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A distributed quantum computing network requires a quantum communication channel between spatially separated processing units. In superconducting circuits, such a channel can be implemented based on propagating microwave photons to encode and transfer quantum information between an emitter and a receiver. However, traveling microwave photons can be lost during the transmission, leading to the failure of information transfer. Heralding protocols can be used to detect such photon losses. In this work, we propose such a protocol and experimentally demonstrate a frequency-bin encoding method of microwave photonic modes using superconducting circuits. We deterministically encode the quantum information from a superconducting qubit by simultaneously emitting its information into two photonic modes at different frequencies, with a process fidelity of 90.4%. The frequency-bin-encoded photonic modes can be used, at the receiver processor, to detect the occurrence of photon loss. Our work thus provides a reliable method to implement high-fidelity quantum state transfer in a distributed quantum computing network, incorporating error detection to enhance performance and accuracy.

In this paper, we develop an uncertainty-aware decision-making and motion-planning method for an autonomous ego vehicle in forced merging scenarios, considering the motion uncertainty of surrounding vehicles. The method dynamically captures the uncertainty of surrounding vehicles by online estimation of their acceleration bounds, enabling a reactive but rapid understanding of the uncertainty characteristics of the surrounding vehicles. By leveraging these estimated bounds, a non-conservative forward occupancy of surrounding vehicles is predicted over a horizon, which is incorporated in both the decision-making process and the motion-planning strategy, to enhance the resilience and safety of the planned reference trajectory. The method successfully fulfills the tasks in challenging forced merging scenarios, and the properties are illustrated by comparison with several alternative approaches., Comment: Accepted by the 63rd IEEE Conference on Decision and Control, 2024

In quantum computing, precise control of system-environment coupling is essential for high-fidelity gates, measurements, and networking. We present an architecture that employs an artificial giant atom from waveguide quantum electrodynamics to tailor the interaction between a superconducting qubit and its environment. This frequency-tunable giant atom exhibits both frequency and power selectivity for photons: when resonant with the qubit, it reflects single photons emitted from the qubit while remaining transparent to strong microwave signals for readout and control. This approach surpasses the Purcell limit and significantly extends the qubit's lifetime by ten times while maintaining the readout speed, thereby improving both gate operations and readout. Our architecture holds promise for bridging circuit and waveguide quantum electrodynamics systems in quantum technology applications.

Quantum computing has the potential to revolutionize multiple fields by solving complex problems that can not be solved in reasonable time with current classical computers. Nevertheless, the development of quantum computers is still in its early stages and the available systems have still very limited resources. As such, currently, the most practical way to develop and test quantum algorithms is to use classical simulators of quantum computers. In addition, the development of new quantum computers and their components also depends on simulations. Given the characteristics of a quantum computer, their simulation is a very demanding application in terms of both computation and memory. As such, simulations do not scale well in current classical systems. Thus different optimization and approximation techniques need to be applied at different levels. This review provides an overview of the components of a quantum computer, the levels at which these components and the whole quantum computer can be simulated, and an in-depth analysis of different state-of-the-art acceleration approaches. Besides the optimizations that can be performed at the algorithmic level, this review presents the most promising hardware-aware optimizations and future directions that can be explored for improving the performance and scalability of the simulations.

The realization of fault-tolerant quantum computing requires the execution of quantum error-correction (QEC) schemes, to mitigate the fragile nature of qubits. In this context, to ensure the success of QEC, a protocol capable of implementing both qubit reset and leakage reduction is highly desirable. We demonstrate such a protocol in an architecture consisting of fixed-frequency transmon qubits pair-wise coupled via tunable couplers -- an architecture that is compatible with the surface code. We use tunable couplers to transfer any undesired qubit excitation to the readout resonator of the qubit, from which this excitation decays into the feedline. In total, the combination of qubit reset, leakage reduction, and coupler reset takes only 83ns to complete. Our reset scheme is fast, unconditional, and achieves fidelities well above 99%, thus enabling fixed-frequency qubit architectures as future implementations of fault-tolerant quantum computers. Our protocol also provides a means to both reduce QEC cycle runtime and improve algorithmic fidelity on quantum computers.

A major challenge for scaling up superconducting quantum computers is unwanted couplings between qubits, which lead to always-on ZZ couplings that impact gate fidelities by shifting energy levels conditional on qubit states. To tackle this challenge, we introduce analytical and numerical techniques, including a diagrammatic perturbation theory and a state-assignment algorithm, as well as a refined intuitive picture for the workings of the ZZ coupling. Together, these tools enable a deeper understanding of the mechanisms behind the ZZ coupling and facilitate finding parameter regions of weak and strong ZZ coupling. We showcase these techniques for a system consisting of two fixed-frequency transmon qubits connected by a flux-tunable transmon coupler. There, we find three types of parameter regions with zero or near-zero ZZ coupling, all of which are accessible with current technology. We furthermore find regions of strong ZZ coupling nearby, which may be used to implement adiabatic controlled-phase gates. Our methods are applicable to many types of qubits and open up for the design of large-scale quantum computers with improved gate fidelities., Comment: 35 pages, 12 figures

