arXiv subjects

Lijian Zhang

Publications and source records attributed to Lijian Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Experimental benchmarking of quantum state overlap estimation strategies with photonic systems

Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing. While various strategies using distinct quantum measurements have been proposed for overlap estimation, the lack of experimental benchmarks on estimation precision limits strategy selection in different situations. Here we compare the performance of four practical strategies for overlap estimation, including tomography-tomography, tomography-projection, Schur collective measurement and optical swap test using photonic quantum systems. We encode the quantum states on the polarization and path degrees of freedom of single photons. The corresponding measurements are performed by photon detection on certain modes following single-photon mode transformation or two-photon interference. We further propose an adaptive strategy with optimized precision in full-range overlap estimation. Our results shed new light on extracting the parameter of interest from quantum systems, prompting the design of efficient quantum protocols.

quant-ph↗

arXiv2024

Achieving the Multi-parameter Quantum Cramér-Rao Bound with Antiunitary Symmetry

The estimation of multiple parameters is a ubiquitous requirement in many quantum metrology applications. However, achieving the ultimate precision limit, i.e. the quantum Cramér-Rao bound, becomes challenging in these scenarios compared to single parameter estimation. To address this issue, optimizing the parameters encoding strategies with the aid of antiunitary symmetry is a novel and comprehensive approach. For demonstration, we propose two types of quantum statistical models exhibiting antiunitary symmetry in experiments. The results showcase the simultaneous achievement of ultimate precision for multiple parameters without any trade-off and the precision is improved at least twice compared to conventional encoding strategies. Our work emphasizes the significant potential of antiunitary symmetry in addressing multi-parameter estimation problems.

quant-ph↗

arXiv2024

Progress and Perspectives on Weak-value Amplification

Weak-value amplification (WVA) is a metrological protocol that effectively amplifies ultra-small physical effects, making it highly applicable in the fields of quantum sensing and metrology. However, the amplification effect is achieved through post-selection, which leads to a significant decrease in signal intensity. Consequently, there is a heated debate regarding the trade-off between the amplification effect and the success probability of post-selection, questioning whether WVA surpasses conventional measurement (CM) in terms of measurement precision. Extensive research indicates that the specific theoretical assumptions and experimental conditions play crucial roles in determining the respective advantages of WVA and CM. WVA provides new perspectives for recognizing the important role of post-selection in precision metrology. It demonstrates significant advantages in two aspects: (i) WVA based on the phase space interaction provides feasible strategies to practically achieve the Heisenberg-scaling precision using only classical resources. (ii) WVA exhibits robustness against certain types of technical noise and imperfections of detectors. Moreover, WVA allows for various modifications to extend the applicable scope and enhance the metrological performance in corresponding situations. Despite substantial progress in recent years, the inherent connection between the advantages of WVA and its unique features remains incompletely understood. In this paper, we systematically review the recent advances in the WVA scheme, with a particular focus on the ultimate precision of WVA under diverse conditions. Our objective is to provide a comprehensive perspective on the benefits of WVA in precision measurement and facilitate the realization of its full potential.

quant-ph↗

arXiv2024

Resource-efficient Direct Characterization of General Density Matrix

Sequential weak measurements allow the direct extraction of individual density-matrix elements instead of globally reconstructing the whole density matrix, opening a new avenue for the characterization of quantum systems. Nevertheless, the requirement of multiple coupling for each qudit of quantum systems and the lack of appropriate precision evaluation constraint its applicability extension, especially for multi-qudit quantum systems. Here, we propose a resource-efficient scheme (RES) to directly characterize the density matrix of general multi-qudit systems, which not only optimizes the measurements but also establishes a feasible estimation analysis. In this scheme, an efficient observable of quantum system is constructed such that a single meter state coupled to each qudit is sufficient to extract the corresponding density-matrix element. An appropriate model based on the statistical distribution of errors are used to evaluate the precision and feasibility of the scheme. We experimentally apply the RES to the direct characterization of general single-photon qutrit states and two-photon entangled states. The results show that the RES outperforms the sequential schemes in terms of efficiency and precision in both weak- and strong- coupling scenarios. This work sheds new light on the practical characterization of large-scale quantum systems and investigation of their non-classical properties.

quant-ph↗

arXiv2024

Enhancing Cross-Dataset EEG Emotion Recognition: A Novel Approach with Emotional EEG Style Transfer Network

