Charge noise is a major obstacle to improved gate fidelities in silicon spin qubits. Numerous methods exist to mitigate charge noise, including improving device fabrication, dynamical decoupling, and real-time parameter estimation. In this work, we demonstrate a class of techniques to mitigate charge noise in semiconductor quantum dots by controlling the noise sources themselves. Using two different classical feedback methods, we stabilize an individual charged two-level fluctuator in a $\mathrm{Si}$/$\mathrm{Si}$-$\mathrm{Ge}$ quantum dot by exploiting sensitive gate-voltage dependence of the switching times. These control methods reduce the low-frequency component of the noise power spectrum by an order of magnitude. These techniques also enable stabilization of the fluctuator in either of its states. In the future, such techniques may enable improved coherence times in quantum dot spin qubits.

Rare-earth-substituted iron garnet (REIG) is an intriguing material for enabling efficient ferrimagnetic spintronics. For potential device applications, it is desired to have a full chart of compensated ferrimagnetism as a function of the rare-earth elements, ideally in the thin-film form of different REIGs. Here, we show the systematic growth of perpendicularly magnetized REIG films (RE = $\mathrm{Gd}$, $\mathrm{Tb}$, $\mathrm{Dy}$, $\mathrm{Ho}$, $\mathrm{Er}$, and $\mathrm{Tm}$) with the same thickness of 15 nm, which is achieved by engineering the strain between the substrates and the REIG thin films. In particular, the evolution of compensated ferrimagnetism, including the saturation magnetization, and compensation temperature as a function of the atomic number (Z) of the ${\mathrm{RE}}^{3+}$ sublattice are studied, which is confirmed by element-specific x-ray absorption spectroscopy and x-ray magnetic circular dichroism measurements. Our work could contribute to the emerging field of ferrimagnetic spintronics by providing a variety of perpendicularly magnetized garnet films with the desired material parameters.

Using a formalism adapted to study transport in quantum open systems, that is, the nonequilibrium Green’s function formalism, we revisit the working principle of the most popular photosynthetic complex, the Fenna Matthews-Olson complex. We underline the driving force behind the exciton flow, which reveals itself in an affine relationship between inflow and outflow currents and exciton populations in the chlorosome and reaction center. We also show that in a realistic parameter regime, two mechanisms, one of which has not previously been proposed, may be at work to protect the reaction center from overheating. The safekeeping mechanism already proposed is closely linked to the exciton-vibration coupling, while the additional mechanism could be triggered by the exciton population on the reaction center.

The transport properties of one-dimensional (1D) ${\mathrm{Ta}\mathrm{Se}}_3$ with lateral size close to the atomic limit are investigated through first-principles calculations. Firstly, the transmission conductances of 1D ${\mathrm{Ta}\mathrm{Se}}_3$ through a mesoscopic region with lattice bending, lattice twisting, and exotic atom adsorption are calculated. We find that a moderate lattice bending or twisting cannot suppress conductance considerably. In contrast, the adsorption of common atomic groups results in a sizable suppression of the conductance. This indicates that a pre-encapsulation is essential for the application of 1D ${\mathrm{Ta}\mathrm{Se}}_3$ as wire in future circuits. More interestingly, 1D ${\mathrm{Ta}\mathrm{Se}}_3$ exhibits triple conductance plateaus under the adjustment of a gate voltage, which implies a promising application as a field-effect transistor. Finally, we calculate the electron-phonon scattering–limited electrical conductivity and thermal conductivity of 1D ${\mathrm{Ta}\mathrm{Se}}_3$ with macroscopic length. We find that these transport coefficients at and above room temperature follow the conventional Wiedemann-Franz law. Moreover, they are comparable to the corresponding values of bulk copper. This indicates that 1D ${\mathrm{Ta}\mathrm{Se}}_3$ is an ideal ultrathin wire for use in the next generation of integrated circuits, instead of noble metals.

