Two types of errors can occur when discriminating pairs of quantum states. Asymmetric state discrimination involves minimizing the probability of one type of error, subject to a constraint on the other. We give explicit expressions bounding the set of achievable errors, using the trace norm, the fidelity, and the quantum Chernoff bound. The upper bound is asymptotically tight and the lower bound is exact for pure states. Unlike asymptotic bounds, our bounds give error values instead of exponents, so can give more precise results when applied to finite-copy state discrimination problems.

We propose a scheme for optical entanglement distribution in quantum networks based on a quasideterministic entangled photon-pair source. By combining heralded photonic Bell-pair generation with spectral mode conversion to interface with quantum memories, the scheme eliminates switching losses due to multiplexing in the source. We analyze this “zero-added-loss multiplexing” (ZALM) Bell-pair source for the particularly challenging problem of long-baseline entanglement distribution via satellites and ground-based memories, where it unlocks additional advantages: (i) the substantially higher channel efficiency $\eta$ of downlinks versus uplinks with realistic adaptive optics, and (ii) photon loss occurring before interaction with the quantum memory—i.e., Alice and Bob receiving rather than transmitting—improve entanglement generation rate scaling by $\mathcal{O}(\sqrt{\eta })$. Based on numerical analyses, we estimate our protocol to achieve $>10\text{ebit/s}$ at memory multiplexing of ${10}^2$ spin qubits for ground distance $>{10}^2\text{km}$, with the spin-spin Bell-state fidelity exceeding 99%. Our architecture presents a blueprint for realizing global-scale quantum networks in the near term.

Single-particle imaging (SPI) at x-ray free-electron lasers is a technique to determine the three-dimensional structure of nanoscale objects like biomolecules from a large number of diffraction patterns of copies of these objects in random orientations. The technique has been limited to relatively low resolution due to background noise and heterogeneity of the target particles. A recently introduced reference-enhanced holographic SPI methodology uses strongly scattering holographic references to improve background tolerance, and, thus, the achievable resolution, at the cost of additional latent variables beyond orientation. Here, we describe an improved reconstruction algorithm based on maximum likelihood estimation, which scales better, enabling fine sampling of latent parameters to reach high resolutions, and much better performance in the low signal limit. Furthermore, we show that structural variations within the target particle are averaged in real space, significantly improving robustness to conformational heterogeneity in comparison to conventional SPI. With these computational improvements, we believe reference-enhanced SPI is capable of reaching sub-nanometer resolution biomolecule imaging.

Using positive muons as local probes implanted at low energy enables gathering information about the material of interest with nanometer-depth resolution (low-energy muon-spin-rotation spectroscopy, LE $\mu \mathrm{SR}$). In this work, we leverage the capabilities of LE $\mu \mathrm{SR}$ to perform a detailed investigation of the widely studied yet poorly understood ${\mathrm{Si}\mathrm{O}}_2$-$\mathrm{Si}\mathrm{C}$ interface. Thermally oxidized samples are investigated before and after annealing in nitric oxide (NO) and argon ($\mathrm{Ar}$) environment. Thermal oxidation is found to result in structural changes both in the $\mathrm{Si}\mathrm{C}$ crystal close to the interface and at the interface itself, which severely degrade the transport properties of charge carriers. Annealing in NO environment is known to passivate the defects leading to a reduction of the density of interface traps ($D_{\mathrm{it}}$); LE $\mu \mathrm{SR}$ further reveals that the NO annealing results in a thin layer of high carrier concentration in $\mathrm{Si}\mathrm{C}$, extending to more than 50 nm depending on the annealing conditions. From our measurements, we see indications of $\mathrm{Si}$ vacancy ($V_{\mathrm{Si}}$) formation in $\mathrm{Si}\mathrm{C}$ after thermal oxidation. Following NO annealing, nitrogen occupies the $V_{\mathrm{Si}}$ sites, leading to the well-documented reduction in $D_{\mathrm{it}}$ and, at the same time, creating a charge-carrier-rich region near the interface. The LE $\mu \mathrm{SR}$ technique sheds light on the near-interface region in the ${\mathrm{Si}\mathrm{O}}_2$-$\mathrm{Si}\mathrm{C}$ system, which is challenging to access using other techniques. By comparing the LE $\mu \mathrm{SR}$ data from a sample with known doping density, we perform a high-resolution quantification of the free carrier concentration near the interface after NO annealing and discuss the origin of the observed near-surface variations. Finally, the depletion of carriers in a MOS capacitor in the region exactly below the interface is demonstrated using LE $\mu \mathrm{SR}$. The NO-annealed sample shows the narrowest depletion region, likely due to the reduced density of interface traps and the charge-carrier-rich region near the interface. Our findings demonstrate the many benefits of LE $\mu \mathrm{SR}$ to study critical regions of semiconductor devices that have been inaccessible with other techniques, while simultaneously retaining both nanoscale-depth resolution and a nondestructive approach.

