High-dimensional Hilbert Space Research Articles

Vector Vortex Beam Emitter Embedded in a Photonic Chip.

Vector vortex beams simultaneously carrying spin and orbital angular momentum of light promise additional degrees of freedom for modern optics and emerging resources for both classical and quantum information technologies. The inherently infinite dimensions can be exploited to enhance data capacity for sustaining the unprecedented growth in big data and internet traffic and can be encoded to build quantum computing machines in high-dimensional Hilbert space. So far, much progress has been made in the emission of vector vortex beams from a chip surface into free space; however, the generation of vector vortex beams inside a photonic chip has not been realized yet. Here, we demonstrate the first vector vortex beam emitter embedded in a photonic chip by using femtosecond laser direct writing. We achieve a conversion of vector vortex beams with an efficiency up to 30% and scalar vortex beams with an efficiency up to 74% from Gaussian beams. We also present an expanded coupled-mode model for understanding the mode conversion and the influence of the imperfection in fabrication. The fashion of embedded generation makes vector vortex beams directly ready for further transmission, manipulation, and emission without any additional interconnection. Together with the ability to be integrated as an array, our results may enable vector vortex beams to become accessible inside a photonic chip for high-capacity communication and high-dimensional quantum information processing.

Finite-Size Scaling of Typicality-Based Estimates

Abstract According to the concept of typicality, an ensemble average can be accurately approximated by an expectation value with respect to a single pure state drawn at random from a high-dimensional Hilbert space. This random-vector approximation, or trace estimator, provides a powerful approach to, e.g. thermodynamic quantities for systems with large Hilbert-space sizes, which usually cannot be treated exactly, analytically or numerically. Here, we discuss the finite-size scaling of the accuracy of such trace estimators from two perspectives. First, we study the full probability distribution of random-vector expectation values and, second, the full temperature dependence of the standard deviation. With the help of numerical examples, we find pronounced Gaussian probability distributions and the expected decrease of the standard deviation with system size, at least above certain system-specific temperatures. Below and in particular for temperatures smaller than the excitation gap, simple rules are not available.

Experimental high-dimensional quantum secret sharing with spin-orbit-structured photons

Secret sharing allows three or more parties to share secret information which can only be decrypted through collaboration. It complements quantum key distribution as a valuable resource for securely distributing information. Here we take advantage of hybrid spin and orbital angular momentum states to access a high dimensional encoding space, demonstrating a protocol that is easily scalable in both dimension and participants. To illustrate the versatility of our approach, we first demonstrate the protocol in two dimensions, extending the number of participants to ten, and then demonstrate the protocol in three dimensions with three participants, the highest realisation of participants and dimensions thus far. We reconstruct secrets depicted as images with a fidelity of up to 0.979. Moreover, our scheme exploits the use of conventional linear optics to emulate the quantum gates needed for transitions between basis modes on a high dimensional Hilbert space with the potential of up to 1.225 bits of encoding capacity per transmitted photon. Our work offers a practical approach for sharing information across multiple parties, a crucial element of any quantum network.

Large-alphabet quantum key distribution using spatially encoded light

Most quantum key distribution protocols using a two-dimensional basis, such as HV polarization as first proposed by Bennett and Brassard in 1984, are limited to a key generation density of 1 bit per photon. We increase this key density by encoding information in the transverse spatial displacement of the used photons. Employing this higher-dimensional Hilbert space together with modern single-photon-detecting cameras, we demonstrate a proof-of-principle large-alphabet quantum key distribution experiment with 1024 symbols and a shared information between sender and receiver of 7 bit per photon.

