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Shengjun Wu

Publications and source records attributed to Shengjun Wu.

At least 19 recordsLinked to original sources

The Inverse Eigenvalue Problem for Partial Transposes of Two-Qubit States

For a bipartite state $\rho$, information about the spectrum of its partial transpose $\rho^{\Gamma_B}$ can be inferred from measurements on multiple copies of $\rho$, without full state tomography. This raises a natural question: which eigenvalue lists can arise as $\operatorname{spec}(\rho^{\Gamma_B})$ for a density operator $\rho$? We completely solve this inverse eigenvalue problem for two qubits. Every nonnegative trace-one spectrum is realized as $\operatorname{spec}(\rho^{\Gamma_B})$ by some PPT state $\rho$, whereas an ordered candidate eigenvalue list $(x,y,z,-q)$, with $x\ge y\ge z\ge0$, $q>0$, and $x+y+z-q=1$, is realized by an NPT state iff $q\le y$ and $qy\le xz$. Sufficiency in the latter case is established by an explicit $X$ state whose quantum steering ellipsoid has center $c=(y-q)/(1-z)$ and normalized volume $V/V_{\max}(c)=qy/(xz)$, providing a geometric interpretation of the inequalities $q\le y$ and $qy\le xz$ as the allowed ellipsoid-center region and the fixed-center volume bound. Beyond this geometric picture, the two-qubit inverse theorem also yields exact negativity bounds from the two lowest nontrivial PT moments. Given fixed values of $p_2=Tr[(\rho^{\Gamma_B})^2]$ and $p_3=Tr[(\rho^{\Gamma_B})^3]$, we determine the exact minimum and maximum negativity over all two-qubit states subject to these moment constraints. When no PPT state is consistent with the pair $(p_2,p_3)$, the minimum is attained either at $x=y$ or $qy=xz$, while the maximum is attained either at $y=z$ or $q=y$. Finally, we show how the two-qubit inequalities persist as necessary constraints for the inverse eigenvalue problem in qubit--qudit systems.

quant-ph

Local Universality and Structural Certificates for Minimal Fixed-Depth Two-Qutrit Gate Decomposition

We study a dimension-saturating fixed-core ansatz in which four copies of a fixed, non-tunable two-qutrit core $K\in SU(9)$ are interleaved with five adjustable local layers from $L=SU(3)\otimes SU(3)$. Since $\dim SU(9)=80$ and $5\dim L=80$, this is the shortest fixed-core architecture not excluded by parameter counting. We formulate the smooth map $\Phi_K:L^5\to SU(9)$ and use its right-trivialized differential to give verifiable certificates for local universality. We construct an explicit Clifford-word core whose Pauli-label splitting makes the identity-point differential an exact isometry, and we classify all 2304 symplectic actions satisfying the same splitting criterion. We also prove a structural obstruction for an important symmetry class: every complex-symmetric core $K=K^{T}$, including every core generated by a time-independent real-symmetric Hamiltonian in the chosen computational basis, has identity-point differential rank at most 78; hence any full-rank certificate for such a core must occur away from that point. We then assess a hardware-motivated superconducting core generated by a noncommuting, temporally asymmetric drive. Direct calculation verifies $K_{\rm sc}\neq K_{\rm sc}^{\mathsf T}$, and the core achieves $F_{\rm avg}\ge 0.999$ for all 1000 Haar-random targets tested under the stated restart protocol. We also report favorable sampled Jacobian-rank, structured-target, and robustness diagnostics. These results establish local universality at the parameter-counting-minimal, dimension-saturating depth, with an exact Clifford certificate complemented by a hardware-motivated numerical case study. Throughout, we separate exact local certificates from numerical evidence for broader synthesis performance.

quant-ph

Wave-particle duality as an uncertainty relation for the average confidence width

We introduce the average confidence width $\Delta_a x=\int_0^1 \Delta_c x (\theta_x) d \theta_x$: the confidence width $\Delta_c x(\theta_x)$ -- the smallest position interval carrying a fraction $\theta_x$ of the probability -- averaged over all levels. It is the first moment of the decreasing rearrangement of $|\psi|^2$, an $L^1$ mean-absolute-deviation measure of localization, so the product $\Delta_{a} x\,\Delta_{a} p$ is dilation invariant and obeys $\Delta_{a} x\,\Delta_{a} p\ge c\,\hbar$. Reading $1/\Delta_{a} x$ as a particle character and $1/\Delta_{a} p$ as a wave character, this lower bound on combined spread is identically an upper bound on combined particle-and-wave character: uncertainty and wave-particle duality are two faces of one inequality. A mean-entropy argument with the Bialynicki-Birula-Mycielski relation gives the rigorous $c\ge\pi/e$, while the achievable constant $c^\ast$ is set by the ground state of the Fourier-invariant operator $|x|+|p|$, $c^\ast\le E_0^2\approx 1.217$. Hence $\pi/e\le c^\ast\le E_0^2<4/\pi$: the optimal state is sub-Gaussian, so the Gaussian -- optimal for the Heisenberg and entropic relations -- is not the duality optimum.