Recognizing the pivotal role of EEG emotion recognition in the development of affective Brain-Computer Interfaces (aBCIs), considerable research efforts have been dedicated to this field. While prior methods have demonstrated success in intra-subject EEG emotion recognition, a critical challenge persists in addressing the style mismatch between EEG signals from the source domain (training data) and the target domain (test data). To tackle the significant inter-domain differences in cross-dataset EEG emotion recognition, this paper introduces an innovative solution known as the Emotional EEG Style Transfer Network (E$^2$STN). The primary objective of this network is to effectively capture content information from the source domain and the style characteristics from the target domain, enabling the reconstruction of stylized EEG emotion representations. These representations prove highly beneficial in enhancing cross-dataset discriminative prediction. Concretely, E$^2$STN consists of three key modules\textemdash transfer module, transfer evaluation module, and discriminative prediction module\textemdash which address the domain style transfer, transfer quality evaluation, and discriminative prediction, respectively. Extensive experiments demonstrate that E$^2$STN achieves state-of-the-art performance in cross-dataset EEG emotion recognition tasks.

cs.HC↗

arXiv2023

Experimental 3D super-localization with Laguerre-Gaussian modes

Improving three-dimensional (3D) localization precision is of paramount importance for super-resolution imaging. By properly engineering the point spread function (PSF), such as utilizing Laguerre-Gaussian (LG) modes and their superposition, the ultimate limits of 3D localization precision can be enhanced. However, achieving these limits is challenging, as it often involves complicated detection strategies and practical limitations. In this work, we rigorously derive the ultimate 3D localization limits of LG modes and their superposition, specifically rotation modes, in the multi-parameter estimation framework. Our findings reveal that a significant portion of the information required for achieving 3D super-localization of LG modes can be obtained through feasible intensity detection. Moreover, the 3D ultimate precision can be achieved when the azimuthal index $l$ is zero. To provide a proof-of-principle demonstration, we develop an iterative maximum likelihood estimation (MLE) algorithm that converges to the 3D position of a point source, considering the pixelation and detector noise. The experimental implementation exhibits an improvement of up to two-fold in lateral localization precision and up to twenty-fold in axial localization precision when using LG modes compared to Gaussian mode. We also showcase the superior axial localization capability of the rotation mode within the near-focus region, effectively overcoming the limitations encountered by single LG modes. Notably, in the presence of realistic aberration, the algorithm robustly achieves the Cramér-Rao lower bound. Our findings provide valuable insights for evaluating and optimizing the achievable 3D localization precision, which will facilitate the advancements in super-resolution microscopy.

quant-ph↗

arXiv2023

Quantum steering for two-mode states with Continuous-variable in laser channel

The Einstein-Podolsky-Rosen steering is an important resource for one-sided device independent quantum information processing. This steering property will be destroyed during the interaction between quantum system and environment for some practical applications. In this paper, we use the representation of characteristic function for probability to examine the quantum steering of two-mode states with continuous-variable in laser channel, where both the gain factor and the loss effect are considered. Firstly, we analyse the steering time of two-mode squeezed vacuum state under one-mode and two-mode laser channel respectively. We find the gain process will introduce additional noise to the two-mode squeezed vacuum state such that the steerable time is reduced. Secondly, by quantising quantum Einstein-Podolsky-Rosen steering, it shows that two-side loss presents a smaller steerability than one-side loss although they share the same two-way steerable time. In addition, we find the more gained party can steer the others state, while the other party cannot steer the gained party in a certain threshold value. In this sense, it seems that the gain effect in one party is equivalent to the loss effect in the others party. Our results pave way for the distillation of Einstein-Podolsky-Rosen steering and the quantum information processing in practical quantum channels.

quant-ph↗

arXiv2023

EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition

As the key to realizing aBCIs, EEG emotion recognition has been widely studied by many researchers. Previous methods have performed well for intra-subject EEG emotion recognition. However, the style mismatch between source domain (training data) and target domain (test data) EEG samples caused by huge inter-domain differences is still a critical problem for EEG emotion recognition. To solve the problem of cross-dataset EEG emotion recognition, in this paper, we propose an EEG-based Emotion Style Transfer Network (E2STN) to obtain EEG representations that contain the content information of source domain and the style information of target domain, which is called stylized emotional EEG representations. The representations are helpful for cross-dataset discriminative prediction. Concretely, E2STN consists of three modules, i.e., transfer module, transfer evaluation module, and discriminative prediction module. The transfer module encodes the domain-specific information of source and target domains and then re-constructs the source domain's emotional pattern and the target domain's statistical characteristics into the new stylized EEG representations. In this process, the transfer evaluation module is adopted to constrain the generated representations that can more precisely fuse two kinds of complementary information from source and target domains and avoid distorting. Finally, the generated stylized EEG representations are fed into the discriminative prediction module for final classification. Extensive experiments show that the E2STN can achieve the state-of-the-art performance on cross-dataset EEG emotion recognition tasks.

eess.SP↗

arXiv2023

Adaptive protocols for SU(11) interferometers to achieve ab initio phase estimation at the Heisenberg limit