Quantum mechanics with patterns of light: Progress in high dimensional and multidimensional entanglement with structured light

Quantum mechanics is now a mature topic dating back more than a century. During its scientific development, it fostered many technological advances that now are integrated into our everyday lives. More recently, over the past few decades, the authors have seen the emergence of a second quantum revolution, ushering in control of quantum states. Here, the spatial modes of light, “patterns of light,” hold tremendous potential: light is weakly interacting and so an attractive avenue for exploring entanglement preservation in open systems, while spatial modes of light offer a route to high dimensional Hilbert spaces for larger encoding alphabets, promising higher information capacity per photon, better security, and enhanced robustness to noise. Yet, progress in harnessing high dimensional spatial mode entanglement remains in its infancy. Here, the authors review the recent progress in this regard, outlining the core concepts in a tutorial manner before delving into the advances made in creation, manipulation, and detection of such quantum states. The authors cover advances in using orbital angular momentum as well as vectorial states that are hybrid entangled, combining spatial modes with polarization to form an infinite set of two-dimensional spaces: multidimensional entanglement. The authors highlight the exciting work in pushing the boundaries in both the dimension and the photon number, before finally summarizing the open challenges, and the questions that remain unanswered.

Salience-aware adaptive resonance theory for large-scale sparse data clustering

Sparse data is known to pose challenges to cluster analysis, as the similarity between data tends to be ill-posed in the high-dimensional Hilbert space. Solutions in the literature typically extend either k-means or spectral clustering with additional steps on representation learning and/or feature weighting. However, adding these usually introduces new parameters and increases computational cost, thus inevitably lowering the robustness of these algorithms when handling massive ill-represented data. To alleviate these issues, this paper presents a class of self-organizing neural networks, called the salience-aware adaptive resonance theory (SA-ART) model. SA-ART extends Fuzzy ART with measures for cluster-wise salient feature modeling. Specifically, two strategies, i.e. cluster space matching and salience feature weighting, are incorporated to alleviate the side-effect of noisy features incurred by high dimensionality. Additionally, cluster weights are bounded by the statistical means and minimums of the samples therein, making the learning rate also self-adaptable. Notably, SA-ART allows clusters to have their own sets of self-adaptable parameters. It has the same time complexity of Fuzzy ART and does not introduce additional hyperparameters that profile cluster properties. Comparative experiments have been conducted on the ImageNet and BlogCatalog datasets, which are large-scale and include sparsely-represented data. The results show that, SA-ART achieves 51.8% and 18.2% improvement over Fuzzy ART, respectively. While both have a similar time cost, SA-ART converges faster and can reach a better local minimum. In addition, SA-ART consistently outperforms six other state-of-the-art algorithms in terms of precision and F1 score. More importantly, it is much faster and exhibits stronger robustness to large and complex data.

Sparse and collaborative representation based kernel pairwise linear regression for image set classification

As an important part of expert and intelligent systems, image set classification has been widely applied to many real-life scenarios including surveillance videos, multi-view camera networks and personal albums. Compared with single image based classification, it is more promising and therefore has attracted significant research attention in recent years. Traditional pairwise linear regression classification (PLRC) introduces the unrelated subspace to increase the discriminative information, and it shows a demonstrated better performance on image set classification. However, the unrelated subspace constructed by PLRC is not optimal and PLRC may fail for well classifying image sets that are not linear separable, or when the axes of linear regression of class-specific samples of different classes have an intersection. In this paper, two new unrelated subspace construction strategies are proposed based on sparse and collaborative representation, respectively. Then, based on them, a new image set classification framework, kernel pairwise linear regression classification (KPLRC) is developed. KPLRC is a nonlinear extension of PLRC and can overcome the drawback of PLRC. Specifically, KPLRC embeds the related and unrelated gallery sets and probe sets into the high-dimensional Hilbert space, and in the kernel space, the data become more linear separable. Extensive experiments on four well-known databases prove that the classification accuracies of KPLRC are better than that of PLRC and several state-of-the-art classifiers. These quantitative assessments reinforce the significance as well as the importance of embedding the proposed method in other intelligent systems application areas.

Library-assisted nonlinear blind separation and annotation of pure components from a single 1H nuclear magnetic resonance mixture spectra

