With recent rapid progress on the development of quantum computing (QC), we explore the application of QC algorithm to high-energy physics (HEP). This new computing paradigm has potential for bringing a significant speed-up to several key tasks in HEP experiments as well as a deeper insight into "quantum nature" of particle physics.

First phase of this project aims to establish the foundation of QC application to HEP in quantum pattern recognition of charged particles produced in high-energy collisions. With more powerful and higher beam-intensity machine such as the high-luminosity LHC, the charged particle tracking is going to be a big challendge due to huge combinatorial background caused by tens of millisons of particles produced at the same time. Our goal is to develop a new quantum-based tracking algorithm to overcome the problem.

In this project, we are collaborating with the HEP group at Lawrence Berkeley National Laboratory (LBNL). The quantum pattern recognition algorithm was originally developed by the LBNL group under the HEP.QPR project.

Project

Quantum Pattern Recognition of Charged Particles

Charged particle tracking is a problem to identify series of detector hits that is consistent with the trajectories of charged particles produced in high-energy collisions. Detector hits are arranged into segments of 3 consecutive detector layer hits, called triplets. The quantum pattern recognition algorithm formulates particle tracking as a quadratic unconstrained binary optimization (QUBO) problem for the combination of triplets. Each triplet is assigned to a qubit and the coupling strength between qubits is defined based on the consistency with a single charged particle.

Alternative approach is being investigated based on quantum circuit algorithm using IBM Qiskit quantum computing framework. Converting the QUBO hamiltonian to an Ising hamiltonian and calculating the eigenvalue of the ground state for the hamiltonian using a quantum-classical hybrid algorithm with variational quantum eigensolver (VQE), the set of triplets belonging to charged particle trajectories is obtained. This study is still very prelimiary.

The quantum circuit algorithm is also being studied using Qulacs, a fast quantum circuit simulator for noisy intermediate scale quantum (NISQ) devices, developed in Kyoto University.

Public Results

Talks/Posters

• Quantum annealing algorithms for track pattern recognition, M. Saito et al, 24th International Conference on Computing in High Energy & Nucelar Physics, Adelaide, November 4-8, 2019. [proceedings]

• Quantum Computing and Possible Application to HEP, K. Terashi, Research Progress Meeting, Berkeley, October 31, 2019.

• Particle tracking with annealers and quantum machine learning with Qulacs, R. Sawada, Quantum Computing Mini-Workshop, Berkeley, October 30, 2019.

• 量子アニーリングを使った荷電粒子飛跡のパターン認識とその応用可能性 (Japanese), 寺師弘二, 他, 日本物理学会 2019年秋季大会, 山形大学, 2019年9月19日.

Members

Yutaro Iiyama, Tomoe Kishimoto, Ryu Sawada, Koji Terashi (in A-Z order)