Recent experimental findings have revealed exceptionally diverse laser-induced spin dynamics in ferrimagnetic $\mathrm{Gd}$/$\mathrm{Fe}\mathrm{Co}$ multilayers, tunable by magnetic field ($H$) and temperature ($T$); however, a comprehensive theoretical understanding of these phenomena remains lacking. Here, we introduce a two-step workflow that first establishes the complete static $H$-$T$ phase diagram of the system, thereby identifying all distinct magnetic phases. With this solid foundation, we then employ the Landau-Lifshitz-Bloch equation to simulate the full spectrum of spin dynamics, capturing both the transverse and longitudinal responses. This integrated approach not only reproduces helicity-independent all-optical switching at low laser fluences but also reveals the critical role of the exchange relaxation mechanism at higher fluences. Our theoretical predictions show excellent agreement with experimental observations, underscoring the effectiveness of our method in unifying the static and dynamic magnetic behavior of ferrimagnetic systems.

Machine learning and artificial intelligence have recently become a popular paradigm for designing and optimizing robotic systems across various scales. Recent studies have showcased the innovative application of large language models (LLMs) in industrial control and in directing legged walking robots. Here, we use an LLM, Generative Pre-trained Transformer 4 (GPT-4), in most scenarios, to train two prototypical microrobots for swimming in viscous fluids. Adopting a few-shot learning approach, we develop a minimal, unified prompt composed of only five sentences. The same concise prompt successfully guides two distinct articulated microrobots—the three-link swimmer and the three-sphere swimmer—in mastering their signature strokes. These strokes, initially conceptualized by physicists, are now effectively interpreted and applied by the LLM, enabling the microrobots to circumvent the physical constraints inherent to microlocomotion. In addition, we extend our study to the four-link and four-sphere swimmers, enabling their effective propulsion. Remarkably, for this specific task, our LLM-based decision-making strategy substantially outperforms a traditional reinforcement-learning method in terms of training speed. We discuss the subtle aspects of prompt design, particularly emphasizing the reduction of costs when using GPT-4.

Beyond well-known advantages such as robust and unidirectional wave transport, the valley topological edge states (TES) manifest distinctive transverse properties, i.e., the transversely symmetric (TS) and antisymmetric (TAS) properties. The TS and TAS properties provide an alternative choice for controlling wave behavior. In this work, the tunable wave coupling between an external bare plate and a valley topological phononic crystal (PnC) waveguide is investigated, together with the tunable TS and TAS properties. It is directly demonstrated in experiments that the TS and TAS properties of TES can efficiently and flexibly tune the wave coupling. Importantly, the TS and TAS properties encounter an inversion in a sliding valley topological PnC waveguide, where the PnC at one side of the interface slides along the interface. Finally, the TS and TAS properties are shown to support nonreciprocal wave transport, achieving a high asymmetric transport ratio, i.e., 26 times or 28.3 dB, between the forward and backward wave incidences. Our results may enrich the designs of functional phononic devices in the future.

We investigate seismic motion propagation through a passively isolated mechanical system, using Wiener filters and convolutional neural networks with time-dilation layers. The goal of this study was to explore the capabilities of neural networks and Wiener filters in characterizing a mechanical system from the measurements. The mechanical system used is a testbed facility for technology development for current and future gravitational-wave detectors, “VATIGrav”, currently being commissioned at the University of Hamburg. It consists of a large vacuum chamber mounted on four active vibration isolators with an optical table inside, mounted on four passive vibration isolators. In this paper, we have used seismic data recorded on the ground and on the optical table inside the chamber. The data were divided in 6 h for training and another 6 h for validation, focusing on inferring 150-s stretches of time series of table motion from the ground motion in the frequency range from 0.1 Hz to about 50 Hz. We compare the performance of a neural network with fast Fourier transform-based loss function and with Huber loss function to single-input, single-output (SISO) and multiple-input, single-output (MISO) Wiener filters. To be able to compute very large MISO Wiener filters (with 15 000 taps) we have optimized the calculations exploiting block-Toeplitz structure of the matrix in Wiener-Hopf equations. We find that for the given task, SISO Wiener filters outperform MISO Wiener filters, mostly due to low coherence between different motion axes. Neural network trained with Huber loss performs slightly worse than Wiener filters. Neural network with FFT-based loss outperforms Wiener filters in some frequency regions, particularly with low amplitudes and reduced coherence, while it tends to slightly underestimate the peaks, where Wiener filters perform better.