Due to its capability for high-throughput screening 1H nuclear magnetic resonance (NMR) spectroscopy is commonly used for metabolite research. The key problem in 1H NMR spectroscopy of multicomponent mixtures is overlapping of component signals and that is increasing with the number of components, their complexity and structural similarity. It makes metabolic profiling, that is carried out through matching acquired spectra with metabolites from the library, a hard problem. Here, we propose a method for nonlinear blind separation of highly correlated components spectra from a single 1H NMR mixture spectra. The method transforms a single nonlinear mixture into multiple high-dimensional reproducible kernel Hilbert Spaces (mRKHSs). Therein, highly correlated components are separated by sparseness constrained nonnegative matrix factorization in each induced RKHS. Afterwards, metabolites are identified through comparison of separated components with the library comprised of 160 pure components. Thereby, a significant number of them are expected to be related with diabetes type 2. Conceptually similar methodology for nonlinear blind separation of correlated components from two or more mixtures is presented in the Supplementary material. Single-mixture blind source separation is exemplified on: (i) annotation of five components spectra separated from one 1H NMR model mixture spectra; (ii) annotation of fifty five metabolites separated from one 1H NMR mixture spectra of urine of subjects with and without diabetes type 2. Arguably, it is for the first time a method for blind separation of a large number of components from a single nonlinear mixture has been proposed. Moreover, the proposed method pinpoints urinary creatine, glutamic acid and 5-hydroxyindoleacetic acid as the most prominent metabolites in samples from subjects with diabetes type 2, when compared to healthy controls.

Kernel-based Fisher discriminant analysis on the Riemannian manifold for nuclear atypia scoring of breast cancer

Breast carcinoma is the most prevalent type of malignancy among women worldwide. Breast cancer grading often termed as Nuclear Atypia Scoring (NAS) forms a significant factor in determining individualized treatment plans and in the prognosis of the disease. For addressing the problem of breast cancer grading, we attempt to model the variations in features between histopathological images of different cancer grades and thereby explore the discriminative information concealed in these variations. In this regard, we aggregate multiple correlated features from the images using the geodesic geometric mean of the region covariances, to obtain the gmRC descriptors. As these gmRC descriptors are symmetric positive definite (SPD) matrices lying on the non-Euclidean Riemannian manifold, the discriminant analysis techniques developed for the Euclidean framework may not be appropriate. Hence, we propose a kernel-based Fisher discriminant analysis on the Riemannian manifold (KFDAR), that exploits the kernel trick for embedding the non-linear Riemannian manifold M into a higher dimensional linear Hilbert space H, which are then reduced to a low-dimensional and more discriminative subspace, where the samples become linearly separable. The kernel approach formulated for the Hilbert space embedding and for the kernel discriminant analysis is based on three Riemannian distance metrics: the log-Euclidean metric and the two symmetrized Bregman divergences – Stein and Jeffrey divergences. The experimental results show that this mapping to a highly discriminative space has succeeded in well-separating the histopathological images belonging to different cancer grades and hence it qualitatively and quantitatively outperforms the existing algorithms for cancer grading.

Microwave-induced orbital angular momentum transfer

The microwave-induced orbital angular momentum (OAM) transfer from a Laguerre-Gaussian (LG) beam to a weak plane-wave is studied in a closed-loop four-level ladder-type atomic system. The analytical investigation shows that the generated fourth field is an LG beam with the same OAM of the applied LG field. Moreover, the microwave-induced subluminal generated pulse can be switched to the superluminal one only by changing the relative phase of applied fields. It is shown that the OAM transfer in subluminal regime is accompanied by a slightly absorption, however, it switches to the slightly gain in superluminal regime. The transfer of light’s OAM and control of the group velocity of the generated pulse can prepare a high-dimensional Hilbert space which has a major role in quantum communication and information processing.

Mixed Region Covariance Discriminative Learning for Image Classification on Riemannian Manifolds

Covariance matrices, known as symmetric positive definite (SPD) matrices, are usually regarded as points lying on Riemannian manifolds. We describe a new covariance descriptor, which could improve the discriminative learning ability of region covariance descriptor by taking into account the mean of feature vectors. Due to the specific geometry of Riemannian manifolds, classical learning methods cannot be directly used on it. In this paper, we propose a subspace projection framework for the classification task on Riemannian manifolds and give the mathematical derivation for it. It is different from the common technique used for Riemannian manifolds, which is to explicitly project the points from a Riemannian manifold onto Euclidean space based upon a linear hypothesis. Under the proposed framework, we define a Gaussian Radial Basis Function- (RBF-) based kernel with a Log-Euclidean Riemannian Metric (LERM) to embed a Riemannian manifold into a high-dimensional Reproducing Kernel Hilbert Space (RKHS) and then project it onto a subspace of the RKHS. Finally, a variant of Linear Discriminative Analyze (LDA) is recast onto the subspace. Experiments demonstrate the considerable effectiveness of the mixed region covariance descriptor and the proposed method.

