9,271 research outputs found
Realizing time crystals in discrete quantum few-body systems
The exotic phenomenon of time translation symmetry breaking under periodic
driving - the time crystal - has been shown to occur in many-body systems even
in clean setups where disorder is absent. In this work, we propose the
realization of time-crystals in few-body systems, both in the context of
trapped cold atoms with strong interactions and of a circuit of superconducting
qubits. We show how these two models can be treated in a fairly similar way by
adopting an effective spin chain description, to which we apply a simple
driving protocol. We focus on the response of the magnetization in the presence
of imperfect pulses and interactions, and show how the results can be
interpreted, in the cold atomic case, in the context of experiments with
trapped bosons and fermions. Furthermore, we provide a set of realistic
parameters for the implementation of the superconducting circuit.Comment: 6 pages, 4 figure
Multiqubit State Learning with Entangling Quantum Generative Adversarial Networks
The increasing success of classical generative adversarial networks (GANs)
has inspired several quantum versions of GANs. Fully quantum mechanical
applications of such quantum GANs have been limited to one- and two-qubit
systems. In this paper, we investigate the entangling quantum GAN (EQ-GAN) for
multiqubit learning. We show that the EQ-GAN can learn a circuit more
efficiently compared to a swap test. We also consider the EQ-GAN for learning
VQE-approximated eigenstates, and find that it generates excellent overlap
matrix elements when learning VQE states of small molecules. However, this does
not directly translate to a good estimate of the energy due to a lack of phase
estimation. Finally, we consider random state learning with the EQ-GAN for up
to six qubits, using different two-qubit gates, and show that it is capable of
learning completely random quantum states, something which could be useful in
quantum state loading.Comment: 6 pages, 4 figures, 1 table + Supporting materia
Drivers, barriers and development needs for LCA in the Nordic building sector - A survey among professionals
Time-varying Learning and Content Analytics via Sparse Factor Analysis
We propose SPARFA-Trace, a new machine learning-based framework for
time-varying learning and content analytics for education applications. We
develop a novel message passing-based, blind, approximate Kalman filter for
sparse factor analysis (SPARFA), that jointly (i) traces learner concept
knowledge over time, (ii) analyzes learner concept knowledge state transitions
(induced by interacting with learning resources, such as textbook sections,
lecture videos, etc, or the forgetting effect), and (iii) estimates the content
organization and intrinsic difficulty of the assessment questions. These
quantities are estimated solely from binary-valued (correct/incorrect) graded
learner response data and a summary of the specific actions each learner
performs (e.g., answering a question or studying a learning resource) at each
time instance. Experimental results on two online course datasets demonstrate
that SPARFA-Trace is capable of tracing each learner's concept knowledge
evolution over time, as well as analyzing the quality and content organization
of learning resources, the question-concept associations, and the question
intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable
or better performance in predicting unobserved learner responses than existing
collaborative filtering and knowledge tracing approaches for personalized
education
Statistical mechanics of a nonlinear discrete system
Statistical mechanics of the discrete nonlinear Schr\"odinger equation is
studied by means of analytical and numerical techniques. The lower bound of the
Hamiltonian permits the construction of standard Gibbsian equilibrium measures
for positive temperatures. Beyond the line of , we identify a phase
transition, through a discontinuity in the partition function. The phase
transition is demonstrated to manifest itself in the creation of breather-like
localized excitations. Interrelation between the statistical mechanics and the
nonlinear dynamics of the system is explored numerically in both regimes.Comment: 4 pages, 3 figure
High-pressure x-ray diffraction of icosahedral Zr-Al-Ni-Cu-Ag quasicrystals
The effect of pressure on the structural stability of icosahedral Zr-Al-Ni-Cu-Ag quasicrystals forming from a Zr65Al7.5Ni10Cu7.5Ag10 metallic glass with a supercooled liquid region of 44 K has been investigated by in situ high-pressure angle-dispersive x-ray powder diffraction at ambient temperature using synchrotron radiation. The icosahedral quasicrystal structure is retained up to the highest hydrostatic pressure used (approximately 28 GPa) and is reversible after decompression. The bulk modulus at zero pressure and its pressure derivative of the icosahedral Zr-Al-Ni-Cu-Ag quasicrystal are 99.10+/-1.26 GPa and 4.25+/-0.16, respectively. The compression behavior of different Bragg peaks is isotropic and the full width at half maximum of each peak remains almost unchanged during compression, indicating no anisotropic elasticity and no defects in the icosahedral Zr-Al-Ni-Cu-Ag quasicrystals induced by pressure
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