Unfolding measurement distributions via quantum annealing
High-energy physics is replete with hard computational problems and it is one of the areas where quantum computing could be used to speed up calculations. We present an implementation of likelihood-based regularized unfolding on a quantum computer. The inverse problem is recast in terms of quadratic unconstrained binary optimization (QUBO), which has the same form of the Ising hamiltonian and hence it is solvable on a programmable quantum annealer. We tested the method using a model that captures the essence of the problem, and compared the results with a baseline method commonly used in precision measurements at the Large Hadron Collider (LHC) at CERN. The unfolded distribution is in very good agreement with the original one. We also show how the method can be extended to include the effect of nuisance parameters representing sources of systematic uncertainties affecting the measurement.
KeywordsUnfolding Hadron-Hadron scattering (experiments)
This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited
- A. Di Meglio, M. Girone, A. Purcell and F. Rademakers, CERN openlab whitepaper on future ICT challenges in scientific research, zenodo (2017).Google Scholar
- G. Cowan, Statistical data analysis, Oxford University Press, Oxford U.K. (1998).Google Scholar
- GEANT4 collaboration, GEANT4: A Simulation toolkit, Nucl. Instrum. Meth.A 506 (2003) 250 [INSPIRE].
- Detector image slightly modified from source, http://www.particleadventure.org/modern_detect.html.
- G. Choudalakis, Fully Bayesian unfolding, arXiv:1201.4612.
- D. O’Malley and V. V. Vesselinov, ToQ.jl: A high-level programming language for D-Wave machines based on Julia, IEEE High Perform. Extreme Comput. Conf. (HPEC), 2016 (2016) 1.Google Scholar
- QNNCloud, Quantum neural network: Optical neural networks operating at the quantum limit, (2017).Google Scholar
- E. Crosson and A.W. Harrow, Simulated Quantum Annealing Can Be Exponentially Faster Than Classical Simulated Annealing, IEEE 57th Ann. Symp. Found. Comput. Sci.2016 (2016) 714.Google Scholar
- Simulated annealing sampler, https://docs.ocean.dwavesys.com/projects/neal/en/latest/reference/sampler.html.
- PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN, Geneva Switzerland (2011).Google Scholar
- N. Dattani, S. Szalay and N. Chancellor, Pegasus: The second connectivity graph for large-scale quantum annealing hardware, arXiv:1901.07636.