8 research outputs found
Impact of ionizing radiation on superconducting qubit coherence
The practical viability of any qubit technology stands on long coherence
times and high-fidelity operations, with the superconducting qubit modality
being a leading example. However, superconducting qubit coherence is impacted
by broken Cooper pairs, referred to as quasiparticles, with a density that is
empirically observed to be orders of magnitude greater than the value predicted
for thermal equilibrium by the Bardeen-Cooper-Schrieffer (BCS) theory of
superconductivity. Previous work has shown that infrared photons significantly
increase the quasiparticle density, yet even in the best isolated systems, it
still remains higher than expected, suggesting that another generation
mechanism exists. In this Letter, we provide evidence that ionizing radiation
from environmental radioactive materials and cosmic rays contributes to this
observed difference, leading to an elevated quasiparticle density that would
ultimately limit superconducting qubits of the type measured here to coherence
times in the millisecond regime. We further demonstrate that introducing
radiation shielding reduces the flux of ionizing radiation and positively
correlates with increased coherence time. Albeit a small effect for today's
qubits, reducing or otherwise mitigating the impact of ionizing radiation will
be critical for realizing fault-tolerant superconducting quantum computers.Comment: 16 pages, 12 figure
Hamiltonian neural networks for solving equations of motion
There has been a wave of interest in applying machine learning to study
dynamical systems. In particular, neural networks have been applied to solve
the equations of motion, and therefore, track the evolution of a system. In
contrast to other applications of neural networks and machine learning,
dynamical systems -- depending on their underlying symmetries - possess
invariants such as energy, momentum, and angular momentum. Traditional
numerical iteration methods usually violate these conservation laws,
propagating errors in time, and reducing the predictability of the method. We
present a Hamiltonian neural network that solves the differential equations
that govern dynamical systems. This unsupervised model is learning solutions
that satisfy identically, up to an arbitrarily small error, Hamilton's
equations and, therefore, conserve the Hamiltonian invariants. Once it is
optimized, the proposed architecture is considered a symplectic unit due to the
introduction of an efficient parametric form of solutions. In addition, by
sharing the network parameters and the choice of an appropriate activation
function drastically improve the predictability of the network. An error
analysis is derived and states that the numerical errors depend on the overall
network performance. The symplectic architecture is then employed to solve the
equations for the nonlinear oscillator and the chaotic Henon-Heiles dynamical
system. In both systems, the symplectic Euler integrator requires two orders
more evaluation points than the Hamiltonian network in order to achieve the
same order of the numerical error in the predicted phase space trajectories.Comment: This version (v4) is the same with version 2 (arXiv:2001.11107v2).
The version 3 (v3) was uploaded by acciden
SHAPER: can you hear the shape of a jet?
Abstract The identification of interesting substructures within jets is an important tool for searching for new physics and probing the Standard Model at colliders. Many of these substructure tools have previously been shown to take the form of optimal transport problems, in particular the Energy Mover’s Distance (EMD). In this work, we show that the EMD is in fact the natural structure for comparing collider events, which accounts for its recent success in understanding event and jet substructure. We then present a Shape Hunting Algorithm using Parameterized Energy Reconstruction (Shaper), which is a general framework for defining and computing shape-based observables. Shaper generalizes N-jettiness from point clusters to any extended, parametrizable shape. This is accomplished by efficiently minimizing the EMD between events and parameterized manifolds of energy flows representing idealized shapes, implemented using the dual-potential Sinkhorn approximation of the Wasserstein metric. We show how the geometric language of observables as manifolds can be used to define novel observables with built-in infrared-and-collinear safety. We demonstrate the efficacy of the Shaper framework by performing empirical jet substructure studies using several examples of new shape-based observables