Papers

Within a numerically exact simulation technique, the dissipative dynamics of a two-qubit architecture is considered in which each qubit couples to its individual noise source (reservoir). The goal is to reveal the role of subtle qubit-reservoir correlations including non-Markovian processes as a prerequisite to guide further improvements of quantum computing devices. This paper addresses the following three topics. First, we examine the validity of the rotating wave approximation imposed previously on the qubit-reservoir coupling with respect to the disentanglement dynamics. Second, generation of the entanglement as well as destruction are analyzed by monitoring the reduced dynamics during and after application of a $\sqrt{\mbox{iSWAP}^\dagger}$ gate, also focusing on memory effects caused by reservoirs. Finally, the performance of a Hadamard + CNOT sequence is analyzed for different gate decomposition schemes. In all three cases, various types of noise sources and qubit parameters are considered.

The problem of scheduling in quantum networks amounts to choosing which entanglement swapping operations to perform to better serve user demand. The choice can be carried out following a variety of criteria (e.g. ensuring all users are served equally vs. prioritizing specific critical applications, adopting heuristic or optimization-based algorithms...), requiring a method to compare different solutions and choose the most appropriate. We present a framework to mathematically formulate the scheduling problem over quantum networks and benchmark general quantum scheduling policies over arbitrary lossy quantum networks. By leveraging the framework, we apply Lyapunov drift minimization to derive a novel class of quadratic optimization based scheduling policies, which we then analyze and compare with a Max Weight inspired linear class. We then give an overview of the pre-existing fiber quantum simulation tools and report on the development of numerous extensions to QuISP, an established quantum network simulator focused on scalability and accuracy in modeling the underlying classical network infrastructure. To integrate satellite links in the discussion, we derive an analytical model for the entanglement distribution rates for satellite-to-ground and ground-satellite-ground links and discuss different quantum memory allocation policies for the dual link case. Our findings show that classical communication latency is a major limiting factor for satellite communication, and the effects of physical upper bounds such as the speed of light must be taken into account when designing quantum links, limiting the attainable rates to tens of kHz. We conclude by summarizing our findings and highlighting the challenges that still need to be overcome in order to study the quantum scheduling problem over fiber and satellite quantum networks. [Abridged abstract, see PDF for full version]

We model the Hayden–Preskill (HP) information recovery protocol in 2d CFTs via local joining quenches. Euclidean path integrals with slits prepare the HP subsystems: the message $M$, its reference $N$, the Page-time black hole $B$, the early radiation $E$, and the late radiation $R$; the remaining black hole after emitting $R$ is denoted as $B'$. The single-slit geometry provides an analytically tractable toy model, while the bounded-slit geometry more closely captures the HP setup. In the free Dirac fermion 2d CFT, the mutual information $I(N\!:\!B')$ shows quasi-particle dynamics with partial or full revivals, whereas that in holographic 2d CFTs, which are expected to be maximally chaotic, exhibit sharp transitions: in the bounded-slit case, when the size of the late radiation becomes comparable to that of the reference $N$, $I(N\!:\!B')$ does vanish at late time, otherwise it remains finite. This contrast between free CFTs and holographic CFTs gives a clear characterization of the HP recovery threshold.

We introduce hybrid sequential quantum computing (HSQC), a paradigm for combinatorial optimization that systematically integrates classical and quantum methods within a structured, stage-wise workflow. HSQC may involve an arbitrary sequence of classical and quantum processes, as long as the global result outperforms the standalone components. Our testbed begins with classical optimizers to explore the solution landscape, followed by quantum optimization to refine candidate solutions, and concludes with classical solvers to recover nearby or exact-optimal states. We demonstrate two instantiations: (i) a pipeline combining simulated annealing (SA), bias-field digitized counterdiabatic quantum optimization (BF-DCQO), and memetic tabu search (MTS); and (ii) a variant combining SA, BF-DCQO, and a second round of SA. This workflow design is motivated by the complementary strengths of each component. Classical heuristics efficiently find low-energy configurations, but often get trapped in local minima. BF-DCQO exploits quantum resources to tunnel through these barriers and improve solution quality. Due to decoherence and approximations, BF-DCQO may not always yield optimal results. Thus, the best quantum-enhanced state is used to continue with a final classical refinement stage. Applied to challenging higher-order unconstrained binary optimization (HUBO) problems on a 156-qubit heavy-hexagonal superconducting quantum processor, we show that HSQC consistently recovers ground-state solutions in just a few seconds. Compared to standalone classical solvers, HSQC achieves a speedup of up to 700 times over SA and up to 9 times over MTS in estimated runtimes. These results demonstrate that HSQC provides a flexible and scalable framework capable of delivering up to two orders of magnitude improvement at runtime quantum-advantage level on advanced commercial quantum processors.

