arXiv subjects

Yuxin Deng

Publications and source records attributed to Yuxin Deng.

At least 19 recordsLinked to original sources

arXiv2026

ReOC: Compilation of Recursive Quantum Oracles with Recursion-Aware Uncomputation

Quantum oracles are essential to many quantum algorithms, and their specifications may involve recursive control flow that depends on runtime quantum data. However, existing reversible compilation frameworks provide limited support for such quantum-controlled recursive structures. We present ReOC, a compilation framework that transforms high-level recursive oracle specifications with quantum control flow into reversible quantum programs. The framework comprises RQIMP, a high-level imperative source language for specifying recursive oracles, and a method of compiling programs in that language into RQC++, an existing high-level quantum recursive language with quantum control flow. In this way, we avoid the tedious and error-prone process of directly writing quantum oracles in RQC++. To manage static storage under dynamic quantum control, ReOC uses an indexed static-register discipline to isolate live variables across recursion layers, enabling safe register reuse while controlling quantum storage usage. Furthermore, to address the exponential time blow-up caused by naive uncomputation in recursive settings, ReOC employs a recursion-aware uncomputation strategy: temporary variables from recursive calls are cleaned using deferred strategies to control time overhead, while those from non-recursive statements are cleaned eagerly to reduce space usage. For linear recursion, this strategy yields overhead linear in recursion depth, parameterized by the per-layer register footprint and primitive-operation costs. Finally, we provide a mathematical proof of compilation correctness from RQIMP to RQC++, establishing semantic preservation and correct uncomputation of temporary quantum variables.

cs.PL↗

arXiv2026

Estimate Collapsibility of Causal Effects in Completed Partial DAGs via Strong d-Convex Hulls

This paper proposes a collapsible method for estimating causal effects that maintains the estimator's consistency before and after marginalization over some variables in completed partially directed acyclic graphs (CPDAGs). We first introduce the estimate collapsibility for CPDAGs and characterize the minimal collapsible sets as strong d-convex hulls. An efficient algorithm is devised to obtain such sets in DAGs and is generalized to CPDAGs. Then, we combine the graph reduction procedure with the IDA framework. Finally, experiments and empirical analysis show the effectiveness of the collapsibility for causal estimations in CPDAGs. Code is available at https://github.com/Jamyang-D/strongly-convex.

stat.ML↗

arXiv2026

Multiconfiguration Pair-Density Functional Theory Calculations of Low-lying States of Complex Chemical Systems with Quantum Computers

Accurately describing strong electron correlation in complex systems remains a prominent challenge in computational chemistry as near-term quantum algorithms treating total correlation often require prohibitively deep circuits. Here we present a hybrid strategy combining the Variational Quantum Eigensolver with Multiconfiguration Pair-Density Functional Theory to efficiently decouple correlation effects. This approach confines static correlation to a compact multireference quantum state while recovering dynamic correlation through a classical on-top density functional using reduced-density information. By enabling self-consistent orbital optimization, the method significantly reduces quantum resource overheads without sacrificing physical rigor. We demonstrate chemical accuracy on standard benchmarks by reproducing C$_2$ equilibrium bond lengths and benzene excitation energies with mean absolute errors of 0.006 {\AA} and 0.048 eV respectively. Most notably, for the strongly correlated Cr$_2$ dimer requiring a large complete active space (48e, 42o), the framework yields a bound potential-energy curve and recovers qualitative dissociation behavior despite realistic hardware noise. These results establish that separating correlation types provides a practical route to reliable predictions on near-term quantum hardware.

cs.CE↗

arXiv2025

Checking Continuous Stochastic Logic against Quantum Continuous-Time Markov Chains