Lattice models featuring the non-Hermitian skin effect have attracted rapidly growing interest due to the nontrivial topology associated with their complex energy spectra. Such non-Hermitian lattices are promising paradigms for engineering exotic light-matter interactions which benefit from the intrinsic chirality and unconventional (non-Bloch) band theory. Here we study a series of unconventional light-matter interactions between quantum emitters and the prototypical Hatano--Nelson model as well as an extended lattice model dubbed the bosonic Kitaev chain. We focus on the robustness of the dynamics to various imperfections and elucidate the underlying mechanisms. We consider both small emitters, which interact with the lattice at single sites, and giant emitters coupling at multiple sites. The latter exhibit an exclusive amplification mechanism, which we find enables decoherence-free dynamics even in the presence of extra dissipation in the system. The protection from dissipation arises from a cooperation of the non-Hermiticity and the self-interference effect, and is therefore lacking for small emitters. These results not only provides a deeper insight into the interplay of non-Hermiticity and various interference effects, but also have potential applications in engineering exotic spin Hamiltonians and quantum networks., Comment: 11+4 pages, 8 figures. Comments are welcome!

This paper presents the LiU-ICE fault diagnosis benchmark. The purpose of the benchmark is to support fault diagnosis research by providing data and a model of an industrially relevant system. Data has been collected from an internal combustion engine test bench operated in both nominal and faulty modes. A state-of-the-art model of the air path through an internal combustion engine with unknown parameters is provided. This benchmark has previously been used in a competition at the 12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (Safe Process) 2024, Ferrara, Italy., Comment: 4 pages, 7 figures

The ability to generate entangled states of light is a key primitive for quantum communication and distributed quantum computation. Continuously driven sources, including those based on spontaneous parametric downconversion, are usually probabilistic, whereas deterministic sources require accurate timing of the control fields. Here, we experimentally generate entangled photonic modes by continuously exciting a quantum emitter, a superconducting qubit, with a coherent drive, taking advantage of mode matching in the time and frequency domain. Using joint quantum state tomography and logarithmic negativity, we show that entanglement is generated between modes extracted from the two sidebands of the resonance fluorescence spectrum. Because the entangled photonic modes are perfectly orthogonal, they can be transferred into distinct quantum memories. Our approach can be utilized to distribute entanglement at a high rate in various physical platforms, with applications in waveguide quantum electrodynamics, distributed quantum computing, and quantum networks.

In quantum information processing, two primary research directions have emerged: one based on discrete variables (DV) and the other on the structure of quantum states in a continuous-variable (CV) space. It is increasingly recognized that integrating these two approaches could unlock new potentials, overcoming the inherent limitations of each. Here, we show that such a DV-CV hybrid approach, applied to superconducting Kerr parametric oscillators (KPOs), enables us to entangle a pair of Schr\"odinger's cat states by two straightforward methods. The first method involves the entanglement-preserving and deterministic conversion between Bell states in the Fock-state basis (DV encoding) and those in the cat-state basis (CV encoding). This method would allow us to construct quantum networks in the cat-state basis using conventional schemes originally developed for the Fock-state basis. In the second method, the $\sqrt{\textrm{iSWAP}}$ gate operation is implemented between two cat states following the procedure used for Fock-state encoding. This DV-like gate operation on CV encoding not only completes the demonstration of a universal quantum gate set in a KPO system but also enables faster and simpler gate operations compared to previous SWAP gate implementations on bosonic modes. Our work offers a simple yet powerful application of DV-CV hybridization while also highlighting the scalability of this planar KPO system.

Open quantum many-body systems are of both fundamental and applicational interest. However, it remains an open challenge to simulate and solve such systems, both with state-of-the-art classical methods and with quantum-simulation protocols. To overcome this challenge, we introduce a simulator for open quantum many-body systems based on giant atoms, i.e., atoms (possibly artificial), that couple to a waveguide at multiple points, which can be wavelengths apart. We first show that a simulator consisting of two giant atoms can simulate the dynamics of two coupled qubits, where one qubit is subject to different drive amplitudes and dissipation rates. This simulation enables characterizing the quantum Zeno crossover in this model. We further show that by equipping the simulator with post-selection, it becomes possible to simulate the effective non-Hermitian Hamiltonian dynamics of the system and thereby characterize the transition from oscillatory to non-oscillatory dynamics due to varying dissipation rates. We demonstrate and analyze the robustness of these simulation results against noise affecting the giant atoms. Finally, we discuss and show how giant-atom-based simulators can be scaled up for digital-analog simulation of large open quantum many-body systems, e.g., generic dissipative spin models., Comment: 15+4 pages, 8+2 figures, source codes will be available at https://github.com/GUANGZECHEN/SING-ATOM

The availability of high-quality datasets is crucial for the development of behavior prediction algorithms in autonomous vehicles. This paper highlights the need to standardize the use of certain datasets for motion forecasting research to simplify comparative analysis and proposes a set of tools and practices to achieve this. Drawing on extensive experience and a comprehensive review of current literature, we summarize our proposals for preprocessing, visualization, and evaluation in the form of an open-sourced toolbox designed for researchers working on trajectory prediction problems. The clear specification of necessary preprocessing steps and evaluation metrics is intended to alleviate development efforts and facilitate the comparison of results across different studies. The toolbox is available at: https://github.com/westny/dronalize., Comment: https://github.com/westny/dronalize