We propose two Clifford+$T$ synthesis algorithms that are optimal with respect to $T$-count. The first algorithm, called deterministic synthesis, approximates any single-qubit unitary by a single-qubit Clifford+$T$ circuit with the minimum $T$-count. The second algorithm, called probabilistic synthesis, approximates any single-qubit unitary by a probabilistic mixture of single-qubit Clifford+$T$ circuits with the minimum $T$-count. For most of single-qubit unitaries, the runtimes of deterministic synthesis and probabilistic synthesis are $\varepsilon^{-1/2 - o(1)}$ and $\varepsilon^{-1/4 - o(1)}$, respectively, for an approximation error $\varepsilon$. Although this complexity is exponential in the input size, we demonstrate that our algorithms run in practical time at $\varepsilon \approx 10^{-15}$ and $\varepsilon \approx 10^{-22}$, respectively. Furthermore, we show that, for most single-qubit unitaries, the deterministic synthesis algorithm requires at most $3\log_2(1/\varepsilon) + o(\log_2(1/\varepsilon))$ $T$-gates, and the probabilistic synthesis algorithm requires at most $1.5\log_2(1/\varepsilon) + o(\log_2(1/\varepsilon))$ $T$-gates. Remarkably, complexity analyses in this work do not rely on any numerical or number-theoretic conjectures.

Post-selection strategies that discard low-confidence computational results can significantly improve the effective fidelity of quantum error correction at the cost of reduced acceptance rates, which can be particularly useful for offline resource state generation and other moderate-depth fault-tolerant circuits. Prior work has primarily relied on the "logical gap" metric with the minimum-weight perfect matching decoder, but this approach faces fundamental limitations including computational overhead that scales exponentially with the number of logical qubits and poor generalizability to arbitrary codes beyond surface codes. We develop post-selection strategies based on computationally efficient heuristic confidence metrics that leverage error cluster statistics (specifically, aggregated cluster sizes and log-likelihood ratios) from clustering-based decoders, which are applicable to arbitrary quantum low-density parity check (QLDPC) codes. We validate our method through extensive numerical simulations on surface codes, bivariate bicycle codes, and hypergraph product codes, demonstrating orders of magnitude reductions in logical error rates with moderate abort rates. For instance, applying our strategy to the [[144, 12, 12]] bivariate bicycle code achieves approximately three orders of magnitude reduction in the logical error rate with an abort rate of only 1% (19%) at a physical error rate of 0.1% (0.3%). Additionally, we integrate our approach with the sliding-window framework for real-time decoding, featuring early mid-circuit abort decisions that eliminate unnecessary overheads. Notably, its performance matches or even surpasses the original strategy for global decoding, while exhibiting favorable scaling in the number of rounds. Our approach provides a practical foundation for efficient post-selection in fault-tolerant quantum computing with QLDPC codes.

