State Discrimination Research Articles

Cryptanalysis and Improvement to the Quantum Private Query Protocol for Enhancing Database Privacy

Abstract In order to protect the privacy of query user and database, some QKD-based quantum private query (QPQ) protocols were proposed. One example is the protocol proposed by Zhou et al., in which the user makes initial quantum states and derives the key bit by comparing the initial quantum state and the outcome state returned from the database by ctrl or shift mode instead of announcing
two non-orthogonal qubits as others which may leak part secret information. In some extent, the security of the database and the privacy of the user are strengthened. Unfortunately, we find that in this protocol, the dishonest user can obtain, utilizing unambiguous state discrimination, much more database information than that is analyzed in Zhou et al.’s original research. To strengthen the database security, we improved the mentioned protocol by modifying the information returned by the database in various ways. The analysis indicates that the security of the improved protocols is greatly enhanced.

Unambiguous discrimination of general quantum operations.

The discrimination of quantum operations has long been an intriguing challenge, with theoretical research notably advancing our understanding of the quantum features in discriminating quantum objects. This challenge is closely related to the discrimination of quantum states, and proof-of-principle demonstrations of the latter have already been realized using optical photons. However, the experimental demonstration of discriminating general quantum operations, including both unitary and nonunitary operations, has remained elusive. In general quantum systems, especially those with high dimensions, the preparation of arbitrary quantum states and the implementation of arbitrary quantum operations and generalized measurements are nontrivial tasks. Here, we experimentally demonstrate the optimal unambiguous discrimination of up to six displacement operators and the unambiguous discrimination of nonunitary quantum operations. Our results demonstrate powerful tools for experimental research in quantum information processing and are expected to stimulate a wide range of valuable applications in the field of quantum sensing.

Quantum-inspired classification based on quantum state discrimination

Quantum-inspired classification based on quantum state discrimination

Operational interpretation of the Choi rank through exclusion tasks

The Choi state is an indispensable tool in the study and analysis of quantum channels. Considering a channel in terms of its associated Choi state can greatly simplify problems. It also offers an alternative approach to the characterization of a channel, with properties of the Choi state providing novel insight into a channel's behavior. The rank of a Choi state, termed the Choi rank, has proven to be an important characterizing property, and here, its significance is further elucidated through an operational interpretation. The Choi rank is shown to provide a universal bound on how successfully two agents, Alice and Bob, can perform an entanglement-assisted exclusion task. The task can be considered an extension of superdense coding, where Bob can only output information about Alice's encoded bit string with certainty. Conclusive state exclusion, in place of state discrimination, is therefore considered at the culmination of the superdense coding protocol. In order to prove this result, a necessary condition for conclusive k-state exclusion of a set of states is presented in order to achieve this result, and the notions of weak and strong exclusion are introduced. Published by the American Physical Society 2024

Witnessing Quantum Incompatibility Structures in High-Dimensional Multimeasurement Systems.

Quantum incompatibility, referred as the phenomenon that some quantum measurements cannot be performed simultaneously, is necessary for various quantum information processing tasks, such as nonlocality and steering. When these applications come to high-dimensional multimeasurement scenarios, it is crucial and challenging to witness the incompatibility of measurements with complex structures. To address this problem, we propose a modified quantum state discrimination protocol that decomposes complex compatibility structures into pairwise ones and employs noise robustness to bound incompatibility structures. We then derive arithmetic bounds for arbitrary measurements and analytical bounds for mutually unbiased bases, and capture some quantum incompatibility structures where measurements are partly compatible and partly incompatible. Finally, we experimentally demonstrate our results and connect them with quantum steering, quantum simulability and quantum communications.