Verifying quantum systems has attracted a lot of interest in the last decades.In this paper, we study the quantitative model-checking of quantum continuous-time Markov chains (quantum CTMCs). The branching-time properties of quantum CTMCs are specified by continuous stochastic logic (CSL), which is well-known for verifying real-time systems, including classical CTMCs. The core of checking the CSL formulas lies in tackling multiphase until formulas. We develop an algebraic method using proper projection, matrix exponentiation, and definite integration to symbolically calculate the probability measures of path formulas. Thus the decidability of CSL is established. To be efficient, numerical methods are incorporated to guarantee that the time complexity is polynomial in the encoding size of the input model and linear in the size of the input formula. A running example of Apollonian networks is further provided to demonstrate our method.

cs.LO↗

arXiv2025

Deep Learning Reforms Image Matching: A Survey and Outlook

Image matching, which establishes correspondences between two-view images to recover 3D structure and camera geometry, serves as a cornerstone in computer vision and underpins a wide range of applications, including visual localization, 3D reconstruction, and simultaneous localization and mapping (SLAM). Traditional pipelines composed of ``detector-descriptor, feature matcher, outlier filter, and geometric estimator'' falter in challenging scenarios. Recent deep-learning advances have significantly boosted both robustness and accuracy. This survey adopts a unique perspective by comprehensively reviewing how deep learning has incrementally transformed the classical image matching pipeline. Our taxonomy highly aligns with the traditional pipeline in two key aspects: i) the replacement of individual steps in the traditional pipeline with learnable alternatives, including learnable detector-descriptor, outlier filter, and geometric estimator; and ii) the merging of multiple steps into end-to-end learnable modules, encompassing middle-end sparse matcher, end-to-end semi-dense/dense matcher, and pose regressor. We first examine the design principles, advantages, and limitations of both aspects, and then benchmark representative methods on relative pose recovery, homography estimation, and visual localization tasks. Finally, we discuss open challenges and outline promising directions for future research. By systematically categorizing and evaluating deep learning-driven strategies, this survey offers a clear overview of the evolving image matching landscape and highlights key avenues for further innovation.

cs.CV↗

arXiv2025

VideoFusion: A Spatio-Temporal Collaborative Network for Multi-modal Video Fusion

Compared to images, videos better reflect real-world acquisition and possess valuable temporal cues. However, existing multi-sensor fusion research predominantly integrates complementary context from multiple images rather than videos due to the scarcity of large-scale multi-sensor video datasets, limiting research in video fusion and the inherent difficulty of jointly modeling spatial and temporal dependencies in a unified framework. To this end, we construct M3SVD, a benchmark dataset with $220$ temporally synchronized and spatially registered infrared-visible videos comprising $153,797$ frames, bridging the data gap. Secondly, we propose VideoFusion, a multi-modal video fusion model that exploits cross-modal complementarity and temporal dynamics to generate spatio-temporally coherent videos from multi-modal inputs. Specifically, 1) a differential reinforcement module is developed for cross-modal information interaction and enhancement, 2) a complete modality-guided fusion strategy is employed to adaptively integrate multi-modal features, and 3) a bi-temporal co-attention mechanism is devised to dynamically aggregate forward-backward temporal contexts to reinforce cross-frame feature representations. Experiments reveal that VideoFusion outperforms existing image-oriented fusion paradigms in sequences, effectively mitigating temporal inconsistency and interference. Project and M3SVD: https://github.com/Linfeng-Tang/VideoFusion.

cs.CV↗

arXiv2025

Cycle-Aware ZZ Crosstalk Mitigation on Quantum Hardware

ZZ crosstalk and decoherence hinder superconducting quantum computing. To enhance parallelism in mitigating ZZ crosstalk, we formulate the problem by integrating quantum cycles and two forms of qubit interference. We then propose CYCO, a CYcle-aware ZZ Crosstalk Optimization algorithm, which uses a timing-based greedy strategy to schedule gates through cycles within quantum circuits. A novel data structure called Time and Distance Dependency Graph is designed to model gate data dependencies and physical distances from quantum topologies for precise scheduling. Additionally, dynamically punching barriers reduces idle time in quantum circuits, further enhancing parallelism. Simulations show a reduction of up to 37.44% in quantum program cycle (14.19% on average) on various NISQ devices with 53 to 127 qubits. Real-device experiments on IBMQ-Brisbane demonstrate significant acceleration in quantum computing while maintaining fidelity.