The energy-time uncertainty relation limits the maximum speed of quantum system evolution and is crucial for determining whether quantum tasks can be accelerated. However, multiparticle quantum speed limits have not been experimentally explored. In this work, we experimentally verify that both multiparticles and entanglement can accelerate the quantum speed on observables in two-particle systems based on ultrahigh precision control of quantum evolution time. Furthermore, we experimentally prove that the initial quantum state plays a critical role in the quantum speed limits of the entangled systems. In addition, we experimentally demonstrate that the upper bound and lower bound of the quantum speed are workable even in a nonunitary Markovian open system with two photons. The results obtained based on two-photon experiments have been shown to be generalizable to more particles. Our work facilitates the characterization of the dynamic transient properties of complex quantum systems and the control of the quantum speed of large-scale quantum systems.

Biophotons are non-thermal and non-bioluminescent ultraweak photon emissions, first hypothesised by Gurwitsch in 1924 as a regulatory mechanism in cell division, and then experimentally observed in living organisms. Today, two main hypotheses explain their origin: stochastic decay of excited molecules and coherent electromagnetic fields produced in biochemical processes. Recent interest focuses on the role of biophotons in cellular communication and disease monitoring. This study presents the first campaign of biophoton emission measurements from cultured astrocytes and glioblastoma cells, conducted at Fondazione Pisana per la Scienza (FPS) using two ultra-sensitive setups developed by the collaboration at the National Laboratories of Frascati (LNF-INFN) and at the University of Rome II - Tor Vergata. The statistical analyses of the data collected revealed a clear separation between cellular signals and dark noise, confirming the high sensitivity of the apparatuses. The Diffusion Entropy Analysis (DEA) was applied to the data to uncover dynamic patterns, revealing anomalous diffusion and long-range memory effects potentially related to intercellular signalling and cellular communication. These findings support the hypothesis that biophoton emissions encode rich information beyond intensity, reflecting metabolic and pathological states. The differences that emerged from the application of Diffusion Entropy Analysis to the biophotonic signals of Astrocytes and Glioblastoma are highlighted and discussed in the paper. This work lays the foundation for future studies on neuronal cultures and proposes biophoton dynamics as a promising tool for non-invasive diagnostics and cellular communication research.

In a recent publication, Dalen, and Selstø, Phys. Rev. A 111, 033116 (2025), it was demonstrated how converged photo electron spectra could be determined using a complex absorbing potential on a truncated numerical domain considerably smaller than the extension of the dynamical wave function. That approach required simulation until virtually all unbound parts of the wave function was absorbed, far beyond the duration of the interaction with the external field. In this work we formulate the method in a semi-analytical manner which allows us to extrapolate to infinite times after the interaction with the external field. In addition to obtaining photoelectron spectra for hydrogen differential in energy and ejection angle, we also demonstrate how – and when – the absorber may be seen as a detector, distorting the angular distributions when the detector is placed in the extreme vicinity of the atom.

In this paper, we present an all-optical method to produce shell-shaped traps for ultracold atoms in microgravity. Our scheme exploits optical double dressing of the ground state to create a short range strongly repulsive central potential barrier. Combined with a long range attractive central potential, this barrier forms the shell trap. We demonstrate that a pure spherical bubble, reaching the quasi 2D regime for standard atom numbers, could be formed from two crossed beams with a parabolic profile. An analytical study shows that the relevant characteristics of the trap depend on the ratio of the ground and excited state polarisabilities and the lifetime of the excited state. As a benchmark, we provide quantitative analysis of a realistic configuration for rubidium ensembles, leading to a 250 Hz transverse confinement for a 35 $\mu$m radius bubble and a trap residual scattering rate of less than 10 s$^{-1}$.

We study the use of triorthogonal codes for universal fault-tolerant quantum computation and propose two methods to circumvent the Eastin-Knill theorem, which prohibits any single quantum error-correcting code from supporting both universality and a transversal gate set. We show that our methods reduce the resource overhead compared with existing fault-tolerant protocols. We develop a simple fault-tolerant implementation of the logical Hadamard gate for triorthogonal codes by exploiting the fact that they have transversal controlled-Z (CZ) gates, resulting in a circuit with reduced overhead. We also introduce a procedure for generating a symmetric Calderbank-Shor-Steane code paired with a triorthogonal code, which allows CNOT and CZ gate transversality across the pair of codes. In addition, we present logical state teleportation circuits that transfer encoded states between the two codes, allowing all logical operations to be performed transversally. Our methods can be integrated into the Steane error correction framework without incurring additional resource cost. Finally, using the 15-qubit code as an example, we demonstrate that our protocols significantly reduce the gate overhead compared with other existing methods. These results highlight the potential of combining distinct code structures to achieve low-overhead, universal fault-tolerant quantum computation.