We propose a setup which combines giant emitters (coupling to light at multiple points separated by wavelength distances) with nonlinear quantum optics and its correlated photons. In this setup, we reveal a mechanism for multiphoton chiral emission: the propagation phase of the center of mass of two strongly correlated photons (a doublon), and the phases encoded in the coupling points of two giant emitters, can yield completely destructive interference in one propagation direction while supporting emission in the other direction. The degree of chirality can be tuned by the phases of the couplings. We show that the proposed setup can provide directional quantum many-body resources, and can be configured as a building block for a chiral quantum network with ``correlated flying qubits'', enabling distinct applications beyond linear chiral setups. Our findings point toward a rich landscape of tailoring multiphoton propagation and correlation properties by exploiting interference effects of giant emitters coupling to nonlinear photonic baths., Comment: 26 pages; 12 figures

Accurate predictions of when a component will fail are crucial when planning maintenance, and by modeling the distribution of these failure times, survival models have shown to be particularly useful in this context. The presented methodology is based on conventional neural network-based survival models that are trained using data that is continuously gathered and stored at specific times, called snapshots. An important property of this type of training data is that it can contain more than one snapshot from a specific individual which results in that standard maximum likelihood training can not be directly applied since the data is not independent. However, the papers show that if the data is in a specific format where all snapshot times are the same for all individuals, called homogeneously sampled, maximum likelihood training can be applied and produce desirable results. In many cases, the data is not homogeneously sampled and in this case, it is proposed to resample the data to make it homogeneously sampled. How densely the dataset is sampled turns out to be an important parameter; it should be chosen large enough to produce good results, but this also increases the size of the dataset which makes training slow. To reduce the number of samples needed during training, the paper also proposes a technique to, instead of resampling the dataset once before the training starts, randomly resample the dataset at the start of each epoch during the training. The proposed methodology is evaluated on both a simulated dataset and an experimental dataset of starter battery failures. The results show that if the data is homogeneously sampled the methodology works as intended and produces accurate survival models. The results also show that randomly resampling the dataset on each epoch is an effective way to reduce the size of the training data., Comment: 7 pages

In this paper, a family of neural network-based survival models is presented. The models are specified based on piecewise definitions of the hazard function and the density function on a partitioning of the time; both constant and linear piecewise definitions are presented, resulting in a family of four models. The models can be seen as an extension of the commonly used discrete-time and piecewise exponential models and thereby add flexibility to this set of standard models. Using a simulated dataset the models are shown to perform well compared to the highly expressive, state-of-the-art energy-based model, while only requiring a fraction of the computation time., Comment: 7 pages

This paper proposes a safety-critical controller for dynamic and uncertain environments, leveraging a robust environment control barrier function (ECBF) to enhance the robustness against the measurement and prediction uncertainties associated with moving obstacles. The approach reduces conservatism, compared with a worst-case uncertainty approach, by incorporating a state observer for obstacles into the ECBF design. The controller, which guarantees safety, is achieved through solving a quadratic programming problem. The proposed method's effectiveness is demonstrated via a dynamic obstacle-avoidance problem for an autonomous vehicle, including comparisons with established baseline approaches.

The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model for multi-agent trajectory prediction is proposed. The model is capable of capturing the complex interactions between traffic participants and the environment, accurately learning the multimodal nature of the data. The effectiveness of the approach is assessed on large-scale datasets of real-world traffic scenarios, showing that our model outperforms several well-established methods in terms of prediction accuracy. By the incorporation of differential motion constraints on the model output, we illustrate that our model is capable of generating a diverse set of realistic future trajectories. Through the use of an interaction-aware guidance signal, we further demonstrate that the model can be adapted to predict the behavior of less cooperative agents, emphasizing its practical applicability under uncertain traffic conditions.

Safe motion planning for robotic systems in dynamic environments is nontrivial in the presence of uncertain obstacles, where estimation of obstacle uncertainties is crucial in predicting future motions of dynamic obstacles. The worst-case characterization gives a conservative uncertainty prediction and may result in infeasible motion planning for the ego robotic system. In this paper, an efficient, robust, and safe motion-planing algorithm is developed by learning the obstacle uncertainties online. More specifically, the unknown yet intended control set of obstacles is efficiently computed by solving a linear programming problem. The learned control set is used to compute forward reachable sets of obstacles that are less conservative than the worst-case prediction. Based on the forward prediction, a robust model predictive controller is designed to compute a safe reference trajectory for the ego robotic system that remains outside the reachable sets of obstacles over the prediction horizon. The method is applied to a car-like mobile robot in both simulations and hardware experiments to demonstrate its effectiveness.

