QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits

TL;DR

QuantumSEA introduces a dynamic, noise-adaptive method for sparse quantum circuit exploration, enabling efficient and robust quantum computations on NISQ devices by optimizing circuit topology and parameters during training.

Contribution

It proposes a novel in-time sparse exploration technique that adaptively optimizes quantum circuit topology and parameters under real noise conditions, improving efficiency and noise robustness.

Findings

Outperforms existing noise-aware and random circuit baselines.

Achieves state-of-the-art results with half the quantum gates.

Reduces circuit execution time by approximately 2x.

Abstract

Parameterized Quantum Circuits (PQC) have obtained increasing popularity thanks to their great potential for near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Achieving quantum advantages usually requires a large number of qubits and quantum circuits with enough capacity. However, limited coherence time and massive quantum noises severely constrain the size of quantum circuits that can be executed reliably on real machines. To address these two pain points, we propose QuantumSEA, an in-time sparse exploration for noise-adaptive quantum circuits, aiming to achieve two key objectives: (1) implicit circuits capacity during training - by dynamically exploring the circuit's sparse connectivity and sticking a fixed small number of quantum gates throughout the training which satisfies the coherence time and enjoy light noises, enabling feasible executions on real quantum devices; (2) noise robustness - by jointly optimizing the topology and parameters of quantum circuits under real device noise models. In each update step of sparsity, we leverage the moving average of historical gradients to grow necessary gates and utilize salience-based pruning to eliminate insignificant gates. Extensive experiments are conducted with 7 Quantum Machine Learning (QML) and Variational Quantum Eigensolver (VQE) benchmarks on 6 simulated or real quantum computers, where QuantumSEA consistently surpasses noise-aware search, human-designed, and randomly generated quantum circuit baselines by a clear performance margin. For example, even in the most challenging on-chip training regime, our method establishes state-of-the-art results with only half the number of quantum gates and ~2x time saving of circuit executions. Codes are available at https://github.com/VITA-Group/QuantumSEA.