Experimental Engineering of Arbitrary Qudit States with Discrete-Time Quantum Walks.

The capability to generate and manipulate quantum states in high-dimensional Hilbert spaces is a crucial step for the development of quantum technologies, from quantum communication to quantum computation. One-dimensional quantum walk dynamics represents a valid tool in the task of engineering arbitrary quantum states. Here we affirm such potential in a linear-optics platform that realizes discrete-time quantum walks in the orbital angular momentum degree of freedom of photons. Different classes of relevant qudit states in a six-dimensional space are prepared and measured, confirming the feasibility of the protocol. Our results represent a further investigation of quantum walk dynamics in photonics platforms, paving the way for the use of such a quantum state-engineering toolbox for a large range of applications.

Full expectation-value statistics for randomly sampled pure states in high-dimensional quantum systems.

We explore how the expectation values 〈ψ|A|ψ〉 of a largely arbitrary observable A are distributed when normalized vectors |ψ〉 are randomly sampled from a high-dimensional Hilbert space. Our analytical results predict that the distribution exhibits a very narrow peak of approximately Gaussian shape, while the tails significantly deviate from a Gaussian behavior. In the important special case that the eigenvalues of A satisfy Wigner's semicircle law, the expectation-value distribution for asymptotically large dimensions is explicitly obtained in terms of a large deviation function, which exhibits two symmetric nonanalyticities akin to critical points in thermodynamics.

On-chip transverse-mode entangled photon pair source

Integrated entangled photon pair source is an essential resource for both fundamental investigations and practical applications of quantum information science. Currently there have been several types of entanglement, among which the transverse-mode entanglement is becoming attractive because of its unique advantages. Here, we report an on-chip transverse-mode entangled photon pair source via the spontaneous four-wave mixing processes in a multimode silicon waveguide. Transverse-mode photon pairs are verified over multiple frequency channels within a bandwidth of ~2 THz, and a maximally entangled Bell state is also produced with a net fidelity of 0.96 ± 0.01. Our entangled photon pair source is the key element for quantum photonics based on transverse-mode, and also has the possibility to extend to higher-dimensional Hilbert space. Furthermore, the transverse-mode entanglement can be converted coherently to path and polarization entanglement, which paves the way to realizing highly complex quantum photonic circuits with multiple degrees of freedom.

Efficient Teleportation for High-Dimensional Quantum Computing

Performance of an entangled quantum channel is affected by classical feedback assisted in quantum communications. For example, in quantum-gate teleportation schemes, the capacity of an independent entangled quantum channel is reduced by utilizing two-way simultaneous classical communication (TWSCC↔). However, by exploiting the superposition of high-dimensional quantum channels, the transmission efficiency of the quantum-gate teleportation can be dramatically improved with TWSCC↔. In this study, we investigate the possibility of achieving an efficient scheme of nonlocal high-dimensional quantum computation by using hyperentangled photon pairs, atoms, and an optical micro-resonator coupled system. The feasibility and efficiency of the scheme are also discussed. Results prove that, for nonlocal quantum computing, high-dimensional quantum operation performs better than traditional methods that decompose the high-dimensional Hilbert space into two-dimensional quantum space under limited prior shared maximally entangled resources.

Mapping Twisted Light into and out of a Photonic Chip.

Twisted light carrying orbital angular momentum (OAM) provides an additional degree of freedom for modern optics and an emerging resource for both classical and quantum information technologies. Its inherently infinite dimensions can potentially be exploited by using mode multiplexing to enhance data capacity for sustaining the unprecedented growth in big data and internet traffic and can be encoded to build large-scale quantum computing machines in high-dimensional Hilbert space. While the emission of twisted light from the surface of integrated devices to free space has been widely investigated, the transmission and processing inside a photonic chip remain to be addressed. Here, we present the first laser-direct-written waveguide being capable of supporting OAM modes and experimentally demonstrate a faithful mapping of twisted light into and out of a photonic chip. The states OAM_{0}, OAM_{-1}, OAM_{+1}, and their superpositions can transmit through the photonic chip with a total efficiency up to 60% with minimal crosstalk. In addition, we present the transmission of quantum twisted light states of single photons and measure the output states with single-photon imaging. Our results may add OAM as a new degree of freedom to be transmitted and manipulated in a photonic chip for high-capacity communication and high-dimensional quantum information processing.