Quantum processors require a signal-delivery architecture with high addressability (low crosstalk) to ensure high performance already at the scale of dozens of qubits. Signal crosstalk causes inadvertent driving of quantum gates, which will adversely affect quantum-gate fidelities in scaled-up devices. Here, we demonstrate packaged flip-chip superconducting quantum processors with signal-crosstalk performance competitive with those reported in other platforms. For capacitively coupled qubit-drive lines, we find on-resonant crosstalk better than -27 dB (average -37 dB). For inductively coupled magnetic-flux-drive lines, we find less than 0.13 % direct-current flux crosstalk (average 0.05 %). These observed crosstalk levels are adequately small and indicate a decreasing trend with increasing distance, which is promising for further scaling up to larger numbers of qubits. We discuss the implication of our results for the design of a low-crosstalk, on-chip signal delivery architecture, including the influence of a shielding tunnel structure, potential sources of crosstalk, and estimation of crosstalk-induced qubit-gate error in scaled-up quantum processors., Comment: 21 pages, 16 figures, includes appendices, updated discussion on source of xy crosstalk, flux stability, etc

Giant atoms are quantum emitters that can couple to light at multiple discrete points. Such atoms have been shown to interact without decohering via a one-dimensional waveguide. Here, we study how giant atoms behave when coupled to a two-dimensional square lattice of coupled cavities, an environment characterized by a finite energy band and band gaps. In particular, we describe the role that bound states in the continuum (BICs) play in how giant atoms avoid decoherence. By developing numerical methods, we are able to investigate the dynamics of the system and show the appearance of interfering BICs within a single giant atom, as well as oscillating BICs between many giant atoms. In this way, we find the geometric arrangements of atomic coupling points that yield protection from decoherence in the two-dimensional lattice. These results on engineering the interaction between light and matter may find applications in quantum simulation and quantum information processing., Comment: 20 pages, 13 figures

The interaction of light and matter at the single-photon level is of central importance in various fields of physics, including, e.g., condensed matter physics, astronomy, quantum optics, and quantum information. Amplification of such quantum light-matter interaction can be highly beneficial to, e.g., improve device performance, explore novel phenomena, and understand fundamental physics, and has therefore been a long-standing goal. Furthermore, simulation of light-matter interaction in the regime of ultrastrong coupling, where the interaction strength is comparable to the bare frequencies of the uncoupled systems, has also become a hot research topic, and considerable progress has been made both theoretically and experimentally in the past decade. In this review, we provide a detailed introduction of recent advances in amplification of quantum light-matter interaction and simulation of ultrastrong light-matter interaction, particularly in cavity and circuit quantum electrodynamics and in cavity optomechanics., Comment: 91 pages+18 figures+1 table

Safety, reliability, and durability are targets of all engineering systems, including Li-ion batteries in electric vehicles. This paper focuses on sensor setup exploration for a battery-integrated modular multilevel converter (BI-MMC) that can be part of a solution to sustainable electrification of vehicles. BI-MMC contains switches to convert DC to AC to drive an electric machine. The various configurations of switches result in different operation modes, which in turn, pose great challenges for diagnostics. The study explores diverse sensor arrangements and system configurations for detecting and isolating faults in modular battery packs. Configurations involving a minimum of two modules integrated into the pack are essential to successfully isolate all faults. The findings indicate that the default sensor setup is insufficient for achieving complete fault isolability. Additionally, the investigation also demonstrates that current sensors in the submodules do not contribute significantly to fault isolability. Further, the results on switch positions show that the system configuration has a significant impact on fault isolability. A combination of appropriate sensor data and system configuration is important in achieving optimal diagnosability, which is a paramount objective in ensuring system safety.

Multi-mode systems can operate in different modes, leading to large numbers of different dynamics. Consequently, applying traditional structural diagnostics to such systems is often untractable. To address this challenge, we present a multi-mode diagnostics algorithm that relies on a multi-mode extension of the Dulmage-Mendelsohn decomposition. We introduce two methodologies for modeling faults, either as signals or as Boolean variables, and apply them to a modular switched battery system in order to demonstrate their effectiveness and discuss their respective advantages.

This paper addresses the training of Neural Ordinary Differential Equations (neural ODEs), and in particular explores the interplay between numerical integration techniques, stability regions, step size, and initialization techniques. It is shown how the choice of integration technique implicitly regularizes the learned model, and how the solver's corresponding stability region affects training and prediction performance. From this analysis, a stability-informed parameter initialization technique is introduced. The effectiveness of the initialization method is displayed across several learning benchmarks and industrial applications., Comment: In Proceedings of the 41 st International Conference on Machine Learning

To study the effects of uncertainty in autonomous motion planning and control, an 8-DOF model of a tractor-semitrailer is implemented and analyzed. The implications of uncertainties in the model are then quantified and presented using sensitivity analysis and closed-loop simulations. The analysis reveals that the significance of various model parameters varies depending on the specific scenario under investigation. By using sampling-based closed-loop predictions, uncertainty bounds on state variable trajectories are determined. Our findings suggest the potential for the inclusion of our method within a robust predictive controller or as a driver-assistance system for rollover or lane departure warnings.

A new family of high-strength Al-alloys has recently been developed, tailored for the powder bed fusion-laser beam process. In these alloys, Mn, Cr and Zr are incorporated in solid solution at amounts up to three times that of equilibrium in the as-printed state. Mn and Cr-enriched precipitates that form during printing and heat treatment influence the material's mechanical properties. In this study, direct imaging of these precipitates was accomplished through the utilisation of in situ synchrotron-based scanning X-ray fluorescence. During heat treatment, a selective accumulation of Cr and Mn in two distinct types of precipitates at grain boundaries was observed. Additionally, the microstructure at the melt-pool boundary, containing precipitates found in the as-printed state, remains thermally stable during the heat treatment. The study demonstrates the significant value of employing high-sensitivity in-situ X-ray fluorescence microscopy in exploring the kinetics of sub-micrometre scale precipitation.

