Estimating the state preparation fidelity of highly entangled states on noisy intermediate-scale quantum (NISQ) devices is an important task for benchmarking and application considerations. Unfortunately, exact fidelity measurements quickly become prohibitively expensive, as they scale exponentially as $O(3^N)$ for $N$-qubit states, using full state tomography with measurements in all Pauli bases combinations. However, Somma and others [PhysRevA.74.052302] established that the complexity could be drastically reduced when looking at fidelity lower bounds for states that exhibit symmetries, such as Dicke States and GHZ States. For larger states, these bounds still need to be tight enough to provide reasonable estimations on NISQ devices. For the first time and more than 15 years after the theoretical introduction, we report meaningful lower bounds for the state preparation fidelity of all Dicke States up to $N=10$, and all GHZ states up to $N=20$ on Quantinuum H1 ion-trap systems using efficient implementations of recently proposed scalable circuits for these states. Our achieved lower bounds match or exceed previously reported exact fidelities on superconducting systems for much smaller states. This work provides a path forward to benchmarking entanglement as NISQ devices improve in size and quality.
相關內容
Deep neural networks are a powerful tool for predicting properties of quantum states from limited measurement data. Here we develop a network model that can simultaneously predict multiple quantum properties, including not only expectation values of quantum observables, but also general nonlinear functions of the quantum state, like entanglement entropies and many-body topological invariants. Remarkably, we find that a model trained on a given set of properties can also discover new properties outside that set. Multi-purpose training also enables the model to infer global properties of many-body quantum systems from local measurements, to classify symmetry protected topological phases of matter, and to discover unknown boundaries between different phases.
Identifying defect patterns in a wafer map during manufacturing is crucial to find the root cause of the underlying issue and provides valuable insights on improving yield in the foundry. Currently used methods use deep neural networks to identify the defects. These methods are generally very huge and have significant inference time. They also require GPU support to efficiently operate. All these issues make these models not fit for on-line prediction in the manufacturing foundry. In this paper, we propose an extremely simple yet effective technique to extract features from wafer images. The proposed method is extremely fast, intuitive, and non-parametric while being explainable. The experiment results show that the proposed pipeline outperforms conventional deep learning models. Our feature extraction requires no training or fine-tuning while preserving the relative shape and location of data points as revealed by our interpretability analysis.
There is no doubt that the Moon has become the center of interest for commercial and international actors. Over the past decade, the number of planned long-term missions has increased dramatically. This makes the establishment of cislunar space networks (CSNs) crucial to orchestrate uninterrupted communications between the Moon and Earth. However, there are numerous challenges, unknowns, and uncertainties associated with cislunar communications that may pose various risks to lunar missions. In this study, we aim to address these challenges for cislunar communications by proposing a machine learning-based cislunar space domain awareness (SDA) capability that enables robust and secure communications. To this end, we first propose a detailed channel model for selected cislunar scenarios. Secondly, we propose two types of interference that could model anomalies that occur in cislunar space and are so far known only to a limited extent. Finally, we discuss our cislunar SDA to work in conjunction with the spacecraft communication system. Our proposed cislunar SDA, involving heuristic learning capabilities with machine learning algorithms, detects interference models with over 96% accuracy. The results demonstrate the promising performance of our cislunar SDA approach for secure and robust cislunar communication.
We investigate the coexistence of massive and critical Internet of Things (IoT) services in the context of the unsourced multiple access (UMA) framework introduced by Polyanskiy (2017), where all users employ a common codebook and the receiver returns an unordered list of decoded codewords. This setup is suitably modified to introduce heterogeneous traffic. Specifically, to model the massive IoT service, a standard message originates independently from each IoT device as in the standard UMA setup. To model the critical IoT service, we assume the generation of alarm messages that are common for all devices. This setup requires a significant redefinition of the error events, i.e., misdetections and false positives. We further assume that the number of active users in each transmission attempt is random and unknown. We derive a random-coding achievability bound on the misdetection and false positive probabilities of both standard and alarm messages on the Gaussian multiple access channel. Using our bound, we demonstrate that orthogonal network slicing enables massive and critical IoT to coexist under the requirement of high energy efficiency. On the contrary, we show that nonorthogonal network slicing is energy inefficient due to the residual interference from the alarm signal when decoding the standard messages.
