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arXiv:2406.08882

SA-DQAS: Self-attention Enhanced Differentiable Quantum Architecture Search

Yize Sun, Jiarui Liu, Zixin Wu, Volker Tresp, Yunpu Ma

Published Jun 13, 2024• 1 citation
arXivHTMLPDF

Abstract

We introduce SA-DQAS, a novel framework that enhances Differentiable Quantum Architecture Search (DQAS) by integrating a self-attention mechanism, enabling more effective quantum circuit design for variational quantum algorithms. Unlike DQAS, which treats placeholders independently, SA-DQAS captures inter-placeholder dependencies to improve architecture learning. We evaluate SA-DQAS across multiple tasks, including MaxCut, Job-Shop Scheduling Problem (JSSP), quantum chemistry simulation, and error mitigation. Experimental results show that SA-DQAS outperforms baselines and prior QAS methods in most cases, producing architectures with better stability, convergence, and noise resilience. To assess scalability and hardware readiness, we further test SA-DQAS-generated circuits on IBM's quantum device using the MaxCut problem. Circuits trained on small graphs are stacked to solve larger instances without retraining, demonstrating generalization to real hardware and larger problem sizes. Our results suggest that SA-DQAS not only improves circuit quality during training but also enables practical deployment on near-term quantum devices. This research represents the first successful integration of self-attention mechanism with DQAS.

DOI
10.48550/arxiv.2406.08882

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We introduce SA-DQAS, a novel framework that enhances Differentiable Quantum Architecture Search (DQAS) by integrating a self-attention mechanism, enabling more effective quantum circuit design for variational quantum algorithms. Unlike DQAS, which treats placeholders independently, SA-DQAS captures inter-placeholder dependencies to improve architecture learning. We evaluate SA-DQAS across multiple tasks, including MaxCut, Job-Shop Scheduling Problem (JSSP), quantum chemistry simulation, and error mitigation. Experimental results show that SA-DQAS outperforms baselines and prior QAS methods in most cases, producing architectures with better stability, convergence, and noise resilience. To assess scalability and hardware readiness, we further test SA-DQAS-generated circuits on IBM's quantum device using the MaxCut problem. Circuits trained on small graphs are stacked to solve larger instances without retraining, demonstrating generalization to real hardware and larger problem sizes. Our results suggest that SA-DQAS not only improves circuit quality during training but also enables practical deployment on near-term quantum devices. This research represents the first successful integration of self-attention mechanism with DQAS.
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The Job shop scheduling problem (JSSP) plays a pivotal role in industrial applications, such as signal processing (SP) and steel manufacturing, involving sequencing machines and jobs to maximize scheduling efficiency. Before, JSSP was solved using manually defined circuits by variational quantum algorithm (VQA). Finding a good circuit architecture is task-specific and time-consuming. Differentiable quantum architecture search (DQAS) is a gradient-based framework that can automatically design circuits. However, DQAS is only tested on quantum approximate optimization algorithm (QAOA) and error mitigation tasks. Whether DQAS applies to JSSP based on a more flexible algorithm, such as variational quantum eigensolver (VQE), is still open for optimization problems. In this work, we redefine the operation pool and extend DQAS to a framework JSSP-DQAS by evaluating circuits to generate circuits for JSSP automatically. The experiments conclude that JSSP-DQAS can automatically find noise-resilient circuit architectures that perform much better than manually designed circuits. It helps to improve the efficiency of solving JSSP.
Abstract Quantum architecture search (QAS) is the process of automating architecture engineering of quantum circuits. It has been desired to construct a powerful and general QAS platform which can significantly accelerate current efforts to identify quantum advantages of error-prone and depth-limited quantum circuits in the NISQ era. Hereby, we propose a general framework of differentiable quantum architecture search (DQAS), which enables automated designs of quantum circuits in an end-to-end differentiable fashion. We present several examples of circuit design problems to demonstrate the power of DQAS. For instance, unitary operations are decomposed into quantum gates, noisy circuits are re-designed to improve accuracy, and circuit layouts for quantum approximation optimization algorithm are automatically discovered and upgraded for combinatorial optimization problems. These results not only manifest the vast potential of DQAS being an essential tool for the NISQ application developments, but also present an interesting research topic from the theoretical perspective as it draws inspirations from the newly emerging interdisciplinary paradigms of differentiable programming, probabilistic programming, and quantum programming.
Differentiable quantum architecture search (DQAS) is a promising framework for the automated design of quantum circuits, particularly for variational quantum optimization algorithms. However, its practical deployment on quantum hardware is limited by the large number of circuit measurements required during optimization, making hardware execution costly. In this work, we show that for a broad class of combinatorial optimization problems and commonly used rotational gate parameterizations, the measurement cost of DQAS can be significantly reduced without changing the optimization objective. We derive the proposed measurement reduction scheme theoretically and validate it experimentally on 3-SAT and MaxCut benchmark problems. Our approach reduces the requested gradient measurement cost by about 39 to 41% while introducing only negligible classical post-processing overhead, lowering the practical cost of executing DQAS on quantum hardware.
Abstract Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS cannot only alleviate the influence of quantum noise and barren plateaus but also outperforms VQAs with pre-selected ansatze.