We propose a method to realize microwave-activated cz gates between two remote spin qubits in quantum dots using a charge-sensitive superconducting coupler. The qubits are longitudinally coupled to the coupler, such that the transition frequency of the coupler depends on the logical qubit states; a capacitive network model using first-quantized charge operators is developed to illustrate this. Driving the coupler transition then implements a conditional phase shift on the qubits. Two pulsing schemes are investigated: a rapid, off-resonant pulse with constant amplitude, and a pulse with envelope engineering that incorporates dynamical decoupling to mitigate charge noise. We develop non-Markovian time-domain simulations to accurately model gate performance in the presence of $1/f^{\beta }$ charge noise. Simulation results indicate that a cz gate fidelity exceeding 90% is possible with realistic parameters and noise models.

The dominant contribution to the energy relaxation of state-of-the-art superconducting qubits is often attributed to their coupling to an ensemble of material defects which behave as two-level systems. These defects have varying microscopic characteristics which result in a wide range of observable defect properties such as resonant frequencies, coherence times, and coupling rates to qubits $g$. Here, we investigate strategies to mitigate losses to the family of defects that strongly couple to qubits ($g/2\pi \ge 0.5\mathrm{M}\mathrm{Hz}$). Such strongly coupled defects occur more rarely and are particularly detrimental to the qubit coherence when resonant with the qubit, and to the fidelities of operations relying on frequency excursions, such as flux-activated two-qubit gates. To assess their impact, we perform swap spectroscopy on 92 frequency-tunable qubits and quantify the spectral density of these strongly coupled modes. We show that the frequency configuration of the defects is rearranged by warming the sample to room temperature, whereas the total number of defects on a processor tends to remain constant. We then explore methods for fabricating qubits with a reduced number of strongly coupled defect modes by systematically measuring their spectral density for decreasing Josephson junction dimensions and for various surface cleaning methods. Our results provide insights into the properties of strongly coupled defect modes and show the benefits of minimizing Josephson junction dimensions to improve qubit properties.

The weak spin-orbit coupling (SOC) in bilayer graphene (BLG) is essential for encoding spin qubits while bringing technical challenges for extracting the opened small SOC gap ${\mathrm{\Delta }}_{\mathrm{SO}}$ in experiments. Moreover, in addition to the intrinsic Kane-Mele term, extrinsic mechanisms also contribute to SOC in BLG, especially under experimental conditions including encapsulation of BLG with hexagonal boron nitride (h-BN) and applying an external out-of-plane electric displacement field D. Although measurements of ${\mathrm{\Delta }}_{\mathrm{SO}}$ in h-BN-encapsulated BLG have been reported, the relatively large experimental variations and existing experimental controversy make it difficult to fully understand the physical origin of ${\mathrm{\Delta }}_{\mathrm{SO}}$. Here, we report a combined experimental and theoretical study on ${\mathrm{\Delta }}_{\mathrm{SO}}$ in h-BN-encapsulated BLG. We use an averaging method to extract ${\mathrm{\Delta }}_{\mathrm{SO}}$ in gate-defined single quantum dot devices. Under D fields as large as 0.57–0.90 V/nm, ${\mathrm{\Delta }}_{\mathrm{SO}}$ ≈ 53.4–61.8 $\text{μ}$eV is obtained from two devices. Benchmarked with values reported at lower D field regime, our results support a D-field-independent ${\mathrm{\Delta }}_{\mathrm{SO}}$. This behavior is confirmed by our first-principles calculations, based on which ${\mathrm{\Delta }}_{\mathrm{SO}}$ is found to be independent of D field, regardless of different h-BN/BLG/h-BN stacking configurations. Our calculations also suggest a weak proximity effect from h-BN, indicating that SOC in h-BN-encapsulated BLG is dominated by the intrinsic Kane-Mele mechanism. Our results offer insightful understanding of SOC in BLG, which benefits SOC engineering and spin manipulations in BLG.