Multi-model fusion metric learning for image set classification

Multi-model image set classification is an important research topic in pattern recognition, because multiple modeling of image sets can represent different characteristics. This paper focuses on the problem of two-model image set classification. We use the mean vector, subspace and covariance matrix to jointly represent an image set, due to they contain different discriminative information. Since the above three representation methods lie on different spaces, the classical multi-view learning methods cannot be directly utilized. In order to reduce the dissimilarity between the heterogeneous spaces, a new multi-model fusion metric learning (MMFML) framework is developed, which includes three two-model fusion Modes. Specifically, by exploiting corresponding kernel function, the original represent data is first embedded into the high dimensional Hilbert spaces to reduce the gaps between the heterogeneous spaces. The final objective function is then learned from the Hilbert space to a common Euclidean subspace, and in the final subspace the classical Euclidean distance is used to classify image sets. Experimental results on Honda/UCSD, Youtube Celebrities and ETH-80 three databases demonstrate the effectiveness of our proposed method.

Image-based 3D model retrieval using manifold learning

We propose a new framework for image-based three-dimensional (3D) model retrieval. We first model the query image as a Euclidean point. Then we model all projected views of a 3D model as a symmetric positive definite (SPD) matrix, which is a point on a Riemannian manifold. Thus, the image-based 3D model retrieval is reduced to a problem of Euclid-to-Riemann metric learning. To solve this heterogeneous matching problem, we map the Euclidean space and SPD Riemannian manifold to the same high-dimensional Hilbert space, thus shrinking the great gap between them. Finally, we design an optimization algorithm to learn a metric in this Hilbert space using a kernel trick. Any new image descriptors, such as the features from deep learning, can be easily embedded in our framework. Experimental results show the advantages of our approach over the state-of-the-art methods for image-based 3D model retrieval.

High-Dimensional Quantum Communication Complexity beyond Strategies Based on Bell's Theorem.

Quantum resources can improve communication complexity problems (CCPs) beyond their classical constraints. One quantum approach is to share entanglement and create correlations violating a Bell inequality, which can then assist classical communication. A second approach is to resort solely to the preparation, transmission, and measurement of a single quantum system, in other words, quantum communication. Here, we show the advantages of the latter over the former in high-dimensional Hilbert space. We focus on a family of CCPs, based on facet Bell inequalities, study the advantage of high-dimensional quantum communication, and realize such quantum communication strategies using up to ten-dimensional systems. The experiment demonstrates, for growing dimension, an increasing advantage over quantum strategies based on Bell inequality violation. For sufficiently high dimensions, quantum communication also surpasses the limitations of the postquantum Bell correlations obeying only locality in the macroscopic limit. We find that the advantages are tied to the use of measurements that are not rank-one projective, and provide an experimental semi-device-independent falsification of such measurements in Hilbert space dimension six.

Optimizing the signal-to-noise ratio of biphoton distribution measurements

Single-photon-sensitive cameras can now be used as massively parallel coincidence counters for entangled photon pairs. This enables measurement of biphoton joint probability distributions with orders-of-magnitude greater dimensionality and faster acquisition speeds than traditional raster scanning of point detectors; to date, however, there has been no general formula available to optimize data collection. Here we analyze the dependence of such measurements on count rate, detector noise properties, and threshold levels. We derive expressions for the biphoton joint probability distribution and its signal-to-noise ratio (SNR), valid beyond the low-count regime up to detector saturation. The analysis gives operating parameters for global optimum SNR that may be specified prior to measurement. We find excellent agreement with experimental measurements within the range of validity, and discuss discrepancies with the theoretical model for high thresholds. This work enables optimized measurement of the biphoton joint probability distribution in high-dimensional joint Hilbert spaces.