Bolt loosening status classification via wave energy dissipation measurements with temperature-compensated deep learning by piezoelectric active sensing

Abstract In industrial applications, bolts, serving as crucial components, endure substantial loads and are susceptible to loosening problems exacerbated by intricate external environmental factors. The active sensing method based on wave energy dissipation exhibits pronounced sensitivity to axial load fluctuations in bolts and demonstrates extensive applicability, with its measurement indicators directly applicable for discerning the bolt’s status. However, environmental factors, notably temperature, can significantly influence signal energy measurements, and the oversight of temperature impact may result in erroneous state discrimination. To tackle this challenge, this paper introduces a cascaded model comprising a temperature compensation subnetwork and a bolt state discrimination subnetwork. The temperature compensation subnetwork takes temperature and signal energy as inputs and outputs the temperature-compensated signal energy, and conveys the outcomes to the bolt state discrimination subnetwork for state classification. In model design, we quantitatively analyzed the number of convolutional blocks and training epochs for the temperature compensation subnetwork with the aim of enhancing the model’s generalization ability, ultimately determining the model architecture. By comparing the experimental results between a single-task model and a model incorporating the temperature compensation subnetwork, we verified the effectiveness of the temperature compensation subnetwork. The experimental outcomes demonstrate that the proposed cascaded model achieved a state classification accuracy of 98.6% on over 1300 temperature-generalized data points. To comprehensively assess our approach, we conducted detailed comparisons with other bolt loosening monitoring methods, elucidating the effectiveness based on data trends and model design. Experimental results demonstrate that the proposed temperature compensation cascaded model accurately identifies bolt states amidst complex temperature variations.

Entanglement witness and nonlocality in confidence of measurement from multipartite quantum state discrimination

We consider multipartite quantum state discrimination and provide a specific relation between the properties of entanglement witness and quantum nonlocality inherent in the confidence of measurement. We first provide the definition of the confidence of measurement as well as its useful properties for various types of multipartite measurement. We show that globally-maximum confidence that cannot be achieved by local operations and classical communication strongly depends on the existence of a certain type of entanglement witness. We also provide conditions for an upper bound on maximum of locally-achievable confidence. Finally, we establish a method in terms of entanglement witness to construct quantum state ensembles with nonlocal maximum confidence.

Nonprojective Bell-state measurements

The Bell-state measurement (BSM) is the projection of two qubits onto four orthogonal maximally entangled states. Here we first propose how to appropriately define more general BSMs, which have more than four possible outcomes, and then study whether they exist in quantum theory. We observe that nonprojective BSMs can be defined in a systematic way in terms of equiangular tight frames of maximally entangled states, i.e., a set of maximally entangled states, where every pair is equally, and in a sense maximally, distinguishable. We show that there exists a five-outcome BSM through an explicit construction and find that it admits a simple geometric representation. Then we prove that there exists no larger BSM on two qubits by showing that no six-outcome BSM is possible. We also determine the most distinguishable set of six equiangular maximally entangled states and show that it falls only somewhat short of forming a valid quantum measurement. Finally, we study the nonprojective BSM in the contexts of both local state discrimination and entanglement-assisted quantum communication. Our results put forward natural forms of nonprojective joint measurements and provide insight into the geometry of entangled quantum states. Published by the American Physical Society 2024

Minimum-error quantum state discrimination: Extremely tight upper and lower bounds with application in digital quantum communication

Minimum-error quantum state discrimination: Extremely tight upper and lower bounds with application in digital quantum communication

Short term road network Macroscopic Fundamental Diagram parameters and traffic state prediction based on LSTM