These are the lecture notes of the master's course "Quantum Computing", taught at Chalmers University of Technology every fall since 2020, with participation of students from RWTH Aachen and Delft University of Technology. The aim of this course is to provide a theoretical overview of quantum computing, excluding specific hardware implementations. Topics covered in these notes include quantum algorithms (such as Grover's algorithm, the quantum Fourier transform, phase estimation, and Shor's algorithm), variational quantum algorithms that utilise an interplay between classical and quantum computers [such as the variational quantum eigensolver (VQE) and the quantum approximate optimisation algorithm (QAOA), among others], quantum error correction, various versions of quantum computing (such as measurement-based quantum computation, adiabatic quantum computation, and the continuous-variable approach to quantum information), the intersection of quantum computing and machine learning, and quantum complexity theory. Lectures on these topics are compiled into 12 chapters, most of which contain a few suggested exercises at the end, and interspersed with four tutorials, which provide practical exercises as well as further details. At Chalmers, the course is taught in seven weeks, with three two-hour lectures or tutorials per week. It is recommended that the students taking the course have some previous experience with quantum physics, but not strictly necessary., Comment: Lecture notes from an MSc overview course on quantum computing. 177 pages, 58 figures, 12 chapters, 4 tutorials

A photoelectron, emitted due to the absorption of light quanta as described by the photoelectric effect, is often characterized experimentally by a classical quantity, its momentum. However, since the photoelectron is a quantum object, its rigorous characterization requires the reconstruction of the complete quantum state, the photoelectron's density matrix. Here, we use quantum state tomography to fully characterize photoelectrons emitted from helium and argon atoms upon absorption of ultrashort, extreme ultraviolet light pulses. While in helium we measure a pure photoelectronic state, in argon, spin-orbit interaction induces entanglement between the ion and the photoelectron, leading to a reduced purity of the photoelectron state. Our work shows how state tomography gives new insights into the fundamental quantum aspects of light-induced electronic processes in matter, bridging the fields of photoelectron spectroscopy and quantum information, and offering new spectroscopic possibilities for quantum technology.

We explore the use of machine-learning techniques to detect quantum speedup in random walks on graphs. Specifically, we investigate the performance of three different neural-network architectures (variations on fully connected and convolutional neural networks) for identifying linear, cyclic, and random graphs that yield quantum speedups in terms of the hitting time for reaching a target node after starting in another node of the graph. Our results indicate that carefully building the data set for training can improve the performance of the neural networks, but all architectures we test struggle to classify large random graphs and generalize from training on one graph size to testing on another. If classification accuracy can be improved further, valuable insights about quantum advantage may be gleaned from these neural networks, not only for random walks, but more generally for quantum computing and quantum transport., Comment: 15 pages, 8 figures

Giant emitters derive their name from nonlocal field-emitter interactions and feature diverse self-interference effects. Most of the existing works on giant emitters have considered Hermitian waveguides or photonic lattices. In this work, we unveil how giant emitters behave if they are coupled to a non-Hermitian bath, i.e., a Hatano-Nelson (HN) model which features a non-Hermitian skin effect due to the asymmetric inter-site tunneling rates. We show that the behaviors of the giant emitters are closely related to the stability of the bath. In the convectively unstable regime, where the HN model can be mapped to a pseudo-Hermitian lattice, a giant emitter can either behave as in a Hermitian bath or undergo excitation amplification, depending on the relative strength of different emitter-bath coupling paths. Based on this mechanism, we can realize protected nonreciprocal interactions between giant emitters, with nonreciprocity opposite to that of the bath. Such giant-emitter effects are not allowed, however, if the HN model enters the absolutely unstable regime, where the coupled emitters always show secular energy growth. Our proposal provides a new paradigm of non-Hermitian quantum optics, which may be useful for, e.g., engineering effective interactions between quantum emitters and performing many-body simulations in the non-Hermitian framework., Comment: 13 pages, 7 figures

Exploiting the strengths of different quantum hardware components may enhance the capabilities of emerging quantum processors. Here, we propose and analyze a quantum architecture that leverages the non-local connectivity of optics, along with the exquisite quantum control offered by superconducting microwave circuits, to produce entangled optical resource states. Contrary to previous proposals on optically distributing entanglement between superconducting microwave processors, we use squeezing between microwaves and optics to produce microwave-optical Bell pairs in a dual-rail encoding from a single microwave quantum processor. Moreover, the microwave quantum processor allows us to deterministically entangle microwave-optical Bell pairs into larger cluster states, from which entangled optical photons can be extracted through microwave measurements. Our scheme paves the way for small microwave quantum processors to create heralded entangled optical resource states for optical quantum computation, communication, and sensing using imperfect microwave-optics transducers. We expect that improved isolation of the superconducting processor from stray optical fields will allow the scheme to be demonstrated using currently available hardware., Comment: 16 pages (+11 pages in supplement) 7 Figures (+4 in supplement)