quant-ph↗

arXiv2025

Local Reasoning about Probabilistic Behaviour for Classical-Quantum Programs

Verifying the functional correctness of programs with both classical and quantum constructs is a challenging task. The presence of probabilistic behaviour entailed by quantum measurements and unbounded while loops complicate the verification task greatly. We propose a new quantum Hoare logic for local reasoning about probabilistic behaviour by introducing distribution formulas to specify probabilistic properties. We show that the proof rules in the logic are sound with respect to a denotational semantics. To demonstrate the effectiveness of the logic, we formally verify the correctness of non-trivial quantum algorithms including the HHL and Shor's algorithms. Moreover, we embed our logic into the proof assistant Coq. The resulting logical framework, called CoqQLR, can facilitate semi-automated reasoning about classical--quantum programs.

cs.PL↗

arXiv2024

Encodability Criteria for Quantum Based Systems

Quantum based systems are a relatively new research area for that different modelling languages including process calculi are currently under development. Encodings are often used to compare process calculi. Quality criteria are used then to rule out trivial or meaningless encodings. In this new context of quantum based systems, it is necessary to analyse the applicability of these quality criteria and to potentially extend or adapt them. As a first step, we test the suitability of classical criteria for encodings between quantum based languages and discuss new criteria. Concretely, we present an encoding, from a language inspired by CQP into a language inspired by qCCS. We show that this encoding satisfies compositionality, name invariance (for channel and qubit names), operational correspondence, divergence reflection, success sensitiveness, and that it preserves the size of quantum registers. Then we show that there is no encoding from qCCS into CQP that is compositional, operationally corresponding, and success sensitive.

cs.LO↗

arXiv2024

Algorithmic Analysis of Termination Problems for Nondeterministic Quantum Programs

We consider the two categories of termination problems of quantum programs with nondeterminism: 1) Is an input of a program terminating with probability one under all schedulers? If not, how can a scheduler be synthesized to evidence the nontermination? 2) Are all inputs terminating with probability one under their respective schedulers? If yes, a further question asks whether there is a scheduler that forces all inputs to be terminating with probability one together with how to synthesize it; otherwise, how can an input be provided to refute the universal termination? For the effective verification of the first category, we over-approximate the reachable set of quantum program states by the reachable subspace, whose algebraic structure is a linear space. On the other hand, we study the set of divergent states from which the program terminates with probability zero under some scheduler. The divergent set has an explicit algebraic structure. Exploiting them, we address the decision problem by a necessary and sufficient condition, i.e. the disjointness of the reachable subspace and the divergent set. Furthermore, the scheduler synthesis is completed in exponential time. For the second category, we reduce the decision problem to the existence of invariant subspace, from which the program terminates with probability zero under all schedulers. The invariant subspace is characterized by linear equations. The states on that invariant subspace are evidence of the nontermination. Furthermore, the scheduler synthesis is completed by seeking a pattern of finite schedulers that forces all inputs to be terminating with positive probability. The repetition of that pattern yields the desired universal scheduler that forces all inputs to be terminating with probability one. All the problems in the second category are shown to be solved in polynomial time.

