Papers

In modern power systems, edge devices serve as local hubs that collect data, perform on-site computing, sense electrical parameters, execute control actions, and communicate with neighboring edge devices as part of the larger grid. However, as the number of monitored nodes and control loops grows, traditional edge devices face serious limits. They can become overloaded by complex signal processing and decision tasks, causing delays and higher energy use. Standard sensors hit a noise floor that prevents them from detecting miniature changes, making it harder to spot early signs of faults or instability. Meanwhile, conventional communication links struggle with bandwidth limits, security risks, and rising encryption demands, which together slow down and weaken the transfer of critical grid information. Quantum technologies have the potential to overcome these challenges. Quantum computers can deliver exponential speed-ups for optimization and machine-learning tasks that ordinary processors cannot handle. Quantum sensors can sense signals with atomic precision, giving edge devices a more precise view of grid dynamics. Quantum communication techniques, including quantum key distribution, offer methods to achieve information-theoretic security and ensure that information arrives quickly and without tampering. We explore how quantum technologies can be integrated into edge devices, highlighting both opportunities and challenges.

In this paper, we introduce an algorithm for contracting (i.e. numerically evaluating) ZX-diagrams whose computational complexity is governed by rank-width, a structural graph parameter that behaves nicely under ZX rewrite rules. Our method evaluates a graph-like ZX-diagram in ~O(4^R) time, given its rank-decomposition of width R. By constructing suitable rank-decompositions, we provide a classical simulation algorithm for quantum circuits whose runtime is bounded by both ~O(2^n) and ~O(2^(t/2)), where n is the number of qubits and t is the number of non-Clifford phase gates. Thus, our method is no slower than either the naive state-vector simulation or the elementary stabiliser decomposition baseline, and in practice can be even faster when the reduced ZX-diagram has low rank-width. We benchmark our simulation algorithm against Quimb, a popular tensor contraction library, and observe substantial reductions in floating-point operations, often by several orders of magnitude, for random and structured non-Clifford circuits and for random ZX-diagrams.

We propose a quantum implicit neural representation (QINR)-based autoencoder (AE) and variational autoencoder (VAE) for image reconstruction and generation tasks. Our purpose is to demonstrate that the QINR in VAEs and AEs can transform information from the latent space into highly rich, periodic, and high-frequency features. Additionally, we aim to show that the QINR-VAE can be more stable than various quantum generative adversarial network (QGAN) models in image generation because it can address the low diversity problem. Our quantum-classical hybrid models consist of a classical convolutional neural network (CNN) encoder and a quantum-based QINR decoder. We train the QINR-AE/VAE with binary cross-entropy with logits (BCEWithLogits) as the reconstruction loss. For the QINR-VAE, we additionally employ Kullback-Leibler divergence for latent regularization with beta/capacity scheduling to prevent posterior collapse. We introduce learnable angle-scaling in data reuploading to address optimization challenges. We test our models on the MNIST, E-MNIST, and Fashion MNIST datasets to reconstruct and generate images. Our results demonstrate that the QINR structure in VAE can produce a wider variety of images with a small amount of data than various generative models that have been studied. We observe that the generated/reconstructed images from the QINR-VAE/AE are clear with sharp boundaries and details. Overall, we find that the addition of QINR-based quantum layers into the AE/VAE frameworks shows improved performance of reconstruction/generation under the constrained experimental setting relative to the specific baselines.