Macroscopic Fundamental Diagram (MFD) is widely used in traffic state evaluation due to its description of the macro level of urban road network. This study focuses on the discrimination and short-term prediction of macro traffic states in urban road networks, using MFD combined with FCM clustering for state partitioning to characterize different macro states of the road network. To predict the MFD state, this paper builds two LSTMs to perform short-term predictions on two important parameters in MFD: road network weighted flow qw and road network weighted density kw. The parameters of the first three statistical intervals and the predicted time period are used as inputs to output the MFD parameters for the predicted time period. To ensure that the two LSTM structures and hyper-parameters settings can achieve the best prediction performance for MFD, the parameter optimization process of both should be included in the same search framework for hyper-parameters search. Therefore, this paper uses GA algorithm combined with multi-objective particle swarm optimization algorithm as the solving algorithm, with the accuracy of solving two MFD parameters and the accuracy of MFD point positioning as the hyper-parameters solving objectives. The study was validated using actual road network data from Hong Kong, and the results showed that the method proposed in this paper has an MRE prediction error of less than 7.8% for the two parameters of MFD, and can predict the future temporal trend of the two parameters, demonstrating the feasibility of MFD related predictions. The model's prediction of the overall shape and change trajectory trend of MFD is consistent with reality, and some test sets in MFD state prediction show high accuracy, the overall accuracy is 81.45%. To verify the effectiveness of the multi-objective search algorithm, typical LSTM models and RNN models were used for comparison. The experiment proved that the model used in this study performed better in error control and state prediction. This study explores and practices a short-term prediction method for road network MFD parameters, MFD status, and their changing trends. Provided path reference for urban road network prediction, traffic control status, and MFD related research.

ARNT2 controls prefrontal somatostatin interneurons mediating affective empathy

Empathy, crucial for social interaction, is impaired across various neuropsychiatric conditions. However, the genetic and neural underpinnings of empathy variability remain elusive. By combining forward genetic mapping with transcriptome analysis, we discover that aryl hydrocarbon receptor nuclear translocator 2 (ARNT2) is a key driver modulating observational fear, a basic form of affective empathy. Disrupted ARNT2 expression in the anterior cingulate cortex (ACC) reduces affect sharing in mice. Specifically, selective ARNT2 ablation in somatostatin (SST)-expressing interneurons leads to decreased pyramidal cell excitability, increased spontaneous firing, aberrant Ca2+ dynamics, and disrupted theta oscillations in the ACC, resulting in reduced vicarious freezing. We further demonstrate that ARNT2-expressing SST interneurons govern affective state discrimination, uncovering a potential mechanism by which ARNT2 polymorphisms associate with emotion recognition in humans. Our findings advance our understanding of the molecular mechanism controlling empathic capacity and highlight the neural substrates underlying social affective dysfunctions in psychiatric disorders.

Charge Carrier Capture by Prominent Defect Centers in 4H-SiC

The knowledge of capture properties of electrically active defects is of primary importance as it helps to understand which deep states are effective in controlling the excess free carriers’ lifetime. Combining DLTS capture experiments with thermal emission measurements enables an overall thermodynamic description of deep states, thus making it possible to characterize recombination centers in semiconductor-based devices. In the present study, junction DLTS capture rate measurements were employed to extract the true capture cross-sections (inversely proportional to the carrier lifetime) and capture energy barriers for the main lifetime limiting defects in 4H-SiC (silicon carbide). A peculiar forward bias dependence of the capture parameters was observed for the shallow boron (B) hole trap. Capture rate measurements on the deep boron (D-center) trap also evidenced the presence of two capture mechanisms, thus allowing discrimination of D1 and D2 deep states within the D-center DLTS peak. The results were combined with activation energies and apparent capture cross-sections to obtain the free energy (ΔG) of electronic activation for the analysed deep states.

Majorisation‐minimisation algorithm for optimal state discrimination in quantum communications

AbstractDesigning optimal measurement operators for quantum state discrimination (QSD) is an important problem in quantum communications and cryptography applications. Prior works have demonstrated that optimal quantum measurement operators can be obtained by solving a convex semidefinite program (SDP). However, solving the SDP can represent a high computational burden for many real‐time quantum communication systems. To address this issue, a majorisation‐minimisation (MM)‐based algorithm, called Quantum Majorisation‐Minimisation (QMM) is proposed for solving the QSD problem. In QMM, the authors reparametrise the original objective, then tightly upper‐bound it at any given iterate, and obtain the next iterate as a closed‐form solution to the upper‐bound minimisation problem. Our numerical simulations demonstrate that the proposed QMM algorithm significantly outperforms the state‐of‐the‐art SDP algorithm in terms of speed, while maintaining comparable performance for solving QSD problems in quantum communication applications.