The precision of phase estimation with interferometers can be greatly enhanced using non-classical quantum states, and the SU(11) interferometer is an elegant scheme, which generates two-mode squeezed state internally and also amplifies the signal. It has been shown in [Phys. Rev. A {\bf 95}, 063843 (2017)] that the photon-number measurement can achieve the Heisenberg limit, but only for estimating a small phase shift. We relax the constraint on the phase size by considering two adaptive protocols: one also uses the photon-number measurement with a specially tuned sequence of feedback phase; the other implements the yet-to-be-realised optimal measurement but without fine tuning.

quant-ph↗

arXiv2022

Interaction-free, single-pixel quantum imaging with undetected photons

A typical imaging scenario requires three basic ingredients: 1. a light source that emits light, which in turn interacts and scatters off the object of interest; 2. detection of the light being scattered from the object and 3. a detector with spatial resolution. These indispensable ingredients in typical imaging scenarios may limit their applicability in the imaging of biological or other sensitive specimens due to unavailable photon-starved detection capabilities and inevitable damage induced by interaction. Here, we propose and experimentally realize a quantum imaging protocol that alleviates all three requirements. By embedding a single-photon Michelson interferometer into a nonlinear interferometer based on induced coherence and harnessing single-pixel imaging technique, we demonstrate interaction-free, single-pixel quantum imaging of a structured object with undetected photons. Thereby, we push the capability of quantum imaging to the extreme point in which no interaction is required between object and photons and the detection requirement is greatly reduced. Our work paves the path for applications in characterizing delicate samples with single-pixel imaging at silicon-detectable wavelengths.

quant-ph↗

arXiv2022

A Scheme for Deterministic N-photon State Generation Using Lithium Niobate on Insulator Device

Large-photon-number quantum state is a fundamental but non-resolved request for practical quantum information applications. Here we propose an N-photon state generation scheme that is feasible and scalable, using lithium niobate on insulator circuits. Such scheme is based on the integration of a common building block called photon-number doubling unit (PDU), for deterministic single-photon parametric down-conversion and up-conversion. The PDU relies on 10^7-optical-quality-factor resonator and mW-level on-chip power, which is within the current fabrication and experiment limits. N-photon state generation schemes, with cluster and GHZ state as examples, are shown for different quantum tasks.

quant-ph↗

arXiv2022

Distillation of Gaussian Einstein-Podolsky-Rosen steering with noiseless linear amplification

Einstein-Podolsky-Rosen (EPR) steering is one of the most intriguing features of quantum mechanics and an important resource for quantum communication. For practical applications, it remains a challenge to protect EPR steering from decoherence due to its intrinsic difference from entanglement. Here, we experimentally demonstrate the distillation of Gaussian EPR steering and entanglement in lossy and noisy environments using measurement-based noiseless linear amplification. Different from entanglement distillation, the extension of steerable region happens in the distillation of EPR steering besides the enhancement of steerabilities. We demonstrate that the two-way or one-way steerable region is extended after the distillation of EPR steering when the NLA is implemented based on Bob's or Alice's measurement results. We also show that the NLA helps to extract secret key from insecure region in one-sided device-independent quantum key distribution with EPR steering. Our work paves the way for quantum communication exploiting EPR steering in practical quantum channels.

quant-ph↗

arXiv2022

Efficient Distinction between Quantum Direct and Common Causes and its Experimental Verification

Identifying the causal structures between two statistically correlated events has been widely investigated in many fields of science. While some of the well-studied classical methods are carefully generalized to quantum version of causal inference for certain cases, an effective and efficient way to detect the more general quantum causal structures is still lacking. Here, we introduce a quantity named `Causal Determinant' to efficiently identify the quantum causal structures between two quantum systems and experimentally verify the validity of the method. According to the causal determinant, the quantum direct cause imposed by an arbitrary unitary operator can be perfectly discriminated with the quantum common cause, in which the two quantum systems share a joint quantum state. In addition, the causal determinant has the capability to discriminate between more general causal structures and predict the range of their parameters. The ability to detect more general quantum causal structures of our method can shed new light on the field of quantum causal inference.