quant-ph↗

arXiv2023

A Sample-Driven Solving Procedure for the Repeated Reachability of Quantum CTMCs

Reachability analysis plays a central role in system design and verification. The reachability problem, denoted $\Diamond^J\,Φ$, asks whether the system will meet the property $Φ$ after some time in a given time interval $J$. Recently, it has been considered on a novel kind of real-time systems -- quantum continuous-time Markov chains (QCTMCs), and embedded into the model-checking algorithm. In this paper, we further study the repeated reachability problem in QCTMCs, denoted $\Box^I\,\Diamond^J\,Φ$, which concerns whether the system starting from each \emph{absolute} time in $I$ will meet the property $Φ$ after some coming \emph{relative} time in $J$. First of all, we reduce it to the real root isolation of a class of real-valued functions (exponential polynomials), whose solvability is conditional to Schanuel's conjecture being true. To speed up the procedure, we employ the strategy of sampling. The original problem is shown to be equivalent to the existence of a finite collection of satisfying samples. We then present a sample-driven procedure, which can effectively refine the sample space after each time of sampling, no matter whether the sample itself is successful or conflicting. The improvement on efficiency is validated by randomly generated instances. Hence the proposed method would be promising to attack the repeated reachability problems together with checking other $ω$-regular properties in a wide scope of real-time systems.

cs.LO↗

arXiv2023

Trusta: Reasoning about Assurance Cases with Formal Methods and Large Language Models

Assurance cases can be used to argue for the safety of products in safety engineering. In safety-critical areas, the construction of assurance cases is indispensable. Trustworthiness Derivation Trees (TDTs) enhance assurance cases by incorporating formal methods, rendering it possible for automatic reasoning about assurance cases. We present Trustworthiness Derivation Tree Analyzer (Trusta), a desktop application designed to automatically construct and verify TDTs. The tool has a built-in Prolog interpreter in its backend, and is supported by the constraint solvers Z3 and MONA. Therefore, it can solve constraints about logical formulas involving arithmetic, sets, Horn clauses etc. Trusta also utilizes large language models to make the creation and evaluation of assurance cases more convenient. It allows for interactive human examination and modification. We evaluated top language models like ChatGPT-3.5, ChatGPT-4, and PaLM 2 for generating assurance cases. Our tests showed a 50%-80% similarity between machine-generated and human-created cases. In addition, Trusta can extract formal constraints from text in natural languages, facilitating an easier interpretation and validation process. This extraction is subject to human review and correction, blending the best of automated efficiency with human insight. To our knowledge, this marks the first integration of large language models in automatic creating and reasoning about assurance cases, bringing a novel approach to a traditional challenge. Through several industrial case studies, Trusta has proven to quickly find some subtle issues that are typically missed in manual inspection, demonstrating its practical value in enhancing the assurance case development process.

cs.SE↗

arXiv2023

ResMatch: Residual Attention Learning for Local Feature Matching

Attention-based graph neural networks have made great progress in feature matching learning. However, insight of how attention mechanism works for feature matching is lacked in the literature. In this paper, we rethink cross- and self-attention from the viewpoint of traditional feature matching and filtering. In order to facilitate the learning of matching and filtering, we inject the similarity of descriptors and relative positions into cross- and self-attention score, respectively. In this way, the attention can focus on learning residual matching and filtering functions with reference to the basic functions of measuring visual and spatial correlation. Moreover, we mine intra- and inter-neighbors according to the similarity of descriptors and relative positions. Then sparse attention for each point can be performed only within its neighborhoods to acquire higher computation efficiency. Feature matching networks equipped with our full and sparse residual attention learning strategies are termed ResMatch and sResMatch respectively. Extensive experiments, including feature matching, pose estimation and visual localization, confirm the superiority of our networks.

cs.CV↗

arXiv2022

Too Slow to Be Useful? On Incorporating Humans in the Loop of Smart Speakers

Real-time crowd-powered systems, such as Chorus/Evorus, VizWiz, and Apparition, have shown how incorporating humans into automated systems could supplement where the automatic solutions fall short. However, one unspoken bottleneck of applying such architectures to more scenarios is the longer latency of including humans in the loop of automated systems. For the applications that have hard constraints in turnaround times, human-operated components' longer latency and large speed variation seem to be apparent deal breakers. This paper explicates and quantifies these limitations by using a human-powered text-based backend to hold conversations with users through a voice-only smart speaker. Smart speakers must respond to users' requests within seconds, so the workers behind the scenes only have a few seconds to compose answers. We measured the end-to-end system latency and the conversation quality with eight pairs of participants, showing the challenges and superiority of such systems.