Valley-dependent topological physics offers a promising avenue for designing nanoscale devices based on gapless single-layer graphene. To demonstrate this potential, we investigate an electrical bias-controlled topological discontinuity in valley polarization within a two-segment armchair nanoribbon of gapless single-layer graphene. This discontinuity is created at the interface by applying opposite in-plane, transverse electrical biases to the two segments. An efficient tight-binding theoretical formulation is developed to calculate electron states in the structure. In a reference configuration, we obtain energy eigenvalues and probability distributions that feature interface-confined electron eigenstates induced by the topological discontinuity. Moreover, to elucidate the implications of interface confinement for electron transport, a modified configuration is introduced to transform the eigenstates into transport-active, quasi-localized ones. We show that such states result in Fano "anti-resonances" in transmission spectra. The resilience of these quasi-localized states and their associated Fano fingerprints is examined with respect to fluctuations. Finally, a proof-of-concept band-stop electron energy filter is presented, highlighting the potential of this confinement mechanism and, more broadly, valley-dependent topological physics in designing nanoscale devices in gapless single-layer graphene.

We analyze the tensor network loop cluster expansion, introduced in [G. Park, J. Gray, and G. K.-L. Chan, Phys. Rev. B 112, 174310 (2025)] as a systematic correction to belief propagation, in the context of general quantum many-body problems. We provide numerical examples of the accuracy and practical applicability of the approach for the computation of ground-state observables for high bond dimension tensor networks, in two- and three-dimensions, with open and periodic boundary conditions, and for spin and fermion problems. We find that the contraction error converges approximately exponentially with cluster size, enabling accurate local observable and energy estimates for many systems where standard contraction methods are otherwise impractical.

Quantum thermodynamics investigates how robust the second law of thermodynamics serves as the unique fundamental law in the small quantum world. To tackle this problem, the quantum coherence constitutes a major difficulty of investigations, which provides severe constraints hindering the recovery of a single thermodynamic potential. Here we solve this long-standing problem of quantum information theory by revealing that the state convertibility under thermal operations is fully characterized by the second law of thermodynamics. Specifically, we prove that whether a quantum state with quantum coherence is convertible to another by a thermal operation with a correlated catalyst is completely determined by the free energy ordering. Unlike previous attempts, our setting does not resort to any additional external coherent assist, providing a faithful operational characterization of thermodynamic state transformation.

We extend the generic Bell inequalities suggested by Son, Lee, and Kim [Phys. Rev. Lett. 96, 060406 (2006)] to incorporate multiple observables for tripartite systems and introduce a geometric methodology for calculating classical upper bounds of the inequalities. Our method transforms the problem of finding the classical upper bounds into identifying constraints in linear congruence relations. Using this approach, we derive the upper bounds for scenarios with three and four observables per party. In order to demonstrate quantum violations, we employ Greenberger-Horne-Zeilinger entangled states that can achieve values exceeding the classical upper bounds, with the violation becoming more pronounced as the number of observables increases.