Investigation on instability mechanism and control of abandoned roadways in coal pillars recovery face: A case study

The abandoned roadways (ARs) in front of the longwall face catastrophic instability will seriously hamper mining progress, which is a complicated process related to the stress environment, the roadway section, and the mechanical properties of the surrounding rock. The cusp catastrophe theory is employed to establish a state identification model for the irregular coal pillar-roof system (CPRS) formed by the ARs and re-mining entries. To begin, the state discrimination equation (Δp) for the gradual CPRS is derived, and the critical value at which the system transitions into an unstable state under quasi-static conditions is determined. The results indicated that when 16.49 m ≤ L ≤ 22.63 m (L denotes the equivalent span of the intersection roof) and 0 < Re ≤ 2.61 m (Re denotes the width of the elastic zone within the triangular coal pillar), the triangular CPRS is inherently unstable. Similarly, for trapezoidal CPRS configurations where the length Lm (the span of the right-angled trapezoid roof in the propulsion direction) varies from 4.0 to 12.60 m, the system is unstable as well. Subsequently, the model was further enhanced by accounting for the impact of the Pc (advance stress increment load), where a critical criterion for the catastrophic instability of the CPRS was proposed, which represented the external energy required to transition the CPRS from an unstable state to catastrophic instability in different mining stages. After that, the stability degree of the irregular coal pillar was categorized, and a coupling zoning control technology was applied to CPR operations. Field monitoring results demonstrated the effectiveness of the zoning control technology, providing valuable guidance for similar mining practices.

Associations among attentional state, retrieval quality, and mnemonic discrimination

Memory specificity is shown when participants reject lures that are similar to studied objects. Lure rejections may reflect hippocampal pattern separation that encodes objects distinctively. However, lure features shared with studied objects may evoke pattern completion of varying quality. This was shown when self-reported attention during study promoted lure rejections and false alarms. We used an experimental and individual differences approach to examine the roles of attentive encoding and retrieval quality in lure classifications. An object-based mnemonic discrimination task included thought probes during study and subjective retrieval reports after recognition responses. On-task reports reflecting attentive encoding were associated with lure rejections and false alarms within-and between-subjects. Additionally, accurate lure and target classifications were more strongly associated with subjective recollection following on- than off-task reports. Collectively, these results suggest that attention during study was associated with recollection of criterial features that differentiated existing memories from perceptual inputs.

Similar assembly state discriminator for reinforcement learning-based robotic connector assembly

In practice, the process of robot assembly in an unstructured environment faces difficulties due to the presence of unpredictable environmental errors related to vision and pose. Therefore, to minimize the uncertain environmental errors during the robotic assembly process in an unstructured environment, several studies have considered a reinforcement learning (RL)-based approach. However, if assembly parts are changed, it becomes difficult to apply RL-based methods to assemble various parts because additional learning may be required. Especially in the case of connector assembly, fine-tuning is essential because the shape changes depending on the type of connector. In this study, we propose a similar assembly state discriminator that transforms the state information (force, velocity, and RGB image) of reinforcement learning into generalized features to respond various types of connector assembly tasks. This method processes the data to include essential features for assembly regardless of connector type. By learning the RL model with the processed data using this method, the RL model trained for a specific connector can be efficiently applied to other types of connectors without fine-tuning. The assembly success rate for the 7 types of connectors (Harting, HDMI, USB, power, air jack, banana plug and PCIE) using the proposed method was demonstrated to be over 96 %.

Estimation of Hamiltonian Parameters from Thermal States.