quant-ph

Confidence uncertainty: position and momentum can be jointly determined with a guaranteed probability

Standard-deviation and entropic formulations of uncertainty principle capture the spread of the probability distribution but say little about the probability itself contained in a small region. We introduce the confidence uncertainty $\Delta^{c}x(\theta_x)$ as the minimal Lebesgue measure of the support set in which the particle is found with probability at least $\theta_x$, and the companion interval confidence uncertainty $\Delta^{I}x(\theta_x)$ which restricts the support to a single interval. We prove two complementary uncertainty inequalities. (i) For $\theta_x+\theta_p\le 1$ both confidence uncertainties can be made arbitrarily small simultaneously, so that no nontrivial product bound holds; in particular, position and momentum can be jointly localised with probability at least~$50\%$. (ii) For $\theta_x+\theta_p>1$ a lower bound holds: combining Lenard's projection inequality with the Donoho--Stark operator-norm bound we obtain $\Delta^{c}x\,\Delta^{c}p\geq 2\pi\hbar\bigl(\sqrt{\theta_x\theta_p}-\sqrt{(1-\theta_x)(1-\theta_p)}\bigr)^{\!2}$, and for the interval version we obtain the sharp implicit Landau--Pollak bound $\Delta^{I}x\,\Delta^{I}p\geq 4\hbar\,\lambda_{0}^{-1}\!\bigl((\sqrt{\theta_x\theta_p}-\sqrt{(1-\theta_x)(1-\theta_p)})^{2}\bigr)$, where $\lambda_{0}(c)$ is the largest prolate-spheroidal eigenvalue. We support the analytical bounds with numerical evaluation of $\lambda_{0}(c)$, provide closed-form small-$c$ and large-$c$ asymptotics, compute the optimal Slepian-superposition states that saturate the interval bound, and compare the resulting product against the variance Heisenberg--Kennard, the Bia\l{}ynicki-Birula--Mycielski entropic, and the Donoho--Stark concentration bounds. The unified picture provides a complete phase diagram on $(\theta_x,\theta_p)\in[0,1]^{2}$.

quant-ph

A measure for genuine tripartite entanglement

We introduce a single real-valued functional $I(\vec{n}_1,\vec{n}_2)$, built from four three-qubit correlation expectation values, that turns the Greenberger--Horne--Zeilinger (GHZ) algebraic paradox into a \emph{quantitative} witness of genuine tripartite entanglement. We prove that for every three-qubit state $\rho$ and every pair of measurement directions $|I(\vec{n}_1,\vec{n}_2;\rho)|\le 2$, with the bound saturated if and only if $\vec{n}_1\perp \vec{n}_2$ and $\rho$ is locally unitarily equivalent to the GHZ state. We obtain a closed-form expression for $I(\hat{x},\hat{y})$ on the five-parameter Ac\'in canonical family of three-qubit pure states; it depends only on the product $\lambda_0\lambda_4$ and is maximised when $\lambda_0=\lambda_4=1/\sqrt{2}$. For the W state we show that $I(\hat{x},\hat{y})=0$ and that $\max_{\vec{n}_1, \vec{n}_2} | I_{W} |=35/27 \approx 1.296$, strictly below the GHZ value. Maximising the underlying correlation structure over \emph{independent} local orthonormal frames on the three parties yields a manifestly local-unitary (LU) invariant quantity $\mathcal{E}_{GHZ}(\rho)\in[0,1]$ that equals one if and only if $\rho$ is LU equivalent to the GHZ state, takes the value $35/54\approx 0.648$ on the W state, and is bounded by $1/2$ on all biseparable and fully separable states; it is therefore a device-independent indicator of GHZ-type genuine tripartite correlation. We carefully delimit which properties are proven and which (notably global convexity and the resulting genuine-multipartite-entanglement witness threshold) are established numerically and remain open analytically. We also outline a generalisation of $I$ to three-qudit systems built from the Heisenberg--Weyl operators, recovering the standard qubit construction when $d=2$.

