arXiv subjects

Junyu Zhou

Publications and source records attributed to Junyu Zhou.

23 records · Page 2Linked to original sources

arXiv2025

Infant Cry Emotion Recognition Using Improved ECAPA-TDNN with Multiscale Feature Fusion and Attention Enhancement

Infant cry emotion recognition is crucial for parenting and medical applications. It faces many challenges, such as subtle emotional variations, noise interference, and limited data. The existing methods lack the ability to effectively integrate multi-scale features and temporal-frequency relationships. In this study, we propose a method for infant cry emotion recognition using an improved Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Network (ECAPA-TDNN) with both multi-scale feature fusion and attention enhancement. Experiments on a public dataset show that the proposed method achieves accuracy of 82.20%, number of parameters of 1.43 MB and FLOPs of 0.32 Giga. Moreover, our method has advantage over the baseline methods in terms of accuracy. The code is at https://github.com/kkpretend/IETMA.

eess.AS↗

arXiv2024

MarQSim: Reconciling Determinism and Randomness in Compiler Optimization for Quantum Simulation

Quantum simulation, fundamental in quantum algorithm design, extends far beyond its foundational roots, powering diverse quantum computing applications. However, optimizing the compilation of quantum Hamiltonian simulation poses significant challenges. Existing approaches fall short in reconciling deterministic and randomized compilation, lack appropriate intermediate representations, and struggle to guarantee correctness. Addressing these challenges, we present MarQSim, a novel compilation framework. MarQSim leverages a Markov chain-based approach, encapsulated in the Hamiltonian Term Transition Graph, adeptly reconciling deterministic and randomized compilation benefits. We rigorously prove its algorithmic efficiency and correctness criteria. Furthermore, we formulate a Min-Cost Flow model that can tune transition matrices to enforce correctness while accommodating various optimization objectives. Experimental results demonstrate MarQSim's superiority in generating more efficient quantum circuits for simulating various quantum Hamiltonians while maintaining precision.

quant-ph↗

arXiv2024

Fermihedral: On the Optimal Compilation for Fermion-to-Qubit Encoding

This paper introduces Fermihedral, a compiler framework focusing on discovering the optimal Fermion-to-qubit encoding for targeted Fermionic Hamiltonians. Fermion-to-qubit encoding is a crucial step in harnessing quantum computing for efficient simulation of Fermionic quantum systems. Utilizing Pauli algebra, Fermihedral redefines complex constraints and objectives of Fermion-to-qubit encoding into a Boolean Satisfiability problem which can then be solved with high-performance solvers. To accommodate larger-scale scenarios, this paper proposed two new strategies that yield approximate optimal solutions mitigating the overhead from the exponentially large number of clauses. Evaluation across diverse Fermionic systems highlights the superiority of Fermihedral, showcasing substantial reductions in implementation costs, gate counts, and circuit depth in the compiled circuits. Real-system experiments on IonQ's device affirm its effectiveness, notably enhancing simulation accuracy.

quant-ph↗

arXiv2024

Bosehedral: Compiler Optimization for Bosonic Quantum Computing

Bosonic quantum computing, based on the infinite-dimensional qumodes, has shown promise for various practical applications that are classically hard. However, the lack of compiler optimizations has hindered its full potential. This paper introduces Bosehedral, an efficient compiler optimization framework for (Gaussian) Boson sampling on Bosonic quantum hardware. Bosehedral overcomes the challenge of handling infinite-dimensional qumode gate matrices by performing all its program analysis and optimizations at a higher algorithmic level, using a compact unitary matrix representation. It optimizes qumode gate decomposition and logical-to-physical qumode mapping, and introduces a tunable probabilistic gate dropout method. Overall, Bosehedral significantly improves the performance by accurately approximating the original program with much fewer gates. Our evaluation shows that Bosehedral can largely reduce the program size but still maintain a high approximation fidelity, which can translate to significant end-to-end application performance improvement.

quant-ph↗

arXiv2016

Characterization and compression of dissipative-soliton-resonance pulses in fiber lasers

We report numerical and experimental studies of dissipative-soliton-resonance (DSR) in a fiber laser with a nonlinear optical loop mirror. The DSR pulse presents temporally a flat-top profile and a clamped peak power. Its spectrum has a rectangle profile with characteristic steep edges. It shows a unique behavior as pulse energy increases: The rectangle part of the spectrum is unchanged while the newly emerging spectrum sits on the center part and forms a peak. Experimental observations match well with the numerical results. Moreover, compression of the DSR pulses is both numerically and experimentally demonstrated for the first time. An experimentally obtained DSR pulse of 63 ps duration is compressed down to 760 fs, with low-intensity pedestals using a grating pair. Before being compressed to its narrowest width, the pulse firstly evolves into a cat-ear profile, and the corresponding autocorrelation trace shows a crown shape, which distinguishes itself from properties of other solitons formed in fiber lasers.

physics.optics↗