Surface-code threshold estimates depend on the inference pipeline, including decoder and estimator choices. We compare decoders within a single LiDMaS+ workflow under Pauli-reference and digitized hybrid continuous-variable/discrete sweeps. In the Pauli-reference mode, the matching-style backend outperforms Union-Find and yields crossing median $p_c=0.0531$ (bootstrap interval $[0.0415,0.0572]$) and collapse fit $p_c=0.052$ ($\nu=1.35$). For the hybrid mode, a dense transition-window sweep at $d=3,5,7$ uses $\sigma\in[0.30,0.50]$ with step $0.01$ and $3000$ trials per point. After the initial exact-zero plateau is excluded from crossing localization, the matching-style backend gives interior crossing estimates $\sigma_c=0.4707$ for $(d=3,5)$ and $\sigma_c=0.3275$ for $(d=5,7)$; the latter lies in a low-LER region and remains estimator-sensitive. A targeted $d=9$ extension shows larger Union-Find LER at moderate-to-high $\sigma$ and matching-fallback rates up to $0.747$ at $\sigma=0.50$. In a $d=5$ neural-guidance sensitivity sweep, full learned reweighting reduces the sampled mean LER from $0.1773$ to $0.1663$ over $\sigma\in[0.35,0.55]$. These results show that estimator resolution and backend fallback diagnostics are part of an auditable decoder comparison.

We present a formalism for semiclassical time evolution in quantum mechanics, building on a century of work. We identify complex saddle points in real time, real saddle points in complex time, and complex saddle points in complex time that reproduce the known answers in classic problems. For the decay of a metastable state, we find finite time and finite energy analogs of the "bounce" which do not have strict zero or negative modes. The one-loop phase of the wave function and the multiplicity of bounce solutions at late times are discussed. The motivation of this work is to learn how to compute decay rates in quantum field theory in situations with non-trivial time dependence, by first taking a humble step backwards to the fascinating world of quantum mechanics.

Analogously to classical computers, quantum processors exhibit side channels that may give attackers access to potentially proprietary algorithms. We identify and exploit a previously unexplored side channel in trapped-ion quantum processors that arises from the radio-frequency (RF) signals used to modulate lasers for ion cooling, gate execution, and readout. In these quantum processors, acousto-optical modulators (AOMs) imprint phase and frequency modulations onto laser fields interacting with the ions to implement individual and collective unitaries. The AOMs are driven by strong RF signals, a fraction of which leaks out of the device. We discuss general strategies to exploit this side channel and demonstrate how to detect RF leakage from a state-of-the-art qudit-based quantum processor using off-the-shelf components. From this data, we extract pulse characteristics of single-ion and entangling gates, thereby implementing a proof-of-principle exploitation of the novel attack vector. Finally, we outline ways to mitigate the information leakage through the presented side channel.

Temporal modes of photons are a promising encoding scheme for high-dimensional quantum networks due to their high channel capacity and fiber compatibility. However, realizing their full potential requires devices capable of synchronizing, processing and interfacing these modes across photonic and atomic bandwidths. In this work, we demonstrate a programmable high-dimensional temporal mode processor using a Raman quantum memory in warm cesium vapor. We exploit the single-mode nature of the Raman interaction kernel, dynamically shaping the control field to synthesize a tunable coherent filter that selectively addresses specific temporal waveforms. This mechanism enables on-demand storage, filtering, and conversion, providing a coherent interface between MHz- and GHz-bandwidth modes. We validate the platform's selectivity across a basis of 30 orthogonal Hermite-Gaussian modes and certify high-fidelity quantum operation via 5-dimensional process tomography. By combining deterministic mode conversion with bidirectional bandwidth interfacing, we establish the Raman memory as a critical active node for scalable quantum information processing.

We present a method for calculating the ground state energy of the Fermi-Hubbard model leveraging Rydberg atom processors and sample-based quantum diagonalization (SQD). By exploiting the perturbative relationship between the Fermi-Hubbard and Heisenberg models, the procedure samples from the Heisenberg model as prepared on the Rydberg atom processor, and uses the samples to diagonalize the Fermi-Hubbard model for large U. We include anisotropy and next-nearest-neighbor interactions and discuss the relevant regime for quasi-superconductivity in the 1-dimensional Fermi- Hubbard model. Numerical and experimental results on the Aquila quantum processor are presented for ground state energy calculations as well as the chemical potential. We find that the Heisenberg model sampling in the studied regime is sufficient to converge near to the ground state for up to 56 qubits, and we see a clear advantage of Rydberg atom sampling as opposed to random sampling even with 10x more samples for diagonalization. We also present a gate-based implementation of the gate-based SQD algorithm on IBM Quantum hardware for 56-qubit Hubbard model as a benchmark. Finally, we provide a gap analysis for studying emergent superconductivity using this method.

