A5101509600- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Stochastic Gradient Optimization Techniques
- Quantum Mechanics and Applications
- Advanced Bandit Algorithms Research
- Machine Learning and Algorithms
- Computability, Logic, AI Algorithms
- Parallel Computing and Optimization Techniques
- Complexity and Algorithms in Graphs
- Sparse and Compressive Sensing Techniques
- Quantum and electron transport phenomena
- Quantum-Dot Cellular Automata
- Adversarial Robustness in Machine Learning
- Advanced Graph Theory Research
- Neural Networks and Reservoir Computing
- Cloud Computing and Resource Management
- Quantum many-body systems
- Graph Labeling and Dimension Problems
- Markov Chains and Monte Carlo Methods
- Advanced Optimization Algorithms Research
- Statistical Mechanics and Entropy
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Laser-Matter Interactions and Applications
- Advanced Data Storage Technologies
Tianjin Medical University
2025
Peking University
2021-2025
Hubei University of Technology
2025
Beijing University of Technology
2023
Tsinghua University
2013-2023
Harvard University
2023
Massachusetts Institute of Technology
2020-2022
Center for Theoretical Biological Physics
2020-2022
Joint Center for Quantum Information and Computer Science
2017-2021
University of Maryland, College Park
2018-2021
Quantum state preparation is an important subroutine for quantum computing. We show that any $n$-qubit can be prepared with a $\Theta(n)$-depth circuit using only single- and two-qubit gates, although cost of exponential amount ancillary qubits. On the other hand, sparse states $d\geqslant2$ non-zero entries, we reduce depth to $\Theta(\log(nd))$ $O(nd\log d)$ The algorithm exponentially faster than best-known results number qubits nearly optimal increases polynomially system size. discuss...
We give two new quantum algorithms for solving semidefinite programs (SDPs) providing speed-ups. consider SDP instances with m constraint matrices, each of dimension n, rank at most r, and sparsity s. The first algorithm assumes an input model where one is given access to oracle the entries matrices unit cost. show that it has run time O~(s^2 (sqrt{m} epsilon^{-10} + sqrt{n} epsilon^{-12})), epsilon error solution. This gives optimal dependence in terms m, n quadratic improvement over...
The study of quantum generative models is well-motivated, not only because its importance in machine learning and chemistry but also the perspective implementation on near-term machines. Inspired by previous studies adversarial training classical models, we propose first design Wasserstein Generative Adversarial Networks (WGANs), which has been shown to improve robustness scalability even noisy hardware. Specifically, a definition semimetric between data, inherits few key theoretical merits...
We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang's breakthrough algorithm recommendation systems [STOC'19]. Motivated quantum linear algebra and singular value transformation (SVT) Gilyén et al. [STOC'19], we develop SVT that run in time independent input dimension, under suitable sampling assumptions. Our give compelling evidence corresponding QRAM data structure model, does not...
The algorithmic error of digital quantum simulations is usually explored in terms the spectral norm distance between actual and ideal evolution operators. In practice, this worst-case analysis may be unnecessarily pessimistic. To address this, we develop a theory average-case performance Hamiltonian simulation with random initial states. We relate to Frobenius multiplicative give upper bounds for product formula (PF) truncated Taylor series methods. As applications, estimate general lattice...
Accurate and robust localization is crucial for indoor mobile robots, where traditional vision-based systems often struggle with precision reliability. This paper presents a SLAM/UWB fusion algorithm designed to overcome these challenges. By building on the ORB-SLAM3 stereo-inertial framework integrating Ultra-Wideband (UWB) positioning data using an Extended Kalman Filter (EKF), method introduces time distance thresholds filter outliers improve accuracy. Experimental results show that this...
To address the decreased robustness and accuracy of traditional visual SLAM systems in dynamic environments, this paper proposes a method that integrates FastestDet object detection to enhance performance. First, objects are detected classified by FastestDet. A depth map-based clustering algorithm is then used segment static background from highly objects. Epipolar geometry constraints applied eliminate points low-dynamic objects, retaining only for pose estimation, thus improving accuracy....