We upper bound and lower bound the optimal precision with which one can estimate an unknown Hamiltonian parameter via measurements of Gibbs thermal states with a known temperature. The bounds depend on the uncertainty in the Hamiltonian term that contains the parameter and on the term's degree of noncommutativity with the full Hamiltonian: higher uncertainty and commuting operators lead to better precision. We apply the bounds to show that there exist entangled thermal states such that the parameter can be estimated with an error that decreases faster than 1/sqrt[n], beating the standard quantum limit. This result governs Hamiltonians where an unknown scalar parameter (e.g., a component of a magnetic field) is coupled locally and identically to n qubit sensors. In the high-temperature regime, our bounds allow for pinpointing the optimal estimation error, up to a constant prefactor. Our bounds generalize to joint estimations of multiple parameters. In this setting, we recover the high-temperature sample scaling derived previously via techniques based on quantum state discrimination and coding theory. In an application, we show that noncommuting conserved quantities hinder the estimation of chemical potentials.

Measurement device-independent quantum state discrimination

Quantum state discrimination depicts the general progress of extracting classical information from quantum systems. We show that quantum state discrimination can be realized in a measurement device-independent scenario using tools of self-testing results. That is, the states can be discriminated credibly with the untrusted experiment devices by the correspondence between quantum correlations and states. In detail, we show that two states that are not conjugate with each other can be discriminated without additional trusted input states, and the discrimination of others require a kind of trusted inputs. Further we show the guessing probability analysis of this protocol for minimum error discrimination, which is acceptable based on numerical results.

Multimodal rapid identification of growth stages and discrimination of growth status for Morchella

We introduce a multimodal rapid identification and growth status discrimination method for morchella. Based on the unique biological characteristics and growth environmental requirements of morchella, the efficient and accurate identification of key growth stages of morchella is achieved through the integration of multimodal information acquisition technology. During the rapid identification process of the growth stage of Morchella, the Multi Stage Vision Enhanced Position Encoding Vision Transformer (MS-EP ViT) model is adopted. By introducing multi-stage input embedding, enhanced position encoding, and optimized Transformer Encoder layers, the performance of the model in identifying different growth stages of Morchella mushrooms is significantly improved. In the multimodal Morchella growth state discrimination method, text and image modalities are integrated, a Non downsampled Contourlet Transform Mask Region based Convolutional Neural Network (NSCT Mask R-CNN) model is designed, and a multimodal feature extraction strategy combining Non downsampled Contourlet Transform (NSCT) features with environmental features is explored. This strategy effectively achieves the goals of object detection and instance segmentation, and thus we have accurately evaluated the growth status of Morchella in the later stages of mulberry, young mushroom, and mature. The experimental results show that both models have achieved significant improvements in recognition accuracy and stability, and the rationality of hyperparameter settings has been verified through convergence and parameter sensitivity experiments. Overall, we provide a more accurate and efficient identification method for monitoring the growth of Morchella, which helps to better understand the growth of Morchella and provides scientific basis for optimizing its growth environment.

General measurements with limited resources and their application to quantum unambiguous state discrimination

In this report, we present a framework for implementing an arbitrary n-outcome generalized quantum measurement (POVM) on an m-qubit register as a sequence of two-outcome measurements requiring only single ancillary qubit. Our procedure offers a particular construction for the two-outcome partial measurements which can be composed into a full implementation of the measurement on any gate architecture. This implementation in general requires classical feedback; we present specific cases when this is not the case. We apply this framework on the unambiguous state discrimination and analyze possible strategies. In the simplest case, it gives the same construction as is known, if we opt for performing conclusiveness measurement first. However, it also offers possibility of performing measurement for one of the state outcomes first, leaving conclusiveness measurement for later. This shows flexibility of presented framework and opens possibilities for further optimization. We present discussion also on biased qubit case as well as general case of unambiguous quantum state discrimination in higher dimension.