Distributed quantum computing can potentially address the scalability challenge by networking processors through photon-mediated remote entanglement. Prior approaches assumed that remote Bell pairs require distillation before use, incurring substantial overhead, to achieve sufficiently high fidelity. However, recent results show that lattice-surgery operations at logical qubit boundaries tolerate significantly higher error rates than previously assumed. We quantify the resource trade-offs between distillation overhead and surface-code distance requirements under realistic constraints including probabilistic entanglement generation and memory decoherence. We identify the fidelity crossover point separating the two regimes. Below this threshold, the distillation strategy dominates, reducing resource overhead by up to two orders of magnitude. Above it, no-distillation becomes the more efficient choice, reducing resource overhead by more than half. We briefly describe the application of these methods to ion-trap and neutral-atom platforms. These results provide joint design guidelines for optimizing photonic interconnects and fault-tolerant architectures in distributed quantum computing.

We propose a hybrid quantum-classical algorithm for the simulation of real-time dynamics in interacting quantum field theories coupled to classical fields, focusing on the self-consistent estimation of semiclassical backreaction. By discretizing space and time, we construct an iterative protocol that simulates the Trotterized dynamics of the quantum fields subject to the dynamical classical fields. By estimating certain quantum expectation values through a set of projective measurements, we source the equations of motion of the classical fields, and solve them numerically to feed them forward to the quantum simulation in an iterative self-consistent loop. Semiclassical backreaction is relevant in various fields of physics, particularly in cosmology, where quantum matter fluctuations affect the gravitational field dynamics, and a controlled renormalization must be carefully considered to get a sensible continuum limit. We benchmark our algorithm in this context, focusing on scalar-tensor theories of modified gravity exhibiting a chameleon mechanism, such that a light classical scalar field driving cosmic acceleration becomes massive in high-density regions, effectively screening any possible yet unobserved fifth force. By focusing on numerically tractable regimes, we explicitly show the convergence and robustness of our algorithm when considering the continuum limit and the effect of quantum shot noise. Our work paves the way for future experiments exploring other non-tractable regimes, including non-perturbative interactions of the quantum fields and how these can change backreaction and the gravitational dynamics.

Field emission (FE) electron sources are made close to atomic-scale to reach the highest spatial resolution as well as stable emission for electron microscopy, electron beam inspection and lithography. At present, no single agreed method exists of using FE current-voltage data to extract the apparent emission area, which is needed for predicting some beam properties. The 1956 theory of Murphy and Good (MG) is better physics than the 1920s theory of Fowler and Nordheim (FN) and colleagues, but many researchers use simplified FN theory to analyse experimental data. The present paper reports an experimental method of finding apparent emission area, based on using field ion and field electron microscopes (FIM-FEM). The discrepancy of emission area between the FIM-FEM method and MG-based analysis is a factor of 7.4, while that with simplified FN-based analysis is about 25, confirming MG theory is better for FE data analysis. The result allows deduction of key indicators, including source energy spread, reduced brightness and emission efficiency. A downloadable program is made available to help analysis. Our work provides a new experimental method of characterizing FE electron sources, especially the atomic-scale cold cathode, for which existing plot-based data-analysis methods are not suitable.