Thermoelectric materials have attracted considerable interest for energy applications such as waste-heat recovery and energy harvesting to power Internet-of-things sensors. In recent decades, an increasing number of different strategies to increase performance have been invented and tested, including the synthesis of thin films and other high-performance multilayered structures. Although it has already been shown that the pure combination of the properties of each layer without interactions will yield worse performance compared to the best layer, a critical estimation of the size of the deviation to trace back individual properties is still missing. In this paper we derive a set of formulas to describe the total Seebeck coefficient, electrical and thermal conductivity, power factor, and $zT$ value of a two-layer system from a simple model and elucidate the origin and size of the contribution of each layer to the total thermoelectric performance. We further show that the influence of the substrate can lead to large deviation between the measured and the film’s properties, advising caution when analyzing such systems. Moreover, this model allows one to ensure that the contribution of the substrate is below a desired threshold by introducing material-related quantities ${\epsilon }_{\sigma }$ and ${\epsilon }_{\lambda }$.

Quantum machine learning has the potential to provide powerful algorithms for artificial intelligence. The pursuit of quantum advantage in quantum machine learning is an active area of research. For current noisy intermediate-scale quantum computers, various quantum-classical hybrid algorithms have been proposed. One such previously proposed hybrid algorithm is a gate-based variational embedding classifier, which is composed of a classical neural network and a parameterized gate-based quantum circuit. We propose a quantum variational embedding classifier based on an analog quantum computer, where control signals vary continuously in time: our particular focus is an implementation using quantum annealers. In our algorithm, the classical data are transformed into the parameters of the time-varying Hamiltonian of the analog quantum computer by a linear transformation. The nonlinearity needed for a nonlinear classification problem is purely provided by the analog quantum computer through the nonlinear dependence of the final quantum state on the control parameters of the Hamiltonian. We perform numerical simulations that demonstrate the effectiveness of our algorithm for performing binary and multiclass classification on linearly inseparable datasets such as concentric circles and MNIST digits. Our classifier can reach accuracy comparable with that of the best classical classifiers. We find that the performance of our classifier can be increased by increasing the number of qubits, until the performance saturates and fluctuates. Moreover, the number of optimization parameters of our classifier scales linearly with the number of qubits. The increase of the number of training parameters when the size of our model increases is therefore not as fast as that of a neural network. Our algorithm presents the possibility of using current quantum annealers for solving practical machine-learning problems, and it could also be useful to explore quantum advantage in quantum machine learning.

In view of the long-standing controversy over the surface ferromagnetism of $\mathrm{Fe}\mathrm{O}$, we propose to prepare its nanoparticles based on the lithium-battery reaction mechanism and carry out operando magnetometry measurements. This operando magnetoelectrochemical technology allows accurate magnetic detection of electrochemical synthesis products, effectively avoiding various intractable surface reactions during sample transmission processes. Due to the large surface area of nanosized $\mathrm{Fe}\mathrm{O}$, the magnetometry measurements show strong ferromagnetism and a high Curie temperature of 754 K, which can be attributed to surface frustration and the surface-bulk exchange interaction. These critical results confirm the surface ferromagnetism of $\mathrm{Fe}\mathrm{O}$ and provide valuable insights toward the design of spintronic devices.