Magnetic droplets are nanoscale, non-topological, magnetodynamical solitons that can be nucleated in spin torque nano-oscillators (STNOs) or spin Hall nano-oscillators (SHNOs). All theoretical, numerical, and experimental droplet studies have so far focused on the free layer (FL), and any additional dynamics in the reference layer (RL) have been entirely ignored. Here we show, using all-perpendicular STNOs, that there is not only significant magnetodynamics in the RL, but the reference layer itself can host a droplet coexisting with the FL droplet. Both droplets are observed experimentally as stepwise changes and sharp peaks in the dc and differential resistance, respectively. Whereas the single FL droplet is highly stable, the coexistence state exhibits high-power broadband microwave noise. Micromagnetic simulations corroborate the experimental results and reveal a strong interaction between the droplets. Our demonstration of strongly interacting and closely spaced droplets offers a unique platform for fundamental studies of highly non-linear soliton pair dynamics., Comment: 23 pages, 6 figures

Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun's can greatly improve predictions., Comment: https://github.com/westny/mtp-go

The reproducibility of qubit parameters is a challenge for scaling up superconducting quantum processors. Signal crosstalk imposes constraints on the frequency separation between neighboring qubits. The frequency uncertainty of transmon qubits arising from the fabrication process is attributed to deviations in the Josephson junction area, tunnel barrier thickness, and the qubit capacitor. We decrease the sensitivity to these variations by fabricating larger Josephson junctions and reduce the wafer-level standard deviation in resistance down to 2%. We characterize 32 identical transmon qubits and demonstrate the reproducibility of the qubit frequencies with a 40 MHz standard deviation (i.e. 1%) with qubit quality factors exceeding 2 million. We perform two-level-system (TLS) spectroscopy and observe no significant increase in the number of TLSs causing qubit relaxation. We further show by simulation that for our parametric-gate architecture, and accounting only for errors caused by the uncertainty of the qubit frequency, we can scale up to 100 qubits with an average of only 3 collisions between quantum-gate transition frequencies, assuming 2% crosstalk and 99.9% target gate fidelity., Comment: 8 figures, 18 pages (8 pages main text), units fixed in Fig. 1e

Encoding quantum information into superpositions of multiple Fock states of a harmonic oscillator can provide protection against errors, but it comes with the cost of requiring more complex quantum gates that need to address multiple Fock states simultaneously. Therefore, characterizing the quantum process fidelity of these gates also becomes more challenging. Here, we demonstrate the use of coherent-state quantum process tomography (csQPT) for a bosonic-mode superconducting circuit. CsQPT uses coherent states as input probes for the quantum process in order to completely characterize the quantum operation for an arbitrary input state. We show results for this method by characterizing a logical quantum gate constructed using displacement and SNAP operations on an encoded qubit. With csQPT, we are able to reconstruct the Kraus operators for the larger Hilbert space rather than being limited to the logical subspace. This allows for a more accurate determination of the different error mechanisms that lead to the gate infidelity.

The fidelity of quantum operations is often limited by incoherent errors, which typically can be modeled by fundamental Markovian noise processes such as amplitude damping and dephasing. In Phys. Rev. Lett. 129, 150504 (2022; https://doi.org/10.1103/PhysRevLett.129.150504), we presented an analytical result for the average gate fidelity of a general multiqubit operation in terms of the dissipative rates and the corresponding Lindblad jump operators, provided that the operation remains in the computational subspace throughout the time evolution. Here we generalize this expression for the average gate fidelity to include the cases where the system state temporarily leaves the computational subspace during the gate. Such gate mechanisms are integral to several quantum-computing platforms, and our formula is applicable to all of them; as examples, we employ it for the two-qubit controlled-Z gate in both superconducting qubits and neutral atoms. We also obtain the average gate fidelity for simultaneous operations applied in multiqubit systems. These results are useful for understanding the error budgets of quantum gates while scaling up quantum computers., Comment: 8+9 pages

We investigate the amplification of a microwave probe signal by a superconducting artificial atom, a transmon, strongly coupled to the end of a one-dimensional semi-infinite transmission line. The end of the transmission line acts as a mirror for microwave fields. Due to the weak anharmonicity of the artificial atom, a strong pump field creates multi-photon excitations among the dressed states. Transitions between these dressed states, Rabi sidebands, give rise to either amplification or attenuation of the weak probe. We obtain a maximum amplitude amplification of about 18 %, higher than in any previous experiment with a single artificial atom, due to constructive interference between Rabi sidebands. We also characterize the noise properties of the system by measuring the spectrum of spontaneous emission.

