arXiv subjects

Yuewen Hou

Publications and source records attributed to Yuewen Hou.

6 recordsLinked to original sources

arXiv2026

C-Phase-Aware Compilation for Efficient Fault-Tolerant Quantum Execution

Achieving practical quantum advantage on fault-tolerant quantum computers (FTQC) is fundamentally constrained by the substantial spatial and temporal overheads required to map logical operations onto physical hardware. Existing compilation approaches typically adopt coarse-grained, slice-based abstractions that overlook fine-grained microarchitectural effects, such as routing contention, leading to inefficient resource utilization and limited alignment between algorithm structure and hardware capabilities. We introduce Qomet, a microarchitecture-aware compiler that tightly couples algorithmic properties with lattice surgery (LS) execution. By exploiting C-Phase gate commutativity, Qomet translates sequential operations into simultaneous multi-target interactions, natively leveraging LS to eliminate false dependencies and expose instruction-level parallelism. To support this, Qomet employs an adaptive, event-driven scheduler that captures precise spatial and routing constraints to overlap instructions temporally. By minimizing grid idling and routing contention, Qomet achieves a geometric-mean execution speedup of 4.29$\times$ and a maximum speedup of 59.7$\times$ across realistic workloads.

quant-ph↗

arXiv2026

Scalable Clifford-Based Classical Initialization for the Quantum Approximate Optimization Algorithm

Variational Quantum Algorithms (VQAs), such as the Quantum Approximate Optimization Algorithm (QAOA), offer a promising route to tackling combinatorial optimization problems on near and intermediate-term quantum devices. However, their performance critically depends on the choice of initial parameters, and the limited expressiveness of the QAOA ansatz makes identifying effective initializations both difficult and unscalable. To address this, we propose a framework, Scalable Parameter Initialization for QAOA (SPIQ), that employs a relaxed QAOA ansatz to enable classical search over a set of Clifford-preparable quantum states that yield high-quality solutions. These states serve as superior QAOA initializations, driving rapid convergence while significantly reducing the quantum circuit evaluations needed to reach high-quality solutions and consequently lowering quantum-device cost. We present a scalable, application-agnostic initialization framework that achieves an absolute accuracy improvement of up to 80% over state-of-the-art initialization and reduces initial-state diversity by up to 10,000x across QUBO, PUBO, and PCBO problems spanning tens to hundreds of qubits. We further benchmark its performance on a wide range of problem formulations and instances derived from real-world datasets, demonstrating consistent and scalable improvements. Furthermore, we introduce two complementary strategies for selecting high-quality Clifford points identified by our search procedure and using them to seed multi-start optimization, thereby enhancing exploration and improving solution quality.

quant-ph↗

arXiv2025

TreeVQA: A Tree-Structured Execution Framework for Shot Reduction in Variational Quantum Algorithms

Variational Quantum Algorithms (VQAs) are promising for near- and intermediate-term quantum computing, but their execution cost is substantial. Each task requires many iterations and numerous circuits per iteration, and real-world applications often involve multiple tasks, scaling with the precision needed to explore the application's energy landscape. This demands an enormous number of execution shots, making practical use prohibitively expensive. We observe that VQA costs can be significantly reduced by exploiting execution similarities across an application's tasks. Based on this insight, we propose TreeVQA, a tree-based execution framework that begins by executing tasks jointly and progressively branches only as their quantum executions diverge. Implemented as a VQA wrapper, TreeVQA integrates with typical VQA applications. Evaluations on scientific and combinatorial benchmarks show shot count reductions of $25.9\times$ on average and over $100\times$ for large-scale problems at the same target accuracy. The benefits grow further with increasing problem size and precision requirements.

quant-ph↗

arXiv2024

Asymptotic limit of fully coupled multi-scale non-linear stochastic system: the non-autonomous approximation method

In this paper, we develop a novel argument, the non-autonomous approximation method, to seek the asymptotic limits of the fully coupled multi-scale McKean-Vlasov stochastic systems with irregular coefficients, which, as summarized in [3,Section 7], remains an open problem in the field. We provide an explicit characterization for the averaged limit of the non-linear stochastic system, where both the choice of the frozen equation and the definition of the averaged coefficients are more or less unexpected since new integral terms with respect to the measure variable appear. More importantly, in contrast with the classical theory of multi-scale systems which focuses on the averaged limit of the slow process, we propose a new perspective that the asymptotic behavior of the entire system is actually governed by the limit of the fast motion. By studying the long-time estimates of the solution of the Kolmogorov equation in Wasserstein space, we identify the limiting distribution of the fast motion of the non-linear system, which, to the best of our knowledge, is new even for the classical multi-scale It\^o SDEs. Furthermore, rates of convergence are also obtained, which are rather sharp and depend only on the regularity of the coefficients with respect to the slow variable. The innovation of our argument is to transform the non-linear system into a sequence of linear but non-autonomous systems, which is rather simple insofar as it avoids to involve the mean-field type PDEs associated with non-linear stochastic system, and at the same time, it turns out to be quite effective as it enables us to show that the strong convergence in the averaging principle of the non-linear stochastic system follows directly from the weak convergence, which significantly simplified the proof.

math.PR↗

arXiv2024

Empowering the Quantum Cloud User with QRIO

Quantum computing is moving swiftly from theoretical to practical applications, making it crucial to establish a significant quantum advantage. Despite substantial investments, access to quantum devices is still limited, with users facing issues like long wait times and inefficient resource management. Unlike the mature cloud solutions for classical computing, quantum computing lacks effective infrastructure for resource optimization. We propose a Quantum Resource Infrastructure Orchestrator (QRIO), a state-of-the-art cloud resource manager built on Kubernetes that is tailored to quantum computing. QRIO seeks to democratize access to quantum devices by providing customizable, user-friendly, open-source resource management. QRIO's design aims to ensure equitable access, optimize resource utilization, and support diverse applications, thereby speeding up innovation and making quantum computing more accessible and efficient to a broader user base. In this paper, we discuss QRIO's various features and evaluate its capability in several representative usecases.

cs.DC↗

arXiv2022

SIR Epidemics on Evolving Erd\H{o}s-R\'enyi Graphs

In the standard SIR model, infected vertices infect their neighbors at rate $\lambda$ independently across each edge. They also recover at rate $\gamma$. In this work we consider the SIR-$\omega$ model where the graph structure itself co-evolves with the SIR dynamics. Specifically, $S-I$ connections are broken at rate $\omega$. Then, with probability $\alpha$, $S$ rewires this edge to another uniformly chosen vertex; and with probability $1-\alpha$, this edge is simply dropped. When $\alpha=1$ the SIR-$\omega$ model becomes the evoSIR model. Jiang et al. proved in \cite{DOMath} that the probability of an outbreak in the evoSIR model converges to 0 as $\lambda$ approaches the critical infection rate $\lambda_c$. On the other hand, numerical experiments in \cite{DOMath} revealed that, as $\lambda \to \lambda_c$, (conditionally on an outbreak) the fraction of infected vertices may not converge to 0, which is referred to as a discontinuous phase transition. In \cite{BB} Ball and Britton give two (non-matching) conditions for continuous and discontinuous phase transitions for the fraction of infected vertices in the SIR-$\omega$ model. In this work, we obtain a necessary and sufficient condition for the emergence of a discontinuous phase transition of the final epidemic size of the SIR-$\omega$ model on \ER\, graphs, thus closing the gap between these two conditions.

math.PR↗