Superconducting interferometers are quantum devices able to transduce a magnetic flux into an electrical output with excellent sensitivity, integrability, and power consumption. Yet, their voltage response is intrinsically nonlinear, a limitation which is conventionally circumvented through the introduction of compensation inductances or by the construction of complex device arrays. Here we propose an intrinsically linear flux-to-voltage mesoscopic transducer, exploiting the superconducting quantum interference proximity transistor (SQUIPT) as a fundamental building block, called bi-SQUIPT. It provides a voltage-noise spectral density as low as approximately ${10}^{-16}{\mathrm{V}/\mathrm{Hz}}^{1/2}$ and, more interestingly, under a proper operation parameter selection, exhibits a spur-free dynamic range as large as approximately $60$ dB, a value on par with that obtained with state-of-the-art linear flux-to-voltage superconducting transducers based on superconducting quantum interference devices (SQUIDs). Furthermore, thanks to its peculiar measurement configuration, the bi-SQUIPT is tolerant to imperfections and nonidealities in general. For the above reasons, we believe that the bi-SQUIPT could provide a relevant step beyond in the field of low-dissipation and low-noise current amplification with a special emphasis on applications in cryogenic quantum electronics.

The architecture of artificial atoms coupled to superconducting cavities allows the study of light-matter interactions and intriguing phenomena such as cavity gain implying photon generation. Here, we integrate a high-impedance cavity with a double quantum dot (DQD) in GaAs/(Al,Ga)As heterostructures, achieving a considerable DQD-cavity coupling strength and a relatively small cavity decay rate. By applying a strong drive to the DQD, we realize a population inversion and observe the cavity amplitude gain in multiple regions in the measured Landau-Zener-Stückelberg-Majorana interference pattern. We further systematically investigate the dependence of cavity gain on the driving frequency and tunnel coupling strength of the DQD. The results show that the cavity gain is tunable, with a maximum value of approximately $1.16$ in the measured range. Our experimental results are in good agreement with theoretical simulations and may provide an opportunity to implement on-chip microwave sources or microwave amplifiers in a controllable way.

We report on the development of a system called velocimetry by actively stabilized coherent optical transfer (VASCOT) that uses active optical phase tracking to measure the in-line velocity of retroreflecting targets with narrow-band photodetection. VASCOT is experimentally demonstrated over a 584-m atmospheric link to a corner-cube retroreflector (CCR) on an airborne drone. The in-line target-velocity measurement achieves residual uncertainties below $2{\text{nm s}}^{-1}$ within 5 s of averaging. Cycle-slip-free phase tracking over the full 3-min experiment allows the relative target range to be tracked with a one-standard-deviation uncertainty of 39.5 nm. VASCOT also provides an absolute target-range measurement with a statistical error of $\pm 13.7\mathrm{mm}$, which agrees with an independent Global-Positioning-System- (GPS) derived range measurement to within the GPS uncertainty.

The energy loss of hole-charge transfer in nonfullerene-acceptor (NFA) organic solar cells has been significantly reduced by minimizing their interfacial energy offset to near-zero and negative values. However, the physical mechanism of highly efficient hole-charge transfer in these conditions has not been fully addressed. In this work, we theoretically clarify that a moving hole charge along the polymer-donor and NFA interface, generated from the spontaneous dissociation of NFA molecular excited states, tends to transfer from NFA molecules to polymer-donor to keep moving, even their interfacial energy offset is chosen as a negative value. This dynamic viewpoint revises the conventional static viewpoint about the dependence of hole-charge transfer upon the interfacial energy offset. In addition, considering the polaron effect in organic molecules with a hole charge generated, we redefine the interfacial energy offset and further consider the effects of the NFA molecular electron-lattice interaction and lattice elastic constant on the hole-charge transfer.