Classical reverse diffusion is generated by changing the drift at fixed noise. We show that the quantum version of this principle obeys an exact law with a sharp phase boundary. For Gaussian pure-loss dynamics, the canonical model of continuous-variable decoherence, we prove that the unrestricted instantaneous reverse optimum exhibits a noiseless-to-noisy transition: below a critical squeezing-to-thermal ratio, reversal can be noiseless; above it, complete positivity forces irreducible reverse noise whose minimum cost we determine in closed form. The optimal reverse diffusion is uniquely covariance-aligned and simultaneously minimizes the geometric, metrological, and thermodynamic price of reversal. For multimode trajectories, the exact cost is additive in a canonical set of mode-resolved data, and a globally continuous protocol attains this optimum on every mixed-state interval. If a pure nonclassical endpoint is included, the same pointwise law holds for every $t>0$, but the optimum diverges as $2/t$: exact Gaussian reversal of a pure quantum state is dynamically unattainable. These results establish the exact Gaussian benchmark against which any broader theory of quantum reverse diffusion must be measured.

This paper investigates whether hybrid quantum-classical machine learning can deliver practical improvements in financial fraud detection performance for card-based and other payment transactions. Building on a Guided Quantum Compressor architecture, the approach integrates an autoencoder, a variational quantum circuit, and a classical neural head, and then embeds this hybrid model into a mixture-of-experts framework including a state-of-the-art gradient-boosted tree classifier. Using a European credit card dataset with severe class imbalance, the routed hybrid architecture with 0.6 threshold achieves average precision scores of $0.793\pm0.085$ compared to $0.770\pm0.096$ of XGBoost on 3 repeated 5-fold cross-validation benchmarks. Precision and recall comparisons reveals a possible trade-off of fraud and nominal detections with a reduction in false positives at the cost of a small reduction in fraud detections. The improvements are achieved while adding only 7 to 21 minutes of extra inference time depending on the choice of hyperparameters. These results indicate that selectively routing transactions to quantum-classical models can enhance fraud detection while remaining compatible with the latency and operational constraints of modern financial institutions.

Broadband and low-loss superconducting switches can facilitate large-scale quantum information processors and cryogenic detectors by dynamically reconfiguring the connectivity of their circuits. The time dependent connectivity is enabled by the nonlinearity of lossless Josephson junctions, which are often wired into superconducting loops to be controlled by magnetic flux. However, this approach needs a power-consuming constant flux bias and dynamic flux actuation, both of which are hard to isolate from other switches or flux sensitive elements, limiting their integration density. Here, we design and characterize a microwave switch that implements a persistent current bias and direct current actuation to reduce static power consumption, actuation energy and potential crosstalk to other devices. We show that persistent current associated with tens of flux quanta is stable and long-lived, reducing the need for on-the-fly tuning. We further demonstrate that our switch has desirable performance for superconducting-circuit-based quantum information processing, including an off mode with more than 20 dB isolation comparable to commercial ferrite isolators, power handling larger than 100 pW sufficient for resonator readout tones and amplifier pumps, and modulation bandwidth broader than 600 MHz useful for multiplexing schemes.

Equational reasoning about circuits underpins quantum-circuit optimisation and verification. In the qubit CNOT-dihedral fragment, phase polynomials and layered normal forms support a complete equational theory. We develop the corresponding theory for prime-dimensional qudits, where basis labels, value controls, and phase exponents share prime-field arithmetic. We first describe reversible affine circuits over F_d as transformations x -> Ax + b, with an affine normal form extending Lafont's linear normal form by translations. Adjoining finite-angle diagonal phases by polynomial degree yields linear, quadratic, and cubic calculi. Their binomial-basis identities expose the mixed diagonal gates forced by affine transport. These calculi have unique phase-affine normal forms, and the resulting equational theories are complete: semantic equality coincides with derivable equality.