One of the main applications of electromagnetic metasurfaces (MSs) is to tailor spatial field distributions. The radiation pattern of a given source can be desirably modified by reflection from an MS having appropriate spatial modulation of its local macroscopic parameters. At the microscopic level, spatial modulation requires individually engineered meta-atoms at different points. In contrast, the present research demonstrates the opportunity for radiation-pattern engineering in the reflection regime without using any spatial modulation. The approach involves the deliberate tailoring of the surface impedance of a uniform but spatially dispersive (nonlocal) MS composed of identical meta-atoms. A two-dimensional synthesis problem with a magnetic line current source is solved analytically by finding the required form of the surface impedance as a function of the tangential wave vector in both the visible and evanescent parts of the spatial spectrum. To validate this concept, three different pattern shapes are implemented via full-wave numerical simulations by tuning the spatial dispersion in a realistic mushroom-type high-impedance electromagnetic surface with loaded vias. This work extends the synthesis methods and application area of spatially dispersive MSs, demonstrating that they represent a promising platform for future antenna designs.

We demonstrate the accurate determination of the magnetic strength of a microwave field utilizing the dressed cesium atom as a sensor. A bias magnetic field is initially used to split the Zeeman levels of the ground state. Its value is carefully selected to ensure that the microwave field to be measured is closely resonant to a pair of selected hyperfine Zeeman levels ($|F=3,m_F=3⟩$ and $|F=4,m_F=4⟩$), while other states are neglected. This approach significantly simplifies the interaction model. A weak radio-frequency magnetic field is utilized to drive atoms away from the specified magnetic quantum number state to its neighbor, whose population imbalance is supervised by optical readout. The long atomic transverse relaxation time due to the antirelaxation coatings of alkali-metal-vapor cells makes it possible to precisely depict the interaction between atoms and the microwave field. Consequently, the microwave field can be accurately determined with an uncertainty of less than 1% even beyond 100 nT, corresponding to a radio-frequency spectral line broadening less than 100 Hz. The features we have identified enable us to expand the possibilities of developing new metrology for measuring microwave fields in an alternative way.

Conventional continuous quantum heat engines with incoherent heat transfer perform poorly as they exploit two-body interactions between the system and hot or cold baths, thus having limited capability to outperform their classical counterparts. We introduce distinct continuous quantum heat engines that utilize coherent heat transfer with baths, yielding genuine quantum enhancement in performance. These coherent engines consist of one qutrit system and two photonic baths and enable coherent heat transfer via two-photon transitions involving three-body interactions between the system and hot and cold baths. We demonstrate that coherent engines deliver significantly higher power output with much greater reliability, i.e., lower signal-to-noise ratio of the power, by hundreds of folds over their incoherent counterparts. Importantly, coherent engines can operate close to or at the maximal achievable reliability allowed by the quantum thermodynamic uncertainty relation. Moreover, coherent engines manifest more nonclassical features than incoherent engines because they violate the classical thermodynamic uncertainty relation by a greater amount and for a wider range of parameters. These genuine enhancements in the performance of coherent engines are directly attributed to their capacity to harness higher energetic coherence for the resonant driving case. The experimental feasibility of coherent engines and the improved understanding of how quantum properties can enhance performance may find applications in quantum enabled technologies.

Coherent control via laser pulses, including stimulated Raman adiabatic passage and rapid adiabatic passage, has been extensively studied for robust and selective population transfer of quantum states. Notably, the classical analog of stimulated Raman adiabatic passage has been successfully implemented in optical and acoustic wave transfers within waveguide systems. In this study, we numerically and experimentally investigate the adiabatic transfer of acoustic waves in a composite waveguide structure that serves as the acoustic analog of a quantum rapid adiabatic passage. The composite waveguide structure consists of two waveguide units and supports two odd-symmetric supermodes. The supermode propagation constant can be modulated by adjusting the coupling strength between the paired waveguides within each individual waveguide unit. Under a constant detuning of the supermode propagation constants, the adiabatic passage results in a complete return of acoustic waves in the excited waveguide unit. In the case of chirped detuning, an adiabatic transfer of acoustic waves from the excited waveguide unit to another one is achieved. Our proposal presents opportunities for anomalous manipulation of acoustic waves, which may have potential applications in wave splitting, focusing, and mode conversion.

