Parametrized quantum circuits (PQCs) represent a promising framework for using present-
day quantum hardware to solve diverse problems in materials science, quantum chemistry …

While recent breakthroughs have proven the ability of noisy intermediate-scale quantum
(NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the …

It is a central fact in quantum mechanics that non-orthogonal states cannot be distinguished
perfectly. In general, the optimal measurement for distinguishing such states is a collective …

Quantum computing opens exciting opportunities for kernel-based machine learning
methods, which have broad applications in data analysis. Recent works show that quantum …

We present the Quantum Monte Carlo Integration (QMCI) engine developed by Quantinuum.
It is a quantum computational tool for evaluating multi-dimensional integrals that arise in …

Matrix product state (MPS) offers a framework for encoding classical data into quantum
states, enabling the efficient utilization of quantum resources for data representation and …

Efficient methods to access the entanglement of a quantum many-body state, where the
complexity generally scales exponentially with the system size N, have long been a concern …

摘要核方法在机器学习中有广泛的应用. 量子计算与核方法结合, 可以有效解决经典核方法中当
特征空间变大时计算成本随之增加的问题. 研究表明, 基于核方法构建的最小化量子电路可以 …

The quantum state preparation of probability distributions is an important subroutine for
many quantum algorithms. When embedding $ D $-dimensional multivariate probability …

Encoding classical data in a quantum state is a key prerequisite of many quantum
algorithms. Recently matrix product state (MPS) methods emerged as the most promising …