Quantum Annealing for Semi-Supervised Learning

Semiclassical approach to finite temperature quantum annealing with trapped ions

Recently it has been demonstrated that an ensemble of trapped ions may serve as a quantum annealer for the number-partitioning problem [Nature Comm. DOI: 10.1038/ncomms11524]. This hard computational problem may be addressed employing a tunable spin glass architecture. Following the proposal of the trapped ions annealer, we study here its robustness against thermal effects, that is, we investigate the role played by thermal phonons. For the efficient description of the system, we use a semiclassical approach, and benchmark it against the exact quantum evolution. The aim is to understand better…

Validating a Two Qubit Non-Stoquastic Hamiltonian in Quantum Annealing

We propose a two qubit experiment for validating tunable antiferromagnetic $XX$ interactions in quantum annealing. Such interactions allow the time-dependent Hamiltonian to be non-stoquastic, and the instantaneous ground state can have negative amplitudes in the computational basis. Our construction relies on how the degeneracy of the Ising Hamiltonian's ground states is broken away from the end point of the anneal: above a certain value of the antiferromagnetic $XX$ interaction strength, the perturbative ground state at the end of the anneal changes from a symmetric to an antisymmetric state.…

Quantum Annealing in a Kinetically Constrained System

Classical and quantum annealing is discussed for a kinetically constrained chain of $N$ non-interacting asymmetric double wells, represented by Ising spins in a longitudinal field $h$. It is shown that in certain cases, where the kinetic constraints may arise from infinitely high but vanishingly narrow barriers appearing in the relaxation path of the system, quantum annealing exploiting the quantum-mechanical penetration of sufficiently narrow barriers may be far more efficient than its thermal counterpart. We have used a semiclassical picture of scattering dynamics to do our simulation for th…

使用连续变量量子退火的线性回归

Linear Regression Using Quantum Annealing with Continuous Variables

针对现有量子退火线性回归需增加量子比特数来提升精度的局限，提出一种利用玻色子连续变量直接进行优化的新型量子退火线性回归方法，在满足绝热条件下可确保精度且无需增加量子比特。

伊辛哈密顿量最小化：基于软自旋流形约简的增益计算与量子退火的比较

Ising Hamiltonian Minimization: Gain-Based Computing with Manifold Reduction of Soft-Spins vs Quantum Annealing

针对伊辛哈密顿量最小化问题，比较半经典软自旋模型的增益计算与量子退火的性能，提出流形约简方法解决半经典模型维度景观扩大的挑战，发现结合经典与量子退火技术或可提升优化器性能。

使用黑盒优化和量子退火过滤错误标注的训练实例

Filtering out mislabeled training instances using black-box optimization and quantum annealing

本研究提出结合代理模型黑盒优化与量子退火的方法，过滤错误标注实例，提升数据集质量，并在噪声多数位任务上验证了其高效性与优越性。

用于铁磁$p$-自旋模型基于分岔的量子退火的非随机催化剂

Nonstoquastic catalyst for bifurcation-based quantum annealing of ferromagnetic $p$-spin model

针对铁磁$p$-自旋模型量子退火因一阶相变难寻基态的问题，提出一种用于基于分岔的量子退火的非随机催化剂，可将一阶相变消除，使能隙从随系统尺寸指数衰减变为多项式衰减，有望提升退火性能。

采用并行退火的量子玻尔兹曼机用于医学图像分类

Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification

针对量子玻尔兹曼机训练QPU耗时高的问题，提出改进并行量子退火方法用于有监督训练，在MedMNIST医学图像数据集上取得与同规模CNNs相当的效果，训练轮数更少，速度提升近70%。

面向大规模图像分类的基于退火量子玻尔兹曼机的迁移学习方法研究

Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines

本文提出将量子退火与量子玻尔兹曼机引入量子迁移学习，用于大规模医学图像分类，并在COVID-CT-MD数据集上验证其相比经典迁移学习具有更高准确率和更少训练轮数。

Quantum Semi-Supervised Learning with Quantum Supremacy

Quantum machine learning promises to efficiently solve important problems. There are two persistent challenges in classical machine learning: the lack of labeled data, and the limit of computational power. We propose a novel framework that resolves both issues: quantum semi-supervised learning. Moreover, we provide a protocol in systematically designing quantum machine learning algorithms with quantum supremacy, which can be extended beyond quantum semi-supervised learning. In the meantime, we show that naive quantum matrix product estimation algorithm outperforms the best known classical matr…