cs.HC↗

arXiv2022

A Pattern Matching-Based Framework for Quantum Circuit Rewriting

The realization of quantum algorithms relies on specific quantum compilations according to the underlying quantum processors. However, there are various ways to physically implement qubits in different physical devices and manipulate those qubits. These differences lead to different communication methods and connection topologies, with each vendor implementing its own set of primitive gates. Therefore, quantum circuits have to be rewritten or transformed in order to be transplanted from one platform to another. We propose a pattern matching-based framework for rewriting quantum circuits, called QRewriting. It takes advantage of a new representation of quantum circuits using symbol sequences. Unlike the traditional way of using directed acyclic graphs, the new representation allows us to easily identify the patterns that appear non-consecutively but reducible. Then, we convert the problem of pattern matching into that of finding distinct subsequences and propose a polynomial-time dynamic programming-based pattern matching and replacement algorithm. We develop a rule library for basic optimizations and use it to rewrite the Arithmetic and Toffoli benchmarks from the $G_{IBM}$ gate set to the $G_{Sur}$ gate set. Compared with the existing tool PaF, QRewriting obtains an improvement of reducing depths (resp. gate counts) by 29\% (resp. 14\%).

quant-ph↗

arXiv2022

ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning

Deep-learning-based local feature extraction algorithms that combine detection and description have made significant progress in visible image matching. However, the end-to-end training of such frameworks is notoriously unstable due to the lack of strong supervision of detection and the inappropriate coupling between detection and description. The problem is magnified in cross-modal scenarios, in which most methods heavily rely on the pre-training. In this paper, we recouple independent constraints of detection and description of multimodal feature learning with a mutual weighting strategy, in which the detected probabilities of robust features are forced to peak and repeat, while features with high detection scores are emphasized during optimization. Different from previous works, those weights are detached from back propagation so that the detected probability of indistinct features would not be directly suppressed and the training would be more stable. Moreover, we propose the Super Detector, a detector that possesses a large receptive field and is equipped with learnable non-maximum suppression layers, to fulfill the harsh terms of detection. Finally, we build a benchmark that contains cross visible, infrared, near-infrared and synthetic aperture radar image pairs for evaluating the performance of features in feature matching and image registration tasks. Extensive experiments demonstrate that features trained with the recoulped detection and description, named ReDFeat, surpass previous state-of-the-arts in the benchmark, while the model can be readily trained from scratch.

cs.CV↗

arXiv2021

Symbolic Reasoning about Quantum Circuits in Coq

A quantum circuit is a computational unit that transforms an input quantum state to an output one. A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by it. However, when the number of qubits increases, the matrix dimension grows exponentially and the computation becomes intractable. In this paper, we propose a symbolic approach to reasoning about quantum circuits. It is based on a small set of laws involving some basic manipulations on vectors and matrices. This symbolic reasoning scales better than the explicit one and is well suited to be automated in Coq, as demonstrated with some typical examples.

cs.PL↗

arXiv2021

Quantum Circuit Transformation Based on Tabu Search

The goal of quantum circuit transformation is to map a logical circuit to a physical device by inserting additional gates as few as possible in an acceptable amount of time. We present an effective approach called TSA to construct the mapping. It consists of two key steps: one makes use of a combined subgraph isomorphism and completion to initialize some candidate mappings, the other dynamically modifies the mappings by using tabu search-based adjustment. Our experiments show that, compared with state-of-the-art methods GA, SABRE and FiDLS proposed in the literature, TSA can generate mappings with a smaller number of additional gates and it has a better scalability for large-scale circuits.

quant-ph↗