arXiv subjects

Yuqian Huo

Publications and source records attributed to Yuqian Huo.

6 recordsLinked to original sources

arXiv2026

WSBD: Freezing-Based Optimizer for Quantum Neural Networks

The training of Quantum Neural Networks (QNNs) is hindered by the high computational cost of gradient estimation and the barren plateau problem, where optimization landscapes become intractably flat. To address these challenges, we introduce Weighted Stochastic Block Descent (WSBD), a novel optimizer with a dynamic, parameter-wise freezing strategy. WSBD intelligently focuses computational resources by identifying and temporarily freezing less influential parameters based on a gradient-derived importance score. This approach significantly reduces the number of forward passes required per training step and helps navigate the optimization landscape more effectively. Unlike pruning or layer-wise freezing, WSBD maintains full expressive capacity while adapting throughout training. Our extensive evaluation shows that WSBD converges on average 63.9% faster than Adam for the popular ground-state-energy problem, an advantage that grows with QNN size. We provide a formal convergence proof for WSBD and show that parameter-wise freezing outperforms traditional layer-wise approaches in QNNs. Project page: https://github.com/Damrl-lab/WSBD-Stochastic-Freezing-Optimizer.

cs.LG↗

arXiv2025

Three Birds with One Stone: Improving Performance, Convergence, and System Throughput with Nest

Variational quantum algorithms (VQAs) have the potential to demonstrate quantum utility on near-term quantum computers. However, these algorithms often get executed on the highest-fidelity qubits and computers to achieve the best performance, causing low system throughput. Recent efforts have shown that VQAs can be run on low-fidelity qubits initially and high-fidelity qubits later on to still achieve good performance. We take this effort forward and show that carefully varying the qubit fidelity map of the VQA over its execution using our technique, Nest, does not just (1) improve performance (i.e., help achieve close to optimal results), but also (2) lead to faster convergence. We also use Nest to co-locate multiple VQAs concurrently on the same computer, thus (3) increasing the system throughput, and therefore, balancing and optimizing three conflicting metrics simultaneously.

quant-ph↗

arXiv2025

Anchor: Reducing Temporal and Spatial Output Performance Variability on Quantum Computers

Quantum computing, which has the power to accelerate many computing applications, is currently a technology under development. As a result, the existing noisy intermediate-scale quantum (NISQ) computers suffer from different hardware noise effects, which cause errors in the output of quantum programs. These errors cause a high degree of variability in the performance (i.e., output fidelity) of quantum programs, which varies from one computer to another and from one day to another. Consequently, users are unable to get consistent results even when running the same program multiple times. Current solutions, while focusing on reducing the errors faced by quantum programs, do not address the variability challenge. To address this challenge, we propose Anchor, a first-of-its-kind technique that leverages linear programming to reduce the performance variability by 73% on average over the state-of-the-art implementation focused on error reduction.

quant-ph↗

arXiv2025

Revisiting Noise-adaptive Transpilation in Quantum Computing: How Much Impact Does it Have?

Transpilation, particularly noise-aware optimization, is widely regarded as essential for maximizing the performance of quantum circuits on superconducting quantum computers. The common wisdom is that each circuit should be transpiled using up-to-date noise calibration data to optimize fidelity. In this work, we revisit the necessity of frequent noise-adaptive transpilation, conducting an in-depth empirical study across five IBM 127-qubit quantum computers and 16 diverse quantum algorithms. Our findings reveal novel and interesting insights: (1) noise-aware transpilation leads to a heavy concentration of workloads on a small subset of qubits, which increases output error variability; (2) using random mapping can mitigate this effect while maintaining comparable average fidelity; and (3) circuits compiled once with calibration data can be reliably reused across multiple calibration cycles and time periods without significant loss in fidelity. These results suggest that the classical overhead associated with daily, per-circuit noise-aware transpilation may not be justified. We propose lightweight alternatives that reduce this overhead without sacrificing fidelity -- offering a path to more efficient and scalable quantum workflows.

quant-ph↗

arXiv2024

Modeling and Simulating Rydberg Atom Quantum Computers for Hardware-Software Co-design with PachinQo

Quantum computing has the potential to accelerate various domains: scientific computation, machine learning, and optimization. Recently, Rydberg atom quantum computing has emerged as a promising quantum computing technology, especially with the demonstration of the zonal addressing architecture. However, this demonstration is only compatible with one type of quantum algorithm, and extending it to compile and execute general quantum algorithms is a challenge. To address it, we propose PachinQo, a framework to co-design the architecture and compilation for zonal addressing systems for any given quantum algorithm. PachinQo's evaluation demonstrates its ability to improve a quantum algorithm's estimated probability of success by 45% on average in error-prone quantum environments.

quant-ph↗

arXiv2024

ReCon: Reconfiguring Analog Rydberg Atom Quantum Computers for Quantum Generative Adversarial Networks

Quantum computing has shown theoretical promise of speedup in several machine learning tasks, including generative tasks using generative adversarial networks (GANs). While quantum computers have been implemented with different types of technologies, recently, analog Rydberg atom quantum computers have been demonstrated to have desirable properties such as reconfigurable qubit (quantum bit) positions and multi-qubit operations. To leverage the properties of this technology, we propose ReCon, the first work to implement quantum GANs on analog Rydberg atom quantum computers. Our evaluation using simulations and real-computer executions shows 33% better quality (measured using Frechet Inception Distance (FID)) in generated images than the state-of-the-art technique implemented on superconducting-qubit technology.

quant-ph↗