quant-ph↗

arXiv2021

Progressive Graph Convolution Network for EEG Emotion Recognition

Studies in the area of neuroscience have revealed the relationship between emotional patterns and brain functional regions, demonstrating that dynamic relationships between different brain regions are an essential factor affecting emotion recognition determined through electroencephalography (EEG). Moreover, in EEG emotion recognition, we can observe that clearer boundaries exist between coarse-grained emotions than those between fine-grained emotions, based on the same EEG data; this indicates the concurrence of large coarse- and small fine-grained emotion variations. Thus, the progressive classification process from coarse- to fine-grained categories may be helpful for EEG emotion recognition. Consequently, in this study, we propose a progressive graph convolution network (PGCN) for capturing this inherent characteristic in EEG emotional signals and progressively learning the discriminative EEG features. To fit different EEG patterns, we constructed a dual-graph module to characterize the intrinsic relationship between different EEG channels, containing the dynamic functional connections and static spatial proximity information of brain regions from neuroscience research. Moreover, motivated by the observation of the relationship between coarse- and fine-grained emotions, we adopt a dual-head module that enables the PGCN to progressively learn more discriminative EEG features, from coarse-grained (easy) to fine-grained categories (difficult), referring to the hierarchical characteristic of emotion. To verify the performance of our model, extensive experiments were conducted on two public datasets: SEED-IV and multi-modal physiological emotion database (MPED).

cs.CV↗

arXiv2021

Directly Characterizing the Coherence of Quantum Detectors by Sequential Measurement

The quantum properties of quantum measurements are indispensable resources in quantum information processing and have drawn extensive research interest. The conventional approach to reveal the quantum properties relies on the reconstruction of the entire measurement operators by quantum detector tomography. However, many specific properties can be determined by a part of matrix entries of the measurement operators, which provides us the possibility to simplify the process of property characterization. Here, we propose a general framework to directly obtain individual matrix entries of the measurement operators by sequentially measuring two non-compatible observables. This method allows us to circumvent the complete tomography of the quantum measurement and extract the useful information for our purpose. We experimentally implement this scheme to monitor the coherent evolution of a general quantum measurement by determining the off-diagonal matrix entries. The investigation of the measurement precision indicates the good feasibility of our protocol to the arbitrary quantum measurements. Our results pave the way for revealing the quantum properties of quantum measurements by selectively determining the matrix entries of the measurement operators.

quant-ph↗

arXiv2021

Direct Characterization of Quantum Measurements using Weak Values

The time-symmetric formalism endows the weak measurement and its outcome, the weak value,many unique features. In particular, it allows a direct tomography of quantum states without resort to complicated reconstruction algorithms and provides an operational meaning to wave functions and density matrices. Here, we propose and experimentally demonstrate the direct tomography of a measurement apparatus by taking the backward direction of weak measurement formalism. Our protocol works rigorously with the arbitrary measurement strength, which offers an improved accuracy and precision. The precision can be further improved by taking into account the completeness condition of the measurement operators, which also ensures the feasibility of our protocol for the characterization of the arbitrary quantum measurement. Our work provides new insight on the symmetry between quantum states and measurements, as well as an efficient method to characterize a measurement apparatus.

quant-ph↗

arXiv2021

Quantum verification of NP problems with single photons and linear optics

Quantum computing is seeking to realize hardware-optimized algorithms for application-related computational tasks. NP (nondeterministic-polynomial-time) is a complexity class containing many important but intractable problems like the satisfiability of potentially conflict constraints (SAT). According to the well-founded exponential time hypothesis, verifying an SAT instance of size $n$ requires generally the complete solution in an $O(n)$-bit proof. In contrast, quantum verification algorithms, which encode the solution into quantum bits rather than classical bit strings, can perform the verification task with quadratically reduced information about the solution in $\tilde{O}(\sqrt{n})$ qubits. Here we realize the quantum verification machine of SAT with single photons and linear optics. By using tunable optical setups, we efficiently verify satisfiable and unsatisfiable SAT instances and achieve a clear completeness-soundness gap even in the presence of experimental imperfections. The protocol requires only unentangled photons, linear operations on multiple modes and at most two-photon joint measurements. These features make the protocol suitable for photonic realization and scalable to large problem sizes. Our results open an essentially new route towards quantum advantages and extend the computational capability of optical quantum computing.

quant-ph↗

arXiv2021

Quantum-limited Localisation and Resolution in Three Dimensions

As a method to extract information from optical system, imaging can be viewed as a parameter estimation problem. The fundamental precision in locating one emitter or estimating the separation between two incoherent emitters is bounded below by the multiparameter quantum Cramer-Rao bound (QCRB).Multiparameter QCRB gives an intrinsic bound in parameter estimation. We determine the ultimate potential of quantum-limited imaging for improving the resolution of a far-field, diffraction-limited within the paraxial approximation. We show that the quantum Fisher information matrix (QFIm) about one emitter's position is independent on the true value of it. We calculate the QFIm of two unequal-brightness emitters' relative positions and intensities, the results show that only when the relative intensity and centroids of two point sources including longitudinal and transverse direction are known exactly, the separation in different directions can be estimated simultaneously with finite precision. Our results give the upper bounds on certain far-field imaging technology and will find wide applications from microscopy to astrometry.

quant-ph↗