We investigate how the spin-1 Affleck-Kennedy-Lieb-Tasaki (AKLT) Hamiltonian can emerge from a microscopic fermionic model based on half-filled Hubbard tripods. We first show that a single tripod hosts a robust threefold-degenerate low-energy manifold corresponding to an effective $S = 1$ degree of freedom. This manifold prevails over a broad range of interactions and remains stable against moderate disorder. We then combine exact diagonalization with fourth-order quasi-degenerate perturbation theory to derive an effective bilinear-biquadratic spin model for a pair of coupled tripods and identify coupling regimes where the target ratio is approached. In particular, tuning leg-center hopping together with two symmetry-inequivalent leg-leg hoppings yields the characteristic singlet-triplet degeneracy associated with a biquadratic-to-bilinear ratio close to 1/3. Extending the analysis to three tripods, we compare nonequivalent coupling geometries and find a strategy that suppresses unwanted longer-range and multispin terms while preserving the target nearest-neighbor couplings in the weak-coupling regime. These results establish a concrete bottom-up route from Hubbard clusters to valence-bond-solid spin physics in tunable quantum-dot arrays.

We study a practical question in generative quantum machine learning: given a classical dataset, can we determine, before training, whether it is well suited to a quantum generative model? We focus on a class of quantum circuits known as instantaneous quantum polynomial-time (IQP) circuits, whose output distributions are widely believed to be difficult to sample from using classical methods. These circuits are used to build our quantum generative models. We introduce a Correlation-Complexity Map, a simple diagnostic built from two quantities computed from data samples. The first measures how closely the dataset's spectral correlation patterns resemble those naturally produced by IQP circuits, while the second quantifies how much of the dataset's structural correlation cannot be captured by simple pairwise models. In other words, we can estimate beforehand how well a dataset can be approximated by our model family and also how complex its correlations are, indicating possible failures of classical models. Applying this framework, we identify turbulence data as a promising target for quantum generative modeling. Guided by this analysis, we use a latent-parameter adaptation scheme that reuses a compact IQP circuit over a temporal sequence by learning and interpolating a low-dimensional latent trajectory, and observe competitive performance against classical baselines in a low-data, low-parameter regime. These results suggest that dataset-level diagnostics can help prioritize problems where quantum generative models are most likely to be useful, with improvements in data and parameter efficiency.

High-rate, high-fidelity entanglement distribution is essential for the creation of a quantum internet, and spontaneous parametric downconverters (SPDCs) are, at present, the preferred sources of entangled signal-idler photon pairs for transmission to Alice and Bob's quantum nodes. SPDCs using phase-matched spectral islands are especially attractive, in this regard, because they provide wavelength-division multiplexed signal-idler pairs with single-mode temporal behavior. This paper compares the entanglement distribution rates of three islands-based systems. Two use idler detections for heralding: islands-based zero-added-loss multiplexing (ZALM), and an islands-based Sagnac SPDC source with signal-path erasure. The third employs an unheralded Sagnac SPDC source. For 90% or lower heralding efficiencies, ZALM's per-pump-pulse entanglement distribution rate exceeds that of the signal-path erasure source, and both rates are inferior to unheralded operation's when all three systems employ $N_I$ spectral islands and allocate $N_M = N_I$ quantum memories to each pump pulse. These behaviors, however, must be weighed against the three systems' differing equipment requirements, e.g., ZALM requires a pair of perfectly-matched Sagnac sources, which is a significant burden not incurred by the signal-path erasure approach, and both heralded systems will suffer, in comparison with unheralded operation, if they cannot realize high heralding efficiencies.

In a previous paper we began our analysis on the role of non self-adjoint Hamiltonians in connection with the Heisenberg dynamics for quantum systems. Here, motivated by the growing interest on this topic and on some recent results on dynamical systems, we continue this analysis focusing on what we believe is an unexplored (or, at least, not so explored! aspect of Heisenberg dynamics, related to the need for using vectors which are brute-force normalized. Our main interest is on conserved quantities, and on conditions which guarantee that some observables of the system, or their mean values, do not evolve in time.