Dual-band second-order photonic topological insulators (SPTIs), characterized by multiple corner states, offer exceptional potential for robust multiband manipulation of electromagnetic waves in lower dimensions. However, previous theoretical implementations of dual-band SPTIs have been constrained by limited flexibility, hindering their adaptability for diverse applications. Additionally, experimental validation of these dual-band SPTIs has been lacking. In this study, we introduce a topology optimization method to design SPTIs with customizable dual-band corner states tailored for specific multiband applications. Dual-band SPTIs with customized corner states for manipulating electromagnetic waves of transverse magnetic and electric modes are created. Furthermore, multiple corner states within dual bandgaps are experimentally confirmed. This work opens new avenues for creating customizable photonic devices with dual-band topological features, enabling versatile multiband applications.

In the superparamagnetic regime, magnetic tunnel junctions switch between two resistance states due to random thermal fluctuations. The dwell-time distribution in each state is exponential. We sample this distribution using a temporal encoding scheme, in which information is encoded in the time at which the device switches between its resistance states. We then develop a circuit element known as a probabilistic delay cell that applies an electrical current step to a superparamagnetic tunnel junction and a temporal measurement circuit that measures the timing of the first switching event. Repeated experiments confirm that these times are exponentially distributed. Temporal processing methods then allow us to digitally compute with these exponentially distributed probabilistic delay cells. We describe how to use these circuits in a Metropolis-Hastings stepper and in a weighted random sampler, both of which are computationally intensive applications that benefit from the efficient generation of exponentially distributed random numbers.

We introduce several probabilistic quantum algorithms that overcome the normal unitary restrictions in quantum machine learning by leveraging the linear combination of unitaries (LCU) method. We cover three distinct topics, beginning with quantum native implementations of residual networks (ResNets). We demonstrate that while residual connections between layers of a variational Ansatz can prevent barren plateaus in models, this approach is accompanied by a trade-off in success probability. Second, we implement a quantum analogue of average-pooling layers from convolutional networks using single-qubit-controlled basic arithmetic operators and show that the LCU success probability remains stable for the Modified National Institute of Standards and Technology (MNIST) database. This method can be further generalized to convolutional filters, while using exponentially fewer controlled unitaries than previous approaches. Finally, we propose a general framework for applying a linear combination of irreducible-subspace projections on quantum encoded data for any finite group. This enables a quantum state to remain within an exponentially large space, while selectively amplifying specific subspaces relative to others, alleviating simulability concerns that arise when fully projecting to a polynomially sized subspace. We demonstrate improved classification performance for partially amplified permutation-invariant encoded point-cloud data when compared to noninvariant or fully permutation-invariant encodings. We also demonstrate a novel rotationally invariant encoding for point-cloud data via Schur-Weyl duality. These quantum computing frameworks are all constructed using the LCU method, suggesting that further novel quantum machine-learning (QML) algorithms could be created by utilizing the LCU technique.

The control of flying qubits carried by itinerant photons is ubiquitous in quantum networks. In addition to their logical states, the shapes of flying qubits must also be tailored for high-efficiency information transmission. In this paper, we introduce quantum optimal control theory to the shaping of flying qubits. Building on the flying-qubit control model established in our previous work, we design objective functionals for the generation of shaped flying qubits under practical constraints on the emitters and couplers. Numerical simulations employing gradient-descent algorithms demonstrate that the optimized control can effectively mitigate unwanted level and photon leakage caused by these nonidealities. Notably, while coherent control offers limited shaping capacity with a fixed coupler, it can significantly enhance the shaping performance when combined with a tunable coupler that has restricted tunability. The proposed optimal control framework provides a systematic approach to achieving high-quality control of flying qubits using realistic quantum devices.