quant-ph

How to quantify direct correlations between variables

A crucial question throughout statistics is whether an observed correlation between two variables is a direct correlation or only an indirect one mediated by a confounder. We organize the existing nonlinear measures of direct correlation into two families, each with a systematic construction: (i) removing the direct correlation from the joint distribution and quantifying the resulting distributional shift, and (ii) intervening on one variable via do-calculus and quantifying the response of the other. For every Kullback-Leibler-based measure in either family we propose a Jensen-Shannon-based regularized analogue; the regularized measures take values in $[0,1]$, satisfy the metric property, and are free of the singularities of the Kullback-Leibler divergence. We analyze the achievable upper bound of each regularized measure under the observed marginals, and derive the maximal value each measure can attain when only the alphabet sizes of the variables are fixed; the maxima admit closed forms built on a single binary-entropy function. The measures are compared on a decision-making model and on three public datasets (Titanic survival, UCI Adult income, and the 1973 Berkeley graduate admissions), with bootstrap confidence intervals for every reported value.

stat.ME

Weakly-Driven Quantum Walks for Memory-Constrained Pauli Channel Learning

Accurate characterization of quantum noise, exemplified by the Pauli channel, is a cornerstone for building fault-tolerant quantum computers. A recent protocol (PRX Quantum 6, 020323 (2025)) combining channel concatenation and quantum memory has achieved an exponential reduction in measurement complexity for Pauli channel estimation. This efficiency, however, hinges on using logarithmic quantum memory to suppress hypothesis test errors. In this work, we introduce a mechanism termed the ``weakly-driven quantum walk'' to mitigate the demand for high-quality quantum memory. By exploiting the distinct dynamical properties of quantum walks under biased versus unbiased driving, our algorithm lowers the quantum memory overhead to a constant order while preserving the exponential advantage in measurement complexity. By analogy with weak measurement, our introduced concept of ``weak driving'' preserves pointer coherence even when driven by classical probabilistic information, a principle that may inspire new approaches to similar quantum algorithm design and quantum sensing of weak signals in resource-constrained scenarios.

quant-ph

Experimental asymmetric relativistic zero-knowledge proofs with unconditional security

Zero-knowledge proofs (ZKPs) are widely applied in digital economies, such as cryptocurrencies and smart contracts, for establishing trust and privacy between untrusted parties. Classical ZKPs rely on computational assumptions and are vulnerable to quantum attacks. While a recent advance suggests quantum-sound symmetric relativistic ZKPs for the graph three-coloring problem without computational assumptions, the high round complexity, which leads to unachievable runtime and overall randomness cost, renders them impractical for real-life deployment. To overcome this, we develop an efficient asymmetric relativistic ZKP protocol using relativistic bit commitments, and prove its quantum soundness by relating it to the nonlocal Clauser-Horne-Shimony-Holt (CHSH) game. Our protocol achieves a linear relationship between the round complexity and the number of edges, and thus significantly improves practical feasibility. In addition, we implement a proof-of-principle experiment which completes all interactive rounds in about 0.22 seconds and requires an overall randomness cost of 430.81 MB. Our work illustrates the powerful potential of integrating special relativity with quantum theory in trustless cryptography, paving the way for robust applications against quantum attacks in distrustful Internet environments.

quant-ph

Microwave-activated two-qubit gates for fixed-coupling and fixed-frequency transmon qubits

All-microwave control of fixed-frequency superconducting quantum systems offers the potential to reduce control circuit complexity and increase system coherence. Nevertheless, due to the limited control flexibility in qubit parameters, one has to address several issues, such as quantum crosstalk and frequency crowding, for scaling up qubit architecture with non-tunable elements. This study proposes a microwave-activated two-qubit gate scheme for two fixed-frequency transmon qubits coupled via a fixed-frequency transmon coupler. The protocol relies on applying a microwave pulse exclusively to the coupler, enabling the implementation of a controlled-Z (CZ) gate. We show that the gate fidelity exceeding 0.999 can be achieved within 150 ns, excluding decoherence effects. Moreover, we also show that leakage from the computational subspace to non-computational states can also be effectively suppressed.