Predictive maintenance is an effective tool for reducing maintenance costs. Its effectiveness relies heavily on the ability to predict the future state of health of the system, and for this survival models have shown to be very useful. Due to the complex behavior of system degradation, data-driven methods are often preferred, and neural network-based methods have been shown to perform particularly very well. Many neural network-based methods have been proposed and successfully applied to many problems. However, most models rely on assumptions that often are quite restrictive and there is an interest to find more expressive models. Energy-based models are promising candidates for this due to their successful use in other applications, which include natural language processing and computer vision. The focus of this work is therefore to investigate how energy-based models can be used for survival modeling and predictive maintenance. A key step in using energy-based models for survival modeling is the introduction of right-censored data, which, based on a maximum likelihood approach, is shown to be a straightforward process. Another important part of the model is the evaluation of the integral used to normalize the modeled probability density function, and it is shown how this can be done efficiently. The energy-based survival model is evaluated using both simulated data and experimental data in the form of starter battery failures from a fleet of vehicles, and its performance is found to be highly competitive compared to existing models., Comment: 7 pages

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics., Comment: Code: https://github.com/westny/mtp-go

In the field of fault-tolerant quantum computing, continuous-variable systems can be utilized to protect quantum information from noise through the use of bosonic codes. These codes map qubit-type quantum information onto the larger bosonic Hilbert space, and can be divided into two main categories: translational-symmetric codes, such as Gottesman-Kitaev-Preskill (GKP) codes, and rotational-symmetric codes, including cat and binomial codes. The relationship between these families of codes has not yet been fully understood. We present an iterative protocol for converting between two instances of these codes GKP qunaught states and four-foldsymmetric binomial states corresponding to a zero-logical encoded qubit - using only Gaussian operations. This conversion demonstrates the potential for universality of binomial states for all-Gaussian quantum computation and provides a new method for the heraladed preparation of GKP states. Through numerical simulation, we obtain GKP qunaught states with a fidelity of over 98% and a probability of approximately 3.14%, after only two steps of our iterative protocol, though higher fidelities can be achieved with additional iterations at the cost of lower success probabilities., Comment: 10 pages

This paper proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting multiple model Kalman filter (IAIMM-KF) to predict interactive multi-modal maneuvers of surrounding vehicles, and the advantage of model predictive control (MPC) in planning an optimal trajectory in uncertain dynamic environments. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in computing the reference targets and designing the collision-avoidance constraints of MPC for resilient motion planning of the ego vehicle. The MPC achieves safety awareness by incorporating a tunable parameter to adjust the predicted obstacle occupancy in the design of the safety constraints, allowing the approach to achieve a trade-off between performance and robustness. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by MPC to follow the time-varying reference targets and avoid collisions with obstacles. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset., Comment: 15 pages

High-fidelity and rapid readout of a qubit state is key to quantum computing and communication, and it is a prerequisite for quantum error correction. We present a readout scheme for superconducting qubits that combines two microwave techniques: applying a shelving technique to the qubit that effectively increases the energy-relaxation time, and a two-tone excitation of the readout resonator to distinguish among qubit populations in higher energy levels. Using a machine-learning algorithm to post-process the two-tone measurement results further improves the qubit-state assignment fidelity. We perform single-shot frequency-multiplexed qubit readout, with a 140ns readout time, and demonstrate 99.5% assignment fidelity for two-state readout and 96.9% for three-state readout - without using a quantum-limited amplifier.

Giant atoms -- quantum emitters that couple to light at multiple discrete points -- are emerging as a new paradigm in quantum optics thanks to their many promising properties, such as decoherence-free interaction. While most previous work has considered giant atoms coupled to open continuous waveguides or a single giant atom coupled to a structured bath, here we study the interaction between two giant atoms mediated by a structured waveguide, e.g., a photonic crystal waveguide. This environment is characterized by a finite energy band and a band gap, which affect atomic dynamics beyond the Markovian regime. Here we show that, inside the band, decoherence-free interaction is possible for different atom-cavity detunings, but is degraded from the continuous-waveguide case by time delay and other non-Markovian effects. Outside the band, where atoms interact through the overlap of bound states, we find that giant atoms can interact more strongly and over longer distances than small atoms for some parameters -- for instance, when restricting the maximum coupling strength achievable per coupling point. The results presented here may find applications in quantum simulation and quantum gate implementation., Comment: 15 pages, 10 figures

We perform quantum process tomography (QPT) for both discrete- and continuous-variable quantum systems by learning a process representation using Kraus operators. The Kraus form ensures that the reconstructed process is completely positive. To make the process trace-preserving, we use a constrained gradient-descent (GD) approach on the so-called Stiefel manifold during optimization to obtain the Kraus operators. Our ansatz uses a few Kraus operators to avoid direct estimation of large process matrices, e.g., the Choi matrix, for low-rank quantum processes. The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes, but shines by combining the best features of these two methods. Similar to CS (but unlike PLS), GD-QPT can reconstruct a process from just a small number of random measurements, and similar to PLS (but unlike CS) it also works for larger system sizes, up to at least five qubits. We envisage that the data-driven approach of GD-QPT can become a practical tool that greatly reduces the cost and computational effort for QPT in intermediate-scale quantum systems., Comment: 10 pages, 5 figures, Code and data available at https://github.com/quantshah/gd-qpt

While all quantum algorithms can be expressed in terms of single-qubit and two-qubit gates, more expressive gate sets can help reduce the algorithmic depth. This is important in the presence of gate errors, especially those due to decoherence. Using superconducting qubits, we have implemented a three-qubit gate by simultaneously applying two-qubit operations, thereby realizing a three-body interaction. This method straightforwardly extends to other quantum hardware architectures, requires only a "firmware" upgrade to implement, and is faster than its constituent two-qubit gates. The three-qubit gate represents an entire family of operations, creating flexibility in quantum-circuit compilation. We demonstrate a gate fidelity of $97.90\%$, which is near the coherence limit of our device. We then generate two classes of entangled states, the GHZ and W states, by applying the new gate only once; in comparison, decompositions into the standard gate set would have a two-qubit gate depth of two and three, respectively. Finally, we combine characterization methods and analyze the experimental and statistical errors on the fidelity of the gates and of the target states., Comment: 19 pages, 10 figures