Quantum key distribution (QKD) is the most explored application of quantum information theory. A central problem in entanglement-based QKD (EB-QKD), is whether every entangled state can be used to extract a key. We observe that entanglement is not sufficient for standard practical EB-QKD protocols where the input choices are announced by the parties that want to share a secure key, such as E91 or entanglement-based BB84 type protocols, when even an arbitrarily small amount of leakage of classical side information occurs. We do this by identifying a class of two-qubit isotropic states that are entangled but cannot be used to distil the key under such protocols for any possible measurement by the parties. Counter-intuitively, this gap persists even when the leakage occurs from the "junk" rounds of the protocol, i.e, rounds that cannot be used to generate any key. We then extend this result to arbitrary dimensions and parties by identifying a class of isotropic states that are not useful to extract a secure key under such protocols, even if they are entangled. Finally, we demonstrate that our approach provides a tool to upper-bound the scalability of repeater-based QKD architectures in a protocol-independent manner. Interestingly, we find that allowing for even a tiny noise in the preparation drastically reduces the scalability of the QKD network.

We present an analytical model for intermolecular Coulombic electron capture (ICEC) which includes the internal nuclear dynamics of the molecules involved. In ICEC, an electron attaches to an atom or molecule by transferring excess energy to a neighbor, ionizing said neighbor. While previous theoretical investigations assumed fixed nuclei, recent studies indicate that relative motion between the two ICEC partners significantly influences the process. Here, we incorporate the internal nuclear motion of the molecules involved into an analytical equation of the ICEC cross section. We employ two approaches: 1. utilizing theoretical vibrationally resolved photoionization cross sections and 2. applying the Franck-Condon principle. Our theory yields electron spectra, ICEC cross sections for individual vibronic transitions, and temperature dependent cross sections. Nuclear dynamics lead to a distribution of the electronic cross section over several vibrational states and, in our model system H+ LiH, triggers dissociation of LiH during ICEC.

We introduce an $\varepsilon$-approximate unitary 2-design that is compatible with the structure of p- and q-quadratures in continuous-variable (CV) quantum systems. The design unitaries are defined on a finite-dimensional discretisation of the CV space and can be physically implemented as operations on the full CV space. This establishes the first approximate unitary design for CV systems. The design alternatingly acts with unitaries based on the quadrature operators $\hat q$ and $\hat p$. We prove that the parameter $\varepsilon$ is given by $1/d^\ell$, where $d$ is the dimension of the truncated Hilbert space and $\ell$ is the number of iterations. We propose an Unclonable Encryption scheme in which the encryption operators are given by the unitaries which constitute the approximate unitary design. We prove its security using recent results on decoupling. This establishes unclonable-indistinguishable security for a CV encryption for the first time.

We investigate the problem of compiling the generation of graph states to arbitrarily many distributed homogeneous quantum processing units (QPUs), providing a scalable partitioning algorithm and graph state generation protocol to minimize the number of Bell pairs required. Current approaches focus on the naive metric of cut edges to estimate the quantum communication cost. We show that the problem of balanced k graph partitioning, with the objective of minimizing the sizes of the maximum matchings between the partitions, leads to lower entanglement requirements across partitions. Our heuristic algorithm, BURY, partitions graph states to require fewer Bell pairs for generation than state-of-the-art k partition algorithms. Furthermore, we show that BURY reduces the cut-rank of the partitions, demonstrating that the partitioning found by our algorithm is likely to minimize the Bell pair utilization of any future improved distributed graph state generation protocol. We also discuss how our methods apply to the dynamic case where the graph state generation and measurement are performed concurrently. Our compilation approach provides a scalable foundation for reducing quantum network overhead for distributed measurement-based quantum computation (MBQC), as well as any scheme where distributed graph state generation is desired.