quant-ph

Quantum Sparse Coding and Decoding Based on Quantum Network

Sparse coding provides a versatile framework for efficiently capturing and representing crucial data (information) concisely, which plays an essential role in various computer science fields, including data compression, feature extraction, and general signal processing. In this study, we propose a symmetric quantum neural network for realizing sparse coding and decoding algorithms. Our networks consist of multi-layer, two-level unitary transformations that are naturally suited for optical circuits. Each gate is described by two real parameters, corresponding to reflectivity and phase shift. Specifically, the two networks can be efficiently trained together or separately using a quantum natural gradient descent algorithm, either simultaneously or independently. Utilizing the trained model, we achieve sparse coding and decoding of binary and grayscale images in classical problems, as well as that of complex quantum states in quantum problems separately. The results demonstrate an accuracy of 98.77\% for image reconstruction and a fidelity of 97.68\% for quantum state revivification. Our quantum sparse coding and decoding model offers improved generalization and robustness compared to the classical model, laying the groundwork for widespread practical applications in the emerging quantum era.

quant-ph

High-fidelity $\sqrt{i\text{SWAP}}$ gates using a fixed coupler driven by two microwave pulses

Attaining high-fidelity two-qubit gates represents a pivotal quantum operation for the realization of large-scale quantum computation and simulation. In this study, we propose a microwave-control protocol for the implementation of a two-qubit gate employing two transmon qubits coupled via a fixed-frequency transmon coupler. This protocol entails applying two microwave pulses exclusively to the coupler, thereby inducing interaction between the fixed-frequency transmon qubits. This interaction facilitates the realization of $\sqrt{i\text{SWAP}}$ gates. Additionally, we explore the implementation of the gate scheme in two distinct qubit architectures. Demonstrating with experimentally accessible parameters, we show that high-fidelity $\sqrt{i\text{SWAP}}$ gates can be achieved

quant-ph

Image Compression and Reconstruction Based on Quantum Network

Quantum network is an emerging type of network structure that leverages the principles of quantum mechanics to transmit and process information. Compared with classical data reconstruction algorithms, quantum networks make image reconstruction more efficient and accurate. They can also process more complex image information using fewer bits and faster parallel computing capabilities. Therefore, this paper will discuss image reconstruction methods based on our quantum network and explore their potential applications in image processing. We will introduce the basic structure of the quantum network, the process of image compression and reconstruction, and the specific parameter training method. Through this study, we can achieve a classical image reconstruction accuracy of 97.57\%. Our quantum network design will introduce novel ideas and methods for image reconstruction in the future.

quant-ph

Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognition

In this paper, we propose two hybrid quantum-inspired neural networks with adaptive residual and dense connections respectively for pattern recognition. We explain the frameworks of the symmetrical circuit models in the quantum-inspired layers in our hybrid models. We also illustrate the potential superiority of our hybrid models to prevent gradient explosion owing to the sine and cosine functions in the quantum-inspired layers. Groups of numerical experiments on generalization power showcase that our hybrid models are comparable to the pure classical models with different noisy datasets utilized. Furthermore, the comparison between our hybrid models and a state-of-the-art hybrid quantum-classical convolutional network demonstrates 3%-4% higher accuracy of our hybrid densely-connected model than the hybrid quantum-classical network. Additionally, compared with other two hybrid quantum-inspired residual networks, our hybrid models showcase a little higher accuracy on image datasets with asymmetrical noises. Simultaneously, in terms of groups of robustness experiments, the outcomes demonstrate that our two hybrid models outperform pure classical models notably in resistance to adversarial parameter attacks with various asymmetrical noises. They also indicate the slight superiority of our densely-connected hybrid model over the hybrid quantum-classical network to both symmetrical and asymmetrical attacks. Meanwhile, the accuracy of our two hybrid models is a little bit higher than that of the two hybrid quantum-inspired residual networks. In addition, an ablation study indicate that the recognition accuracy of our two hybrid models is 2%-3% higher than that of the traditional quantum-inspired neural network without residual or dense connection. Eventually, we discuss the application scenarios of our hybrid models by analyzing their computational complexity.

cs.LG

Entropic uncertainty relations and entanglement detection from quantum designs

Uncertainty relations and quantum entanglement are pivotal concepts in quantum theory. Beyond their fundamental significance in shaping our understanding of the quantum world, they also underpin crucial applications in quantum information theory. In this article, we investigate entropic uncertainty relations and entanglement detection with an emphasis on quantum measurements with design structures. On the one hand, we derive improved R\'enyi entropic uncertainty relations for design-structured measurements, exploiting the property that the sum of powered (e.g., squared) probabilities of obtaining different measurement outcomes is now invariant under unitary transformations of the measured system and can be easily computed. On the other hand, the above property essentially imposes a state-independent upper bound, which is achieved at all pure states, on one's ability to predict local outcomes when performing a set of design-structured measurements on quantum systems. Realizing this, we also obtain criteria for detecting multi-partite entanglement with design-structured measurements.