An optical lattice is a platform that is used in studies in multiple branches of physics, e.g., ultracold atoms and microfluidic sorting. Unlike traditional complex optical systems, surface plasmon polaritons enable the generation of optical lattices on a chip. Here, we propose an on-chip scheme to transform five topological optical lattices using tunable polarization and a phase vortex. Using the dipolar approximation and numerical simulations, we demonstrate that the spin density force is the main force for sorting chiral particles in the out-of-plane direction when their radius is more than 100 nm. A microflow system is proposed to trap particles with electric optical lattices and sort chiral particles with spin optical lattices. Our findings offer possible routes toward realization of on-chip optical lattices and enable their potential application in both biology and chemistry, e.g., in simultaneous trapping or excitation of multiple objects and identification and sorting of chiral objects.

Quantum nondemolition (QND) measurement enhances the detection efficiency and measurement fidelity, and is highly desired for its applications in precision measurements and quantum information processing. We propose and demonstrate a QND measurement scheme for the spin states of laser-trapped atoms. On ${}^{171}\mathrm{Yb}$ atoms held in an optical dipole trap, a transition that is simultaneously cycling, spin-selective, and spin-preserving is created by introducing a circularly polarized beam of a control laser to optically dress the spin states in the excited level, while leaving the spin states in the ground level unperturbed. We measure the phase of spin precession of $5\times {10}^4$ atoms in a bias magnetic field of 20 mG. This QND approach reduces the optical absorption detection noise by $\sim 19\mathrm{dB}$, to an inferred level of 2.3 dB below the atomic quantum projection noise. In addition to providing a general approach for efficient spin-state readout, this all-optical technique allows quick switching and real-time programming for quantum sensing and quantum information processing.

Simultaneously tracking and imaging moving objects has been an open problem, due to the requirement on a large amount of information and the low photon flux during the very moment that the object can be taken as stationary. Information of the moving object can be decomposed into motion information of the centroid and image of the object. The amount of motion information can be far less than that for imaging. Therefore, we propose to solve the problem by information segregating. That is, to obtain the motion trajectory and gradually achieve the image during the tracking process. As an example, we unify tracking and imaging within a single system, based on ghost imaging with a four-quadrant detector. The differential signals from the quadrant detector are used for tracking, and the sum signals are for imaging. Experimental results show that our method provides high tracking accuracy and high-quality imaging. For objects of angular velocity up to 165 mrad/s, the tracking accuracy reaches 1/7 of the imaging resolution. Possible influences from different practical factors are also discussed.

The two-dimensional intrinsic ferromagnet $\mathrm{Cr}\mathrm{S}\mathrm{Cl}$ monolayer has considerable potential for application in the development of spintronic devices because of properties such as robust ferromagnetic ordering, large spin polarization, high Curie temperature, and high carrier mobilities. Here, we investigate the electromagnetic properties of the $\mathrm{Cr}\mathrm{S}\mathrm{Cl}$ monolayer and the spin-transport properties of some conceptual magnetic devices we construct, such as p-n-junction diodes, field-effect transistors, and phototransistors, by means of first-principles calculations. The results indicate that the p-n-junction diodes of the $\mathrm{Cr}\mathrm{S}\mathrm{Cl}$ monolayer exhibit full spin-polarized transport behavior, and both the p-n and p-i-n junctions show excellent spin-filtering behavior. The phototransistor of the $\mathrm{Cr}\mathrm{S}\mathrm{Cl}$ monolayer exhibits spin-resolved photoresponse characteristics for different wavelengths of light. Furthermore, it possesses the ability to generate a fully spin-up polarized current in the visible range. Our results provide key insights into the fundamental physical properties and the underlying transport and photoresponse mechanisms of the $\mathrm{Cr}\mathrm{S}\mathrm{Cl}$ monolayer.

We propose a quantum algorithm for simulating dissipative waves in inhomogeneous linear media as a boundary-value problem. Using the so-called quantum singular value transformation (QSVT), we construct a quantum circuit that models the propagation of electromagnetic waves in a one-dimensional system with outgoing boundary conditions. The corresponding measurement procedure is also discussed. Limitations of the QSVT algorithm are identified in connection with the large condition numbers that the dispersion matrices exhibit at weak dissipation.