We investigate the dimension-parametric complexity of the reachability problem in vector addition systems with states (VASS) and its extension with pushdown stack (pushdown VASS). Up to now, the problem is known to be $\mathcal{F}_k$-hard for VASS of dimension $3k+2$ (the complexity class $\mathcal{F}_k$ corresponds to the $k$th level of the fast-growing hierarchy), and no essentially better bound is known for pushdown VASS. We provide a new construction that improves the lower bound for VASS: $\mathcal{F}_k$-hardness in dimension $2k+3$. Furthermore, building on our new insights we show a new lower bound for pushdown VASS: $\mathcal{F}_k$-hardness in dimension $\frac k 2 + 4$. This dimension-parametric lower bound is strictly stronger than the upper bound for VASS, which suggests that the (still unknown) complexity of the reachability problem in pushdown VASS is higher than in plain VASS (where it is Ackermann-complete).
Image-level weakly supervised semantic segmentation (WSSS) is a fundamental yet challenging computer vision task facilitating scene understanding and automatic driving. Most existing methods resort to classification-based Class Activation Maps (CAMs) to play as the initial pseudo labels, which tend to focus on the discriminative image regions and lack customized characteristics for the segmentation task. To alleviate this issue, we propose a novel activation modulation and recalibration (AMR) scheme, which leverages a spotlight branch and a compensation branch to obtain weighted CAMs that can provide recalibration supervision and task-specific concepts. Specifically, an attention modulation module (AMM) is employed to rearrange the distribution of feature importance from the channel-spatial sequential perspective, which helps to explicitly model channel-wise interdependencies and spatial encodings to adaptively modulate segmentation-oriented activation responses. Furthermore, we introduce a cross pseudo supervision for dual branches, which can be regarded as a semantic similar regularization to mutually refine two branches. Extensive experiments show that AMR establishes a new state-of-the-art performance on the PASCAL VOC 2012 dataset, surpassing not only current methods trained with the image-level of supervision but also some methods relying on stronger supervision, such as saliency label. Experiments also reveal that our scheme is plug-and-play and can be incorporated with other approaches to boost their performance.
Graph neural networks (GNNs) are a popular class of machine learning models whose major advantage is their ability to incorporate a sparse and discrete dependency structure between data points. Unfortunately, GNNs can only be used when such a graph-structure is available. In practice, however, real-world graphs are often noisy and incomplete or might not be available at all. With this work, we propose to jointly learn the graph structure and the parameters of graph convolutional networks (GCNs) by approximately solving a bilevel program that learns a discrete probability distribution on the edges of the graph. This allows one to apply GCNs not only in scenarios where the given graph is incomplete or corrupted but also in those where a graph is not available. We conduct a series of experiments that analyze the behavior of the proposed method and demonstrate that it outperforms related methods by a significant margin.
Dynamic programming (DP) solves a variety of structured combinatorial problems by iteratively breaking them down into smaller subproblems. In spite of their versatility, DP algorithms are usually non-differentiable, which hampers their use as a layer in neural networks trained by backpropagation. To address this issue, we propose to smooth the max operator in the dynamic programming recursion, using a strongly convex regularizer. This allows to relax both the optimal value and solution of the original combinatorial problem, and turns a broad class of DP algorithms into differentiable operators. Theoretically, we provide a new probabilistic perspective on backpropagating through these DP operators, and relate them to inference in graphical models. We derive two particular instantiations of our framework, a smoothed Viterbi algorithm for sequence prediction and a smoothed DTW algorithm for time-series alignment. We showcase these instantiations on two structured prediction tasks and on structured and sparse attention for neural machine translation.
Multi-relation Question Answering is a challenging task, due to the requirement of elaborated analysis on questions and reasoning over multiple fact triples in knowledge base. In this paper, we present a novel model called Interpretable Reasoning Network that employs an interpretable, hop-by-hop reasoning process for question answering. The model dynamically decides which part of an input question should be analyzed at each hop; predicts a relation that corresponds to the current parsed results; utilizes the predicted relation to update the question representation and the state of the reasoning process; and then drives the next-hop reasoning. Experiments show that our model yields state-of-the-art results on two datasets. More interestingly, the model can offer traceable and observable intermediate predictions for reasoning analysis and failure diagnosis.
Detecting carried objects is one of the requirements for developing systems to reason about activities involving people and objects. We present an approach to detect carried objects from a single video frame with a novel method that incorporates features from multiple scales. Initially, a foreground mask in a video frame is segmented into multi-scale superpixels. Then the human-like regions in the segmented area are identified by matching a set of extracted features from superpixels against learned features in a codebook. A carried object probability map is generated using the complement of the matching probabilities of superpixels to human-like regions and background information. A group of superpixels with high carried object probability and strong edge support is then merged to obtain the shape of the carried object. We applied our method to two challenging datasets, and results show that our method is competitive with or better than the state-of-the-art.
注冊地址： 北京市海淀區羊坊店路18號2幢3層301-191