studies which benchmark SQDMetal against COMSOL Multiphysics and Ansys, demonstrating excellent agreement for both eigenmode and electrostatic (capacitance) simulations. Furthermore, we simulate superconducting resonators and transmon qubits, showing reasonable agreement with experimental measurements. SQDMetal also supports advanced capabilities, including Hamiltonian extraction via the energy participation ratio (EPR) method, incorporation of kinetic inductance effects, and full 3D modelling of device geometry for improved predictive accuracy. By unifying open-source tools into a single framework, SQDMetal lowers the barriers to entry for community members seeking to access high-performance simulations to assist in the design and optimization of their devices.