Quantum computer makes first high-energy physics simulation

Nature ◽  
2016 ◽  
Author(s):  
Davide Castelvecchi
Keyword(s):  
High Energy Physics ◽  
Quantum Computer ◽  
High Energy ◽  
Physics Simulation ◽  
Energy Physics

scholarly journals NEW PHYSICS DISCOVERY POTENTIAL IN FUTURE EXPERIMENTS

1998 ◽  
Vol 13 (40) ◽  
pp. 3235-3249 ◽  
Author(s):  
S. I. BITYUKOV ◽  
N. V. KRASNIKOV
Keyword(s):  
Cross Sections ◽  
High Energy Physics ◽  
High Energy ◽  
New Physics ◽  
Physics Simulation ◽  
Discovery Potential ◽  
Proper Definition ◽  
Definition Of ◽  
Physics Experiments ◽  
Energy Physics

We propose a method to estimate the probability of new physics discovery in future high energy physics experiments. Physics simulation gives both the average numbers <Nb> of background and <Ns> of signal events. We find that the proper definition of the significance for <Nb>, <Ns> ≫ 1 is [Formula: see text] in comparison with often used significances: [Formula: see text] and [Formula: see text]. We propose a method of taking into account the systematical errors related to nonexact knowledge of background and signal cross-sections. An account of such systematics is essential in the search for supersymmetry at LHC. We also propose a method for estimating exclusion limits on new physics in future experiments.

scholarly journals Startup Qilimanjaro—towards a European full-stack coherent quantum annealer platform

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
V. Canivell ◽  
P. Forn-Díaz ◽  
A. Garcia-Saez ◽  
R. Sagastizabal
Keyword(s):  
High Energy Physics ◽  
Quantum Computer ◽  
High Energy ◽  
Quantum Spin ◽  
Research Institutions ◽  
Quantum Annealing ◽  
Funding Sources ◽  
The University ◽  
Energy Physics ◽  
Coherent Quantum

AbstractQilimanjaro Quantum Tech is the full-stack quantum spin-off of three research institutions, the Barcelona Supercomputing Center (BSC), the Institute for High Energy Physics (IFAE) and the University of Barcelona (UB). The company addresses the emerging quantum readiness demand from industry and academia, by providing both algorithmic development services as well as access to a new coherent quantum annealer platform, a special purpose quantum computer. Qilimanjaro is a member of the AVaQus European Commission’s H2020 FET-Open consortium for coherent quantum annealing development, which is led by one of Qilimanjaro’s founders at IFAE. A special feature of Qilimanjaro are its funding sources being exclusively international client contracts.

scholarly journals Unfolding quantum computer readout noise

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Benjamin Nachman ◽  
Miroslav Urbanek ◽  
Wibe A. de Jong ◽  
Christian W. Bauer
Keyword(s):  
Nuclear Physics ◽  
High Energy Physics ◽  
Quantum Computer ◽  
Quantum Algorithm ◽  
High Energy ◽  
Matrix Inversion ◽  
Quantum Computers ◽  
Statistical Fluctuations ◽  
Inversion Techniques ◽  
Energy Physics

Abstract In the current era of noisy intermediate-scale quantum computers, noisy qubits can result in biased results for early quantum algorithm applications. This is a significant challenge for interpreting results from quantum computer simulations for quantum chemistry, nuclear physics, high energy physics (HEP), and other emerging scientific applications. An important class of qubit errors are readout errors. The most basic method to correct readout errors is matrix inversion, using a response matrix built from simple operations to probe the rate of transitions from known initial quantum states to readout outcomes. One challenge with inverting matrices with large off-diagonal components is that the results are sensitive to statistical fluctuations. This challenge is familiar to HEP, where prior-independent regularized matrix inversion techniques (“unfolding”) have been developed for years to correct for acceptance and detector effects, when performing differential cross section measurements. We study one such method, known as iterative Bayesian unfolding, as a potential tool for correcting readout errors from universal gate-based quantum computers. This method is shown to avoid pathologies from commonly used matrix inversion and least squares methods.

Sign in / Sign up

Export Citation Format

Share Document