quant-ph

Entropic uncertainty relations for multiple measurements assigned with biased weights

The entropic way of formulating Heisenberg's uncertainty principle not only plays a fundamental role in applications of quantum information theory but also is essential for manifesting genuine nonclassical features of quantum systems. In this paper we investigate R\'{e}nyi entropic uncertainty relations (EURs) in the scenario where measurements on individual copies of a quantum system are selected with nonuniform probabilities. In contrast with EURs that characterize an observer's overall lack of information about outcomes with respect to a collection of measurements, we establish state-dependent lower bounds on the weighted sum of entropies over multiple measurements. Conventional EURs thus correspond to the special cases when all weights are equal, and in such cases, we show our results are generally stronger than previous ones. Moreover, taking the entropic steering criterion as an example, we numerically verify that our EURs could be advantageous in practical quantum tasks by optimizing the weights assigned to different measurements. Importantly, this optimization does not require quantum resources and is efficiently computable on classical computers.

quant-ph

Implementing arbitrary quantum operations via quantum walks on a cycle graph

The quantum circuit model is the most commonly used model for implementing quantum computers and quantum neural networks whose essential tasks are to realize certain unitary operations. The circuit model usually implements a desired unitary operation by a sequence of single-qubit and two-qubit unitary gates from a universal set. Although this certainly facilitates the experimentalists as they only need to prepare several different kinds of universal gates, the number of gates required to implement an arbitrary desired unitary operation is usually large. Hence the efficiency in terms of the circuit depth or running time is not guaranteed. Here we propose an alternative approach; we use a simple discrete-time quantum walk (DTQW) on a cycle graph to model an arbitrary unitary operation without the need to decompose it into a sequence of gates of smaller sizes. Our model is essentially a quantum neural network based on DTQW. Firstly, it is universal as we show that any unitary operation can be realized via an appropriate choice of coin operators. Secondly, our DTQW-based neural network can be updated efficiently via a learning algorithm, i.e., a modified stochastic gradient descent algorithm adapted to our network. By training this network, one can promisingly find approximations to arbitrary desired unitary operations. With an additional measurement on the output, the DTQW-based neural network can also implement general measurements described by positive-operator-valued measures (POVMs). We show its capacity in implementing arbitrary 2-outcome POVM measurements via numeric simulation. We further demonstrate that the network can be simplified and can overcome device noises during the training so that it becomes more friendly for laboratory implementations. Our work shows the capability of the DTQW-based neural network in quantum computation and its potential in laboratory implementations.

quant-ph

State Classification via a Random-Walk-Based Quantum Neural Network

In quantum information technology, crucial information is regularly encoded in different quantum states. To extract information, the identification of one state from the others is inevitable. However, if the states are non-orthogonal and unknown, this task will become awesomely tricky, especially when our resources are also limited. Here, we introduce the quantum stochastic neural network (QSNN), and show its capability to accomplish the binary discrimination of quantum states. After a handful of optimizing iterations, the QSNN achieves a success probability close to the theoretical optimum, no matter whether the states are pure or mixed. Other than binary discrimination, the QSNN is also applied to classify an unknown set of states into two types: entangled ones and separable ones. After training with four samples, it can classify a number of states with acceptable accuracy. Our results suggest that the QSNN has the great potential to process unknown quantum states in quantum information.

quant-ph

Implementing arbitrary quantum operations via quantum walks on a cycle graph

The quantum circuit model is the most commonly used model for implementing quantum computers and quantum neural networks whose essential tasks are to realize certain unitary operations. Here we propose an alternative approach; we use a simple discrete-time quantum walk (DTQW) on a cycle graph to model an arbitrary unitary operation $U(N)$ without the need to decompose it into a sequence of gates of smaller sizes. Our model is essentially a quantum neural network based on DTQW. Firstly, it is universal as we show that any unitary operation $U(N)$ can be realized via an appropriate choice of coin operators. Secondly, our DTQW-based neural network can be updated efficiently via a learning algorithm, i.e., a modified stochastic gradient descent algorithm adapted to our network. By training this network, one can promisingly find approximations to arbitrary desired unitary operations. With an additional measurement on the output, the DTQW-based neural network can also implement general measurements described by positive-operator-valued measures (POVMs). We show its capacity in implementing arbitrary 2-outcome POVM measurements via numeric simulation. We further demonstrate that the network can be simplified and can overcome device noises during the training so that it becomes more friendly for laboratory implementations. Our work shows the capability of the DTQW-based neural network in quantum computation and its potential in laboratory implementations.

quant-ph