Hybrid quantum systems play a crucial role in advancing scalable and versatile quantum networks as they combine the strengths of different quantum platforms. An important challenge for the development of hybrid quantum networks lies in interfacing heterogeneous quantum nodes and distributing entanglement among them. Single photons emitted from these dissimilar quantum nodes typically show distinct spectral and temporal properties. Therefore, they necessitate spectral filtering and temporal synchronization, which introduce significant photon losses and require additional resources. In this work, we successfully generate indistinguishable photons from two distinct quantum systems of a warm atomic ensemble and a solid-state quantum dot. Remarkably, quantum interference between dissimilar sources is achieved without additional spectral filtering and time synchronization, which enables autonomous quantum nodes for a hybrid quantum network. 133Cs atomic ensemble can efficiently generate heralded single photons at the wavelength of 917 nm of the 6P_(3/2)-6D_(5/2) transition, while the single photons emitted from an InAs/GaAs quantum dot can be tuned to match the 133Cs transition wavelength. Our dense warm atomic ensemble and cavity-coupled quantum dot can efficiently generate bright and resonant single photons at detection rates approaching MHz, respectively. More importantly, these single photons exhibit inherent spectral similarities not only in the wavelength but also in the spectral linewidth, achieving a high spectral overlap of 0.92. Such intrinsic compatibility between dissimilar quantum sources is essential to leverage the advantages of different quantum platforms, paving the way toward a large-scale and functional hybrid quantum network.

The importance of spin-correlated radical pairs in biology is increasingly recognized, with roles in biological effects of weak magnetic fields and emerging quantum spin-based biomedical applications. Fluorescence microscopy offers sufficient sensitivity to study magnetic field effects on radical pair reactions in living cells, but conventional techniques cannot directly resolve their dynamics because most biologically relevant radical pairs are non-emissive. To overcome this challenge, we introduce two novel microscopy techniques: single color pump-probe (PP) and pump-field-probe (PFP) fluorescence. Here, we describe their working principles, provide their mathematical formulation, and validate both techniques through theoretical analysis and experiments on well-established flavin-based magnetic field sensitive reactions. These approaches offer a sensitive and broadly applicable platform for quantifying and visualizing the quantum spin dynamics of radical pair chemical reactions in biological systems.

The evolution of manufacturing toward smart factories has underscored major challenges in equipment maintenance, particularly the dependence on numerous contact sensors for anomaly detection, leading to increased sensor complexity and computational costs. This study explores the use of quantum kernels to enhance anomaly detection based on noncontact sensors. We hypothesize that the expressive power of quantum feature spaces can effectively discriminate among multiple anomaly types using fewer sensors. Experiments were conducted on two types of manufacturing equipment a conveyor and a chain belt machine where a single directional microphone was placed at varying distances (0 to 3 m) to capture audio data. The signals were processed using autoregressive (AR) modeling to extract coefficient based features, which were then mapped into quantum feature space via quantum kernels for one class SVM classification. The quantum kernel classifiers achieved consistently high accuracy and F1 scores (more than 0.92) across all distances, while classical counterparts exhibited significant degradation beyond 0 m. Visualization of the feature space revealed clear separability, with distinct quadrants corresponding to different anomaly types: conveyor anomalies were mainly distributed in the second quadrant, and chain belt anomalies clustered in the fourth. These results demonstrate that quantum kernel methods can achieve robust, multi class anomaly detection in noisy factory environments using minimal non contact sensors. This represents a significant advancement toward quantum enhanced smart factories with reduced sensing infrastructure and improved maintenance efficiency. This work has been accepted for presentation at IEEE QCE25 and will appear in the IEEE Xplore Digital Library.

Given a matrix $A$ of dimension $M \times N$ and a vector $\vec{b}$, the quantum linear system (QLS) problem asks for the preparation of a quantum state $|\vec{y}\rangle$ proportional to the solution of $A\vec{y} = \vec{b}$. Existing QLS algorithms have runtimes that scale linearly with the condition number $\kappa(A)$, the sparsity of $A$, and logarithmically with inverse precision, but often overlook structural properties of $\vec{b}$, whose alignment with $A$'s eigenspaces can greatly affect performance. In this work, we present a new QLS algorithm that explicitly leverages the structure of the right-hand side vector $\vec{b}$. The runtime of our algorithm depends polynomially on the sparsity of the augmented matrix $H = [A, -\vec{b}]$, the inverse precision, the $\ell_2$ norm of the solution $\vec{y} = A^+ \vec{b}$, and a new instance-dependent parameter \[ ET= \sum_{i=1}^M p_i^2 \cdot d_i, \] where $\vec{p} = (AA^{\top})^+ \vec{b}$, and $d_i$ denotes the squared $\ell_2$ norm of the $i$-th row of $H$. We also introduce a structure-aware rescaling technique tailored to the solution $\vec{y} = A^+ \vec{b}$. Unlike left preconditioning methods, which transform the linear system to $DA\vec{y} = D\vec{b}$, our approach applies a right rescaling matrix, reformulating the linear system as $AD\vec{z} = \vec{b}$. As an application of our instance-aware QLS algorithm and new rescaling scheme, we develop a quantum algorithm for solving multivariate polynomial systems in regimes where prior QLS-based methods fail. This yields an end-to-end framework applicable to a broad class of problems. In particular, we apply it to the maximum independent set (MIS) problem, formulated as a special case of a polynomial system, and show through detailed analysis that, under certain conditions, our quantum algorithm for MIS runs in polynomial time.

