Using Quantum Computing to Learn Physics

Leonardo Tomazeli Duarte, Yannick Deville

Motivated by the potential performance gains offered by quantum computing (QC), a recent research focus — known as quantum-assisted machine learning (QAML) — aims to develop machine learning algorithms tailored for execution on quantum computers while processing data that originate from a classical (i.e. non-quantum) phenomenon. QAML has been studied in various quantum computing (QC) paradigms. This paper, in particular, offers a brief overview of QAML within the context of adiabatic quantum computing (AQC). The major challenge in this case is to reformulate the machine learning task as a quadratic unconstrained binary optimization (QUBO) problem, which lies at the core of existing AQC physical implementations. We provide examples of machine learning techniques tailored to AQC, along with applications in remote sensing.

Jacob Denbeaux

Adrian German, Christina Snyder, John M. Phillips

J. E. García-Ramos, Álvaro Sáiz, J. M. Arias +2 more

Abstract In this paper, the application of quantum simulations and quantum machine learning is explored to solve problems in low‐energy nuclear physics. The use of quantum computing to address nuclear physics problems is still in its infancy, and particularly, the application of quantum machine learning (QML) in the realm of low‐energy nuclear physics is almost nonexistent. Three specific examples are presented where the utilization of quantum computing and QML provides, or can potentially provide in the future, a computational advantage: i) determining the phase/shape in schematic nuclear models, ii) calculating the ground state energy of a nuclear shell model‐type Hamiltonian, and iii) identifying particles or determining trajectories in nuclear physics experiments.

Yehui Tang, Junchi Yan, Edwin R. Hancock

Quantum computing (QC) is a new computational paradigm whose foundations relate to quantum physics. Notable progress has been made, driving the birth of a series of quantum-based algorithms that take advantage of quantum computational power. In this paper, we provide a targeted survey of the development of QC for graph-related tasks. We first elaborate the correlations between quantum mechanics and graph theory to show that quantum computers are able to generate useful solutions that can not be produced by classical systems efficiently for some problems related to graphs. For its practicability and wide-applicability, we give a brief review of typical graph learning techniques designed for various tasks. Inspired by these powerful methods, we note that advanced quantum algorithms have been proposed for characterizing the graph structures. We give a snapshot of quantum graph learning where expectations serve as a catalyst for subsequent research. We further discuss the challenges of using quantum algorithms in graph learning, and future directions towards more flexible and versatile quantum graph learning solvers.

Bertrand Georgeot