Various algorithms have been developed to simulate quantum circuits on classical hardware. Among the most prominent are approaches based on stabilizer decompositions and tensor network contraction. In this work, we present a unified framework that bridges these two approaches, placing them under a common formalism. Using this, we present two new algorithms to simulate an $n$-qubit circuit $C$: one that runs in $\tilde{O}(T^{\mathsf{tw}(C)})$ time and the other in $\tilde{O}(T^{\gamma\cdot \mathsf{tw}(C)})$ time, where $\mathsf{tw}(C)$ and $\mathsf{rw}(C)$ refer to the the tree-width and rank-width, respectively, of a tensor network associated to $C$, $T$ is the number of non-Clifford gates in $C$, and $\gamma \approx 3.42$. The proposed algorithms are simple, only require a linear amount of memory, are trivially parallelizable, and interact nicely with ZX-diagram simplification routines. Furthermore, we introduce the refined complexity measures focused tree-width and focused rank-width, which are always at least as efficient as their standard equivalent; these can be directly applied within our simulation algorithms, allowing for a more precise upper bound on the run time.

We improve the accuracy of Active Volume (AV) resource estimates by explicitly scheduling when AV blocks execute and when their reactions complete. We present a cycle-accurate scheduler that assigns each logical qubit a role in every logical cycle. The approach allows us to capture execution effects such as gate packing inefficiencies, parallelization overhead, and memory requirements. In addition, the scheduler explicitly tracks reaction latency and stale state lifetimes, rather than assuming that stale states can be absorbed into a fixed overhead. For a $4\times4$ Fermi–Hubbard test circuit, we find a transition to reaction-dominant regime at approximately $10^3$ logical qubits. Beyond $10^3$ logical qubits, stale state accumulation restricts the effective workspace and the benefit of adding more qubits is substantially decreased. Below this regime the scheduler predicts a $1.64\times$ runtime speedup relative to the previous Analytic Resource Estimators (ARE), primarily due to a $1.80\times$ reduction in average memory occupancy. We also show that parallelization and reaction latency overhead grows with available workspace, inverting the trend of the ARE model used in \cite{Caesura25}. These results provide a path toward reaction-aware analytic resource estimates and a full compilation pipeline for AV architectures. We also develop novel techniques for estimating Y state and routing demand and apply them to our test circuit. The Y state estimates result in small efficiency boost and the routing demand estimates show that routing should not bottleneck early fault-tolerant AV devices.

We introduce a state-based feedback law that stabilizes quantum states or subspaces associated with extremal values of a continuously monitored observable - a problem motivated by quantum cooling tasks. We then propose an output-based approximation that uses simple filtering of the measurement record to emulate the required feedback signal, thereby avoiding full real-time quantum state estimation, a key bottleneck for implementing and scaling filtering-based feedback control. The performance of the resulting strategy is demonstrated numerically on two test-bed models for feedback cooling.

Strong-to-weak spontaneous symmetry breaking (SWSSB) defines a mixed-state phase of matter–without a pure-state counterpart–in which nonlinear observables such as the Rényi-2 correlator develop long-range order while conventional linear correlations remain short-ranged. Here we study the emergence of SWSSB in one-dimensional open quantum systems governed by Lindbladian evolution, where the transition time diverges with system size and SWSSB appears only asymptotically in the steady state. By tracking the late-time growth of the Rényi-2 correlation length, we uncover a universal dynamical regime controlled purely by the symmetry class of the Lindbladian. Contrary to the conventional expectation that late-time dynamics are governed by the low-lying Liouvillian spectrum, we find that the time dependence of the SWSSB transition–exponential versus algebraic–is dictated solely by symmetry, independent of details of the Lindbladian, including whether the Liouvillian spectrum is gapped or gapless. For $\mathbb{Z}_2$-symmetric dynamics, the Rényi-2 correlation length grows exponentially in time–even when the spectrum is gapless–yielding an effective transition time $t_c \propto \operatorname{ln} L$ and enabling rapid preparation of the $\mathbb{Z}_2$ SWSSB steady state. In contrast, U(1)-symmetric dynamics exhibit algebraic scaling, $t_c \propto L^{\alpha}$, with a filling-dependent dynamical exponent: ballistic growth ($\alpha \approx 1$) at finite filling crosses over to diffusive scaling ($\alpha = 2$) in the zero-filling limit. These results establish symmetry–rather than spectral gap structure–as the controlling principle for SWSSB late-time dynamical scaling, and open a new route to nonequilibrium symmetry breaking in open quantum systems.