There exist numerous problems in nature inherently described by finite $D$-dimensional states. Formulating these problems for execution on qubit-based quantum hardware requires mapping the qudit Hilbert space to that of multiqubit which may be exponentially larger. To exclude the infeasible subspace, one common approach relies on penalizing the objective function. However, this strategy can be inefficient as the size of the illegitimate subspace grows. Here we propose to employ the Hamiltonian-based quantum approximate optimization algorithm (QAOA) through devising appropriate mixing Hamiltonians such that the infeasible configuration space is ruled out. We investigate this idea by employing binary, symmetric, and unary mapping techniques. It is shown that the standard mixing Hamiltonian (sum of the bit-flip operations) is the optimal option for symmetric mapping, where the controlled-NOT gate count is used as a measure of implementation cost. In contrast, the other two encoding schemes witness a $p$-fold increase in this figure for a $p$-layer QAOA. We apply this framework to quantum approximate thermalization and find the ground state of the repulsive Bose-Hubbard model in the strong and weak interaction regimes.

The dark-state effect, caused by destructive quantum interference, is an important physical effect in atomic physics and quantum optics. It not only deepens the understanding of light-atom interactions, but also has wide applications in quantum physics and quantum information. Therefore, how to efficiently and conveniently determine the number and form of the dark states in multilevel quantum systems with complex transitions is an important and interesting topic in this field. In this work, we present a general theory for determining the dark states in multilevel quantum systems with any coupling configuration using the arrowhead-matrix method. To confirm the dark states in a multilevel system, we first define the upper- and lower-state subspaces, and then diagonalize the Hamiltonians restricted within the two subspaces to obtain the dressed upper and lower states. By further expressing the transitions between the dressed upper and lower states, we can map the multilevel system to a bipartite-graph network, in which the nodes and links are acted by the dressed states and transitions, respectively. Based on the coupling configurations of the network, we can determine the lower dark states with respect to the upper-state subspace. As examples, we analyze the dark states in three-, four-, and five-level quantum systems, for all possible configurations through the classification of the numbers of upper and lower states. Furthermore, we extend the framework to multilevel quantum systems and discuss the existence of dark states in some typical configurations. We also recover the results of the dark-state polaritons in driven three-level systems with the arrowhead-matrix method. Our theory paves the way for manipulating and utilizing the dark states of multilevel quantum systems in atomic physics and quantum optics.

Measurement-based quantum computation (MBQC) is a strong contender for realizing quantum computers. A critical question for MBQC is the identification of resource graph states that can enable universal quantum computation. Any such universal family must have unbounded entanglement width, which is equivalent to the ability to produce any circle graph state from the states in the family using only local Clifford operations, local Pauli measurements, and classical communication. Yet, it was not previously known whether or not circle graph states themselves are a universal resource. We show that, in spite of their expressivity, circle graph states are not efficiently universal for MBQC (i.e., assuming $\mathsf{BQP} \neq \mathsf{BPP}$). We prove this by articulating a precise graph-theoretic correspondence between circle graph states and a certain subset of fermionic Gaussian states. This is accomplished by synthesizing a variety of techniques that allow us to handle both stabilizer states and fermionic Gaussian states at the same time. As such, we anticipate that our developments may have broader applications beyond the domain of MBQC as well.

