Bulletin of the American Physical Society
5th Joint Meeting of the APS Division of Nuclear Physics and the Physical Society of Japan
Volume 63, Number 12
Tuesday–Saturday, October 23–27, 2018; Waikoloa, Hawaii
Session MA: Quantum Computing and Machine Learning for Nuclear Physics |
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Chair: Phiala Shanahan, MIT Room: Hilton Kona 4 |
Saturday, October 27, 2018 2:00PM - 2:45PM |
MA.00001: Quantum computing for nuclear physics: status and expectations Invited Speaker: Natalie Kloco From weak matrix elements to nuclear forces, lattice quantum chromodynamics is a technique for studying nature directly from quark and gluon degrees of freedom. These calculations, however, are limited by nature's physical characteristics of exponentially growing Hilbert spaces with particle number and sign/signal-to-noise problems. As a result, Minkovski-space dynamics and fermionic many-body structure calculations require exponentially large classical computing resources to provide results with necessary precision. This leaves many systems of interest to nuclear and particle physics (finite density systems, fragmentation functions, non-equilibrium systems etc.) intractable for known algorithms with current and foreseeable classical computational resources. Fortunately, there are good reasons to expect that it will be efficient to simulate locally-interacting quantum systems with quantum systems. By leveraging their natural capacity to represent wavefunctions in logarithmic numbers of qubits and directly manipulate amplitudes rather than probabilities, the use of quantum systems as a computational framework leads to constructions of basic quantum field theories with resource requirements that scale only polynomially with the precision and size of the system. In this talk, I will present an overview of recent efforts in, and the potential for, quantum computing to address important aspects of quantum field theories relevant to nuclear physics. |
Saturday, October 27, 2018 2:45PM - 3:30PM |
MA.00002: Real-time dynamics of lattice gauge theories with a few-qubit quantum computer Invited Speaker: Christine Muschik Gauge theories are fundamental to our understanding of interactions between the elementary constituents of matter as mediated by gauge bosons. However, computing the real-time dynamics in gauge theories is a notorious challenge for classical computational methods. In the spirit of Feynman's vision of a quantum simulator, this has recently stimulated theoretical effort to devise schemes for simulating such theories on engineered quantum-mechanical devices, with the difficulty that gauge invariance and the associated local conservation laws (Gauss laws) need to be implemented. Here we report on a digital quantum simulation of a lattice gauge theory, by realising 1+1-dimensional quantum electrodynamics (Schwinger model) on a few-qubit trapped-ion quantum computer. We are interested in the real-time evolution of the Schwinger mechanism, describing the instability of the bare vacuum due to quantum fluctuations, which manifests itself in the spontaneous creation of electron-positron pairs. To make efficient use of our quantum resources, we map the original problem to a spin model by eliminating the gauge fields in favour of exotic long-range interactions, which have a direct and efficient implementation on an ion trap architecture. We explore the Schwinger mechanism of particle-antiparticle generation by monitoring the mass production and the vacuum persistence amplitude. Moreover, we track the real-time evolution of entanglement in the system, which illustrates how particle creation and entanglement generation are directly related. Our work represents a first step towards quantum simulating high-energy theories with atomic physics experiments, the long-term vision being the extension to real-time quantum simulations of non-Abelian lattice gauge theories. |
Saturday, October 27, 2018 3:30PM - 4:15PM |
MA.00003: Machine learning and its application to lattice Monte Carlo simulations Invited Speaker: Akinori Tanaka Recent development of machine learning (ML), especially deep learning is remarkable. It has been applied to image recognition, image generation and so on with very good precision. From a mathematical point of view, images are just real matrices, so it would be a natural idea to replace this matrices with the configurations of the physical system created by numerical simulation and see what happens. In this talk, I will review basics on ML and recent attempts to improve Markov Chain Monte Carlo simulations including our work on reducing autocorrelation of Hamiltonian Monte Carlo (HMC) algorithm. |
Saturday, October 27, 2018 4:15PM - 5:00PM |
MA.00004: Tensor network approach to quantum field theories suffering from sign problem Invited Speaker: Shinji Takeda In this talk, I will review the recent progress of tensor network approaches whose striking feature is free of the sign problem. After a brief introduction of tensor network methods, I will mainly talk about the Lagranginan (path integral) approach and show its representative results. Finally, I will address outstanding problems and discuss future prospects. |
Saturday, October 27, 2018 5:00PM - 5:15PM |
MA.00005: ABSTRACT WITHDRAWN
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Saturday, October 27, 2018 5:15PM - 5:30PM |
MA.00006: ABSTRACT WITHDRAWN
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Saturday, October 27, 2018 5:30PM - 5:45PM |
MA.00007: ABSTRACT WITHDRAWN
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