In this work Time Series Classification techniques are investigated, and especially their applicability in applications where there are significant differences between the individuals where data is collected, and the individuals where the classification is evaluated. Classification methods are applied to a fault classification case, where a key assumption is that data from a fault free reference case for each specific individual is available. For the investigated application, wave propagation cause almost chaotic changes of a measured pressure signal, and physical modeling is difficult. Direct application of One-Nearest-Neighbor Dynamic Time Warping, a common technique for this kind of problem, and other machine learning techniques are shown to fail for this case and new methods to improve the situation are presented. By using relative features describing the difference from the reference case rather than the absolute time series, improvements are made compared to state-of-the-art time series classification algorithms., Comment: 14 pages, 16 figures

An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic scenarios is proposed. The proposed method is based on model predictive control (MPC), where alternative predictions of the surrounding traffic are determined automatically such that they are intentionally adversarial to the ego vehicle. This provides robustness against the inherent uncertainty in traffic predictions. To reduce conservatism, an assumption that other agents are of no ill intent is formalized. Simulation results from highway driving scenarios show that the proposed method in real-time negotiates traffic situations out of scope for a nominal MPC approach and performs favorably to state-of-the-art reinforcement-learning approaches without requiring prior training. The results also show that the proposed method performs effectively, with the ability to prune disturbance sequences with a lower risk for the ego vehicle., Comment: Published in: IEEE Transactions on Intelligent Vehicles ( Early Access )

We consider a topological stabilizer code on a honeycomb grid, the "XYZ$^2$" code. The code is inspired by the Kitaev honeycomb model and is a simple realization of a "matching code" discussed by Wootton [J. Phys. A: Math. Theor. 48, 215302 (2015)], with a specific implementation of the boundary. It utilizes weight-six ($XYZXYZ$) plaquette stabilizers and weight-two ($XX$) link stabilizers on a planar hexagonal grid composed of $2d^2$ qubits for code distance $d$, with weight-three stabilizers at the boundary, stabilizing one logical qubit. We study the properties of the code using maximum-likelihood decoding, assuming perfect stabilizer measurements. For pure $X$, $Y$, or $Z$ noise, we can solve for the logical failure rate analytically, giving a threshold of 50%. In contrast to the rotated surface code and the XZZX code, which have code distance $d^2$ only for pure $Y$ noise, here the code distance is $2d^2$ for both pure $Z$ and pure $Y$ noise. Thresholds for noise with finite $Z$ bias are similar to the XZZX code, but with markedly lower sub-threshold logical failure rates. The code possesses distinctive syndrome properties with unidirectional pairs of plaquette defects along the three directions of the triangular lattice for isolated errors, which may be useful for efficient matching-based or other approximate decoding., Comment: 15 pages, 7 figures

We have integrated single and coupled superconducting transmon qubits into flip-chip modules. Each module consists of two chips -- one quantum chip and one control chip -- that are bump-bonded together. We demonstrate time-averaged coherence times exceeding $90\,\mu s$, single-qubit gate fidelities exceeding $99.9\%$, and two-qubit gate fidelities above $98.6\%$. We also present device design methods and discuss the sensitivity of device parameters to variation in interchip spacing. Notably, the additional flip-chip fabrication steps do not degrade the qubit performance compared to our baseline state-of-the-art in single-chip, planar circuits. This integration technique can be extended to the realisation of quantum processors accommodating hundreds of qubits in one module as it offers adequate input/output wiring access to all qubits and couplers., Comment: 33 pages, 12 figures, includes supplementary materials, updated with further calculations on participation ratio and Purcell limit

Efficient high-performance decoding of topological stabilizer codes has the potential to crucially improve the balance between logical failure rates and the number and individual error rates of the constituent qubits. High-threshold maximum-likelihood decoders require an explicit error model for Pauli errors to decode a specific syndrome, whereas lower-threshold heuristic approaches such as minimum weight matching are "error agnostic". Here we consider an intermediate approach, formulating a decoder that depends on the bias, i.e., the relative probability of phase-flip to bit-flip errors, but is agnostic to error rate. Our decoder is based on counting the number and effective weight of the most likely error chains in each equivalence class of a given syndrome. We use Metropolis-based Monte Carlo sampling to explore the space of error chains and find unique chains, that are efficiently identified using a hash table. Using the error-rate invariance the decoder can sample chains effectively at an error rate which is higher than the physical error rate and without the need for "thermalization" between chains in different equivalence classes. Applied to the surface code and the XZZX code, the decoder matches maximum-likelihood decoders for moderate code sizes or low error rates. We anticipate that, because of the compressed information content per syndrome, it can be taken full advantage of in combination with machine-learning methods to extrapolate Monte Carlo-generated data., Comment: 15 pages, 9 figures; V2 Added analysis of low error-rate performance