Quantum computing may help reduce computational costs of simulating large and nonlinear systems, but research into the use of quantum computers in atmospheric and oceanic sciences is still at an early stage. This study explores the use of quantum computing for calculating the collision-coalescence process that dominates the size growth of liquid particles in cloud microphysics. Inspired by the quantum algorithms developed in the field of financial engineering, we propose a new algorithm based on a master equation that describes the time evolution of the droplet mass distribution. Our algorithm uses the quantum amplitudes to encode the probability distribution of droplet mass and calculates the expected number of droplets via the quantum amplitude estimation. The key contribution is an encoding strategy that maps the multivariate collision-coalescence problem onto a quantum computation by recording only the transition history rather than the full mass distributions at every time step. This approach reduces the per-step qubit cost from $O(\sqrt{N})$ to $O(\log N)$, where $N$ is the number of mass bins. Our resource analysis shows that the number of T gates (T-count) scales as $\widetilde{O}(N^2)$ per mass bin in $N$, for a fixed number of time steps and a given target accuracy, where the $\widetilde{O}$ notation suppresses polylogarithmic factors in $N$. This is a substantial improvement over classical master-equation methods, whose cost grows super-polynomially with $N$ as the number of possible states increases. Our results suggest that the collision-coalescence process is one of the promising targets of quantum computing in the field of atmospheric science.

This review presents a thermodynamic perspective on quantum coupled transport processes in nanoscale systems. Our analysis is formulated within the framework of entropy production rate, the central quantity governing non-equilibrium processes and expressed through conjugate force-flux pairs. Although thermodynamic laws are universal across classical and quantum domains, the discussion is developed within a microscopic open quantum system framework, focusing on quantum dots (QDs) coupled to electronic reservoirs. We first examine elementary single transport processes and highlight their strong thermodynamic constraints in the near-equilibrium regime. This motivates the study of coupled transport, where multiple force-flux pairs coexist and interact, leading to richer thermodynamic behaviour. Using entropy production as the guiding principle, we analyse coupled energy and particle transport in a minimal two-terminal single-QD setup and show how conventional thermoelectric phenomena, including Seebeck and Peltier effects as well as thermoelectric heat engines and refrigerators, naturally emerge as thermodynamic cross-effects. We then extend the framework to a three-terminal coupled quantum dot (CQD) geometry, which provides a versatile platform for studying coupled transport and reduces, under suitable constraints, to the well-known Sánchez-Büttiker configuration. Beyond standard cross-effects, we discuss the phenomenon of inverse currents in coupled transport (ICC), where a current flows against mutually parallel thermodynamic forces without violating the second law. We show that ICC requires breaking the symmetry between energy and particle transport and identify the conditions for its realization in coupled quantum-dot systems with attractive interdot interactions.

Symmetry-protected topological (SPT) phases extend the Landau paradigm of quantum matter by admitting distinct symmetry-preserving phases that lack any local order parameter. Demonstrating these phases at scale on programmable quantum processors is a key milestone in using quantum hardware to probe emergent many-body phenomena, yet it is impeded by the circuit depth normally required to capture non-trivial entanglement. Here we use a tensor network based approximate quantum compiling (AQC) protocol to construct shallow quantum circuits (18-39 CNOT depth), which prepare 100-site ground states of the spin-1/2 bond-alternating Heisenberg chain in both SPT phases, to 97.9-99.0% fidelity. Upon executing the circuits on IBM quantum hardware, the resulting states exhibit all defining signatures of SPT order including non-local string order for strings of up to length 20, characteristic degeneracies in the entanglement spectrum and clear evidence of symmetry-protected edge modes. The simultaneous observation of these independent diagnostics establishes current quantum computers as versatile platforms for large-scale studies of symmetry-protected quantum matter. More broadly, our results establish a practical foundation for probing non-equilibrium quench dynamics of such systems in regimes that challenge classical computational methods.