For fault-tolerant quantum memory defined by periodic Pauli measurements, called Floquet codes, we prove that every correctable, undetectable spacetime error occurring during the steady stage is a product of (i) measurement operators inserted at the time of the measurement and (ii) pairs of identical Pauli operators sandwiching a measurement that commutes with the operator. We call such errors benign; they define a binary vector subspace of spacetime errors which properly generalize stabilizers of static Pauli stabilizer codes. Hence, the code distance of a Floquet code is the minimal weight of an undetectable spacetime Pauli error that is not benign. Our results apply more generally to families of dynamical codes for which every instantaneous stabilizer is inferred from measurements in a time interval of bounded length.

Bosonic Gaussian unitaries are fundamental building blocks of central continuous-variable quantum technologies such as quantum-optic interferometry and bosonic error-correction schemes. In this work, we present the first time-efficient algorithm for learning bosonic Gaussian unitaries with a rigorous analysis. Our algorithm produces an estimate of the unknown unitary that is accurate to small worst-case error, measured by the physically motivated energy-constrained diamond distance. Its runtime and query complexity scale polynomially with the number of modes, the inverse target accuracy, and natural energy parameters quantifying the allowed input energy and the unitary's output-energy growth. The protocol uses only experimentally friendly photonic resources: coherent and squeezed probes, passive linear optics, and heterodyne/homodyne detection. We then employ an efficient classical post-processing routine that leverages a symplectic regularization step to project matrix estimates onto the symplectic group. In the limit of unbounded input energy, our procedure attains arbitrarily high precision using only $2m+2$ queries, where $m$ is the number of modes. To our knowledge, this is the first provably efficient learning algorithm for a multiparameter family of continuous-variable unitaries.

Electron-photon hybrid systems serve as ideal light-matter interfaces with broad applications in quantum technologies. These systems are typically operated dynamically under nonequilibrium conditions, giving rise to coupled electronic and photonic currents. Understanding the joint fluctuation behavior of these currents is essential for assessing the performance of light-matter interfaces that rely on electron-photon correlations. Here, we investigate the full counting statistics of coupled electronic and photonic currents in an experimentally feasible hybrid system composed of a double quantum dot coupled to an optical cavity. We employ the framework of quantum Lindblad master equation which is augmented with both electronic and photonic counting fields to derive their joint cumulant generating function–a treatment that differs significantly from existing studies, which typically focus on either electron or photon statistics separately. We reveal that the ratio between photonic and electronic currents, as well as their variances, can deviate from an expected quadratic scaling law in the large electron-photon coupling regime. Furthermore, we demonstrate that conventional modelings of photonic dissipation channels in quantum master equations must be modified to ensure that the joint cumulant generating function satisfies the fluctuation symmetry enforced by the fluctuation theorem. Our results advance the understanding of joint fluctuation behaviors in electron-photon hybrid systems and may inform the design of efficient quantum light-matter interfaces.

The existence of self-correcting quantum memories in three dimensions is a long-standing open question at the interface between quantum computing and many-body physics. We take the perspective that large contributions to the entropy arising from fine-tuned spatial symmetries, including the assumption of an underlying regular lattice, are responsible for fundamental challenges to realizing self-correction. Accordingly, we introduce a class of disordered quantum codes, which we call "cored product codes". These codes are derived from classical factors via the hypergraph product but undergo a coring procedure which allows them to be embedded in a lower number of spatial dimensions while preserving code properties. As a specific example, we focus on a fractal code based on the aperiodic pinwheel tiling as the classical factor and perform finite temperature numerical simulations on the resulting three-dimensional quantum memory. We provide evidence that, below a critical temperature, the memory lifetime increases with system size for codes up to 60000 qubits.