<title>Abstract</title> Maximum cut (MaxCut) on graphs is a classic NP-hard problem. In quantum computing, Farhi, Gutmann, and Goldstone proposed the Quantum Approximate Optimization Algorithm (QAOA) for solving MaxCut Its guarantee fraction (the of edges in output over all edges) was mainly studied high-girth graphs, i.e., with only long cycles. On other hand, low-girth are ubiquitous theoretical computer science, including expander being outstanding examples wide applications theory...
We investigate quantum algorithms for classification, a fundamental problem in machine learning, with provable guarantees. Given $n$ $d$-dimensional data points, the state-of-the-art (and optimal) classical algorithm training classifiers constant margin runs $\tilde{O}(n+d)$ time. design sublinear same task running $\tilde{O}(\sqrt{n} +\sqrt{d})$ time, quadratic improvement both and $d$. Moreover, our use standard quantization of input generate output, suggesting minimal overheads when used...
Quantum simulation is a prominent application of quantum computers. While there extensive previous work on simulating finite-dimensional systems, less known about algorithms for real-space dynamics. We conduct systematic study such algorithms. In particular, we show that the dynamics <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math>-dimensional Schrödinger equation with...
Estimating statistical properties is fundamental in statistics and computer science. In this paper, we propose a unified quantum algorithm framework for estimating of discrete probability distributions, with Rényi entropies as specific examples. particular, given oracle that prepares an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -dimensional state Σ <sup xmlns:xlink="http://www.w3.org/1999/xlink"><i>n</i></sup> <sub...
Digital quantum simulation has broad applications in approximating unitary evolution of Hamiltonians. In practice, many tasks for systems focus on states the low-energy subspace instead entire Hilbert space. this paper, we systematically investigate complexity digital based product formulas subspace. We show that error depends effective norm Hamiltonian a variety algorithms and systems, allowing improvements over previous complexities full simulations even imperfect state preparations due to...
We give two quantum algorithms for solving semidefinite programs (SDPs) providing speed-ups. consider SDP instances with $m$ constraint matrices, each of dimension $n$, rank at most $r$, and sparsity $s$. The first algorithm assumes access to an oracle the matrices unit cost. show that it has run time $\tilde{O}(s^2(\sqrt{m}ε^{-10}+\sqrt{n}ε^{-12}))$, $ε$ error solution. This gives optimal dependence in terms $m, n$ quadratic improvement over previous when $m\approx n$. second a fully input...
Estimation of Shannon and Rényi entropies unknown discrete distributions is a fundamental problem in statistical property testing. In this paper, we give the first quantum algorithms for estimating α-Rényi (Shannon entropy being 1-Rényi entropy). particular, demonstrate quadratic speedup estimation generic all α ≥ 0 values, including tight bounds entropy, Hartley (α = 0), collision 2). We also provide upper min-entropy +∞) as well Kullback-Leibler divergence. complement our results with...
While recent work suggests that quantum computers can speed up the solution of semidefinite programs, little is known about complexity more general convex optimization. We present a algorithm optimize function over an $n$-dimensional body using $\tilde{O}(n)$ queries to oracles evaluate objective and determine membership in body. This represents quadratic improvement best-known classical algorithm. also study limitations on power for optimization, showing it requires $\tilde{\Omega}(\sqrt...
We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang’s breakthrough algorithm recommendation systems [STOC’19]. Motivated quantum linear algebra and singular value transformation (SVT) Gilyén et al. [STOC’19], we develop SVT that run in time independent input dimension, under suitable sampling assumptions. Our give compelling evidence corresponding QRAM data structure model, does not...
Classical algorithms are often not effective for solving nonconvex optimization problems where local minima separated by high barriers. In this paper, we explore possible quantum speedups leveraging the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>g</mml:mi><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>b</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi></mml:math> effect of tunneling. Specifically, introduce a algorithm termed tunneling walk (QTW) and apply it to approximately...
Quantum algorithms for simulating Hamiltonian dynamics have been extensively developed, but there has much less work on quantum the of open systems. We give first efficient Markovian generated by Lindbladians that are not necessarily local. introduce two approaches to sparse Lindbladians. First, we show how simulate act within small invariant subspaces using a algorithm implement Stinespring isometries. Second, develop method Lindblad operators concatenating sequence short-time evolutions....