Charged particle tracking with quantum annealing optimization

Abstract

At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded to scale with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework for HL-LHC conditions. We develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML open dataset are presented, demonstrating the successful application of a quantum annealing algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the HL-LHC while leaving open the possibility of a quantum speedup for tracking.

Quantum Computing for Data Analysis in High-Energy Physics

“…obtained by breaking the direct correspondence with the classical setting and designing completely new tracking algorithms that inherently take advantage of the features of quantum processors. Some of the first attempts in this direction [20][21][22][23] were conceived within the quantum annealing model.…”

“…In particular, Ref. [20] and Ref. [21] have formulated track reconstruction as a quadratic unconstrained binary optimization (QUBO) problem, which can be naturally mapped to a quantum annealer.…”

“…In Ref. [20], the QUBO variables correspond to all possible edges between hits. The idea is that, in the optimal solution, the variables assigned with +1 connect hits that are left by the same particle.…”

Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research

“…The hope is to adapt or develop algorithms that can efficiently process HEP data. One example includes algorithms to construct physics objects amenable to analysis from the signals generated in a particle detector-i.e., the clustering of detector hits into so-called tracks for reconstructing a particle's trajectory [81][82][83][84][85][86][87][88] or tracks and calorimeter energy depositions into jets [89][90][91]. Furthermore, quantum-assisted algorithms have been explored in unsupervised learning settings to classify jets according to their origin (b-tagging) [92], generative tasks [93][94][95], and the selection of events or interactions along with background suppression [3,13,[96][97][98][99][100][101][102][103][104][105][106][107][108].…”

Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications

“…For example, there are many promising studies in experimental and theoretical high energy physics (HEP) for exploiting quantum computers. These studies include event classification [2][3][4][5][6], reconstructions of charged particle trajectories [7][8][9][10] and physics objects [11,12], unfolding measured distributions [13] as well as simulation of multiparticle emission processes [14,15]. A common feature of all of these algorithms is that only simplified versions can be run on existing hardware due to the limitations mentioned above.…”