We consider state of the art applications of artificial intelligence (AI) in modelling human financial expectations and explore the potential of quantum logic to drive future advancements in this field. This analysis highlights the application of machine learning techniques, including reinforcement learning and deep neural networks, in financial statement analysis, algorithmic trading, portfolio management, and robo-advisory services. We further discuss the emergence and progress of quantum machine learning (QML) and advocate for broader exploration of the advantages provided by quantum-inspired neural networks.

We explore the molecular beam epitaxy synthesis of superconducting aluminum thin films grown on c-plane sapphire substrates at cryogenic temperatures of 6 K and compare their behavior with films synthesized at room temperature. We demonstrate that cryogenic growth increases structural disorder, producing crystalline grains that modify the optical, electrical, and superconducting properties of aluminum. We observe that cryogenic deposition changes the color of aluminum from fully reflective to yellow and correlate the pseudo-dielectric function and reflectance with structural changes in the film. We find that smaller grain sizes enhance the superconductivity of aluminum, increasing its critical temperature and critical field. We then estimate the superconducting gap and coherence length of Cooper pairs in aluminum in the presence of disorder. Finally, we fabricate superconducting microwave resonators on these films and find that, independently of the growth temperature, the system is dominated by two-level system loss with similar quality factors in the high and low power regimes. We further measure a higher kinetic inductance in the cryogenically grown films.

Linear response functions are a cornerstone concept in physics as they enable efficient estimation of many dynamical properties. In addition to predicting dynamics of observables under perturbations without resimulating the system, these response functions lead to electric conductivity, magnetic susceptibility, dielectric constants, etc. Estimating two-time correlation functions is a key ingredient of measuring linear response functions. However, for open quantum systems, simulating the reduced density operator with a quantum master equation only yields one-point observables and is insufficient for this task. In this paper, we develop a memoryless, system-only formulation of two-point correlations for open quantum systems that extends the standard quantum regression theorem (QRT) beyond the Markov limit. We further incorporate the spectral property of the bath and express the time propagators in the response function as the memoryless generators in Lindblad-type forms. The resulting expressions recast the total response function into evolutions generated by time-dependent Hamiltonian and Lindblad primitives together with the more challenging propagation of commutators and anti-commutators. In addition to the derivation of the new QRT, we present quantum algorithms for these primitives and obtain an estimator for two-time correlations whose cost scales poly-logarithmically in the system dimension and $1/\epsilon^{1.25}$ in the target accuracy $\epsilon$. The framework removes the separability (Born-Markov) assumption and offers a pathway to efficient computation of nonequilibrium properties from open quantum systems.

Magnetic response measurements in the presence of AC drive fields provide critical insight into the properties of magnetic and conductive materials, such as phase transitions in two-dimensional van der Waals magnets, the heating efficiency of magnetic nanoparticles in biological environments, and the integrity of metals in eddy current testing. Nitrogen-vacancy (NV) centers in diamond are a commonly-used platform for such studies, due to their high spatial resolution and sensitivity, but are typically limited to weak-drive conditions, i.e., AC drive fields well below the NV microwave (MW) pulse Rabi strength. Once the AC drive field grows comparable to or larger than the Rabi strength, the induced MW pulse detuning suppresses NV sensitivity to the out-of-phase magnetic response, which encodes dissipation and conductivity in materials of interest. Here, we introduce a phase modulation protocol that cancels MW pulse detuning to leading order, and extends NV AC magnetometry into the strong drive field regime. The protocol, termed SIPHT (Signal Isolation through PHase Tuning), is experimentally demonstrated using an NV ensemble. By directly comparing SIPHT to the conventional Hahn echo AC sensing protocol, we quantify the preservation of NV magnetometry contrast for an out-of-phase signal. We further showcase SIPHT by detecting eddy current-induced magnetic fields from Cu, Al, and Ti samples, with the measured response field phase delays reflecting their distinct conductivities. SIPHT extends NV AC magnetometry to regimes inaccessible to standard dynamical decoupling measurement protocols, unlocking novel utility, e.g., in the study of magnetic hyperthermia and nondestructive testing of conductors.