Weak measurements of a superconducting qubit produce noisy voltage signals that are weakly correlated with the qubit state. To recover individual quantum trajectories from these noisy signals, traditional methods requ...
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Néel skyrmions are generally realized in asymmetric multilayers made of heavy metals (HMs) and ultrathin ferromagnets possessing strong interfacial Dzyaloshinskii-Moriya interactions (iDMIs). Depending on the rel...
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Néel skyrmions are generally realized in asymmetric multilayers made of heavy metals (HMs) and ultrathin ferromagnets possessing strong interfacial Dzyaloshinskii-Moriya interactions (iDMIs). Depending on the relative strengths of iDMIs at the interfaces, various types of Néel skyrmions have been suggested, which are typified with characteristically different topological properties and current-driven dynamics. This suggests the importance of a precise quantification of their spin chiralities. In this paper, we explore the possibility of realizing Néel skyrmions in magnetic multilayers without the direct usage of standard HMs. Specifically, through depositing a thin layer of ferrimagnetic (FIM) CoTb layer on top of an antiferromagnetic (AFM) quantum material of composition Mn3Sn, the AFM exchange interaction at the asymmetric interface provides an equivalent iDMI for stabilizing FIM Néel skyrmions. Secondly, through using advanced four-dimensional Lorentz scanning transmission electron microscopy (4D LSTEM), in combination with x-ray magnetic circular dichroism photoemission electron microscopy (XMCD-PEEM), we can directly determine the spin chirality of FIM Néel skyrmions. The present findings not only broaden the phase space for chiral interfacial magnetism but also provide a possibility for future applications of heavy-metal-free skyrmionic devices.
Networking superconducting quantum computers is a longstanding challenge in quantum science. The typical approach has been to cascade transducers: converting to optical frequencies at the transmitter and to microwave ...
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We show that relatively simple integrated photonic circuits have the potential to realize a high fidelity deterministic controlled-phase gate between photonic qubits using bulk optical nonlinearities. The gate is enab...
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We show that relatively simple integrated photonic circuits have the potential to realize a high fidelity deterministic controlled-phase gate between photonic qubits using bulk optical nonlinearities. The gate is enabled by converting travelling continuous-mode photons into stationary cavity modes using strong classical control fields that dynamically change the effective cavity-waveguide coupling rate. This architecture succeeds because it reduces the wave packet distortions that otherwise accompany the action of optical nonlinearities [J. Shapiro, Phys. Rev. A 73, 062305 (2006); J. Gea-Banacloche, Phys. Rev. A 81, 043823 (2010)]. We show that high-fidelity gates can be achieved with self-phase modulation in χ(3) materials as well as second-harmonic generation in χ(2) materials. The gate fidelity asymptotically approaches unity with increasing storage time for an incident photon wave packet with fixed duration. We also show that dynamically coupled cavities enable a trade-off between errors due to loss and wave packet distortion. Our proposed architecture represents a new approach to practical implementation of quantum gates that is room-temperature compatible and only relies on components that have been individually demonstrated.
We study theoretically the interaction between two photons in a nonlinear cavity. The photons are absorbed into the cavity by an effective tuning of its input-output coupling via external control of a coupling to a se...
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We study theoretically the interaction between two photons in a nonlinear cavity. The photons are absorbed into the cavity by an effective tuning of its input-output coupling via external control of a coupling to a second, strongly output-coupled cavity mode. Such “dynamically coupled” cavities, which can be implemented using bulk χ(2) and χ(3) nonlinearities, enable incoming photon wave packets to be absorbed into the cavity with high fidelity when the duration of the control is similar to that of the wave packets. Further, this configuration can be used to avoid limitations in the photon-photon interaction time set by the delay-bandwidth product of passive cavities and enables the elimination of wave-packet distortions caused by dispersive cavity transmission and reflection. We consider three kinds of nonlinearities, two arising from χ(2) and χ(3) materials and one due to an interaction with a two-level emitter. To analyze the input and output of few-photon wave packets, we use a Schrödinger-picture formalism in which traveling-wave fields are discretized into infinitesimal time bins. We suggest that dynamically coupled cavities provide a very useful tool for improving the performance of quantum devices relying on cavity-enhanced light-matter interactions such as single-photon sources and atomlike quantum memories with photon interfaces. As an example, we present simulation results showing that high-fidelity two-qubit entangling gates may be constructed using any of the considered nonlinear interactions.
Advances in biomarkers and pathophysiology of vascular contributions to cognitive impairment and dementia (VCID) are expected to bring greater mechanistic insights, more targeted treatments, and potentially disease-mo...
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Advances in biomarkers and pathophysiology of vascular contributions to cognitive impairment and dementia (VCID) are expected to bring greater mechanistic insights, more targeted treatments, and potentially disease-modifying therapies. The 2025 Annual Workshop of the Albert research Institute for White Matter and Cognition, sponsored by the Leo and Anne Albert Charitable Trust since 2015, focused on novel biomarkers for VCID. The meeting highlighted the complexity of dementia, emphasizing that the majority of cases involve multiple brain pathologies, with vascular pathology typically present. Potential novel approaches to diagnosis of disease processes and progression that may result in VCID included measures of microglial senescence and retinal changes, as well as artificial intelligence (AI) integration of multimodal datasets. Proteomic studies identified plasma proteins associated with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL; a rare genetic disorder affecting brain vessels) and age-related vascular pathology that suggested potential therapeutic targets. Blood-based microglial and brain-derived extracellular vesicles are promising tools for early detection of brain inflammation and other changes that have been associated with cognitive decline. Imaging measures of blood perfusion, oxygen extraction, and cerebrospinal fluid (CSF) flow were discussed as potential VCID biomarkers, in part because of correlations with classic pathological Alzheimer's disease (AD) biomarkers. MRI-visible perivascular spaces, which may be a novel imaging biomarker of sleep-driven glymphatic waste clearance dysfunction, are associated with vascular risk factors, lower cognitive function, and various brain pathologies including Alzheimer's, Parkinson's and cerebral amyloid angiopathy (CAA). People with Down syndrome are at high risk for dementia. Individuals with Down syndrome who develop dementia almost universally experience mi
The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) comput...
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The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) computing, systematic miscalibrations, drift, and crosstalk in the control of qubits can lead to a coherent form of error that has no classical analog. Coherent errors severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable quantum computations. Moreover, the average error rates measured by randomized benchmarking and related protocols are not sensitive to the full impact of coherent errors and therefore do not reliably predict the global performance of quantum algorithms, leaving us unprepared to validate the accuracy of future large-scale quantum computations. Randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error rates measured via cycle benchmarking. In this work, we demonstrate significant performance gains under randomized compiling for the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. Additionally, we accurately predict algorithm performance using experimentally measured error rates. Our results demonstrate that randomized compiling can be utilized to leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing.
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from t...
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.
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