12 research outputs found
Quantum Fuel with Multilevel Atomic Coherence for Ultrahigh Specific Work in a Photonic Carnot Engine
We investigate scaling of work and efficiency of a photonic Carnot engine
with the number of quantum coherent resources. Specifically, we consider a
generalization of the "phaseonium fuel" for the photonic Carnot engine, which
was first introduced as a three-level atom with two lower states in a quantum
coherent superposition by [M. O. Scully, M. Suhail Zubairy, G. S. Agarwal, and
H. Walther, Science {\bf 299}, 862 (2003)], to the case of level atoms
with coherent lower levels. We take into account atomic relaxation and
dephasing as well as the cavity loss and derive a coarse grained master
equation to evaluate the work and efficiency, analytically. Analytical results
are verified by microscopic numerical examination of the thermalization
dynamics. We find that efficiency and work scale quadratically with the number
of quantum coherent levels. Quantum coherence boost to the specific energy
(work output per unit mass of the resource) is a profound fundamental
difference of quantum fuel from classical resources. We consider typical modern
resonator set ups and conclude that multilevel phaseonium fuel can be utilized
to overcome the decoherence in available systems. Preparation of the atomic
coherences and the associated cost of coherence are analyzed and the engine
operation within the bounds of the second law is verified. Our results bring
the photonic Carnot engines much closer to the capabilities of current
resonator technologies.Comment: 15 pages, 8 figure
Dissipative learning of a quantum classifier
The expectation that quantum computation might bring performance advantages
in machine learning algorithms motivates the work on the quantum versions of
artificial neural networks. In this study, we analyze the learning dynamics of
a quantum classifier model that works as an open quantum system which is an
alternative to the standard quantum circuit model. According to the obtained
results, the model can be successfully trained with a gradient descent (GD)
based algorithm. The fact that these optimization processes have been obtained
with continuous dynamics, shows promise for the development of a differentiable
activation function for the classifier model.Comment: 8 pages, 5 figure
Information-driven Nonlinear Quantum Neuron
The promising performance increase offered by quantum computing has led to
the idea of applying it to neural networks. Studies in this regard can be
divided into two main categories: simulating quantum neural networks with the
standard quantum circuit model, and implementing them based on hardware.
However, the ability to capture the non-linear behavior in neural networks
using a computation process that usually involves linear quantum mechanics
principles remains a major challenge in both categories. In this study, a
hardware-efficient quantum neural network operating as an open quantum system
is proposed, which presents non-linear behaviour. The model's compatibility
with learning processes is tested through the obtained analytical results. In
other words, we show that this dissipative model based on repeated
interactions, which allows for easy parametrization of input quantum
information, exhibits differentiable, non-linear activation functions.Comment: 11 pages, 6 figure
Stabilization and Dissipative Information Transfer of a Superconducting Kerr-Cat Qubit
Today, the competition to build a quantum computer continues, and the number
of qubits in hardware is increasing rapidly. However, the quantum noise that
comes with this process reduces the performance of algorithmic applications, so
alternative ways in quantum computer architecture and implementation of
algorithms are discussed on the one hand. One of these alternative ways is the
hybridization of the circuit-based quantum computing model with the
dissipative-based computing model. Here, the goal is to apply the part of the
algorithm that provides the quantum advantage with the quantum circuit model,
and the remaining part with the dissipative model, which is less affected by
noise. This scheme is of importance to quantum machine learning algorithms that
involve highly repetitive processes and are thus susceptible to noise. In this
study, we examine dissipative information transfer to a qubit model called
Cat-Qubit. This model is especially important for the dissipative-based version
of the binary quantum classification, which is the basic processing unit of
quantum machine learning algorithms. On the other hand, Cat-Qubit architecture,
which has the potential to easily implement activation-like functions in
artificial neural networks due to its rich physics, also offers an alternative
hardware opportunity for quantum artificial neural networks. Numerical
calculations exhibit successful transfer of quantum information from reservoir
qubits by a repeated-interactions-based dissipative scheme.Comment: 8 pages, 5 figure
Application of Power Flow problem to an open quantum neural hardware
Significant progress in the construction of physical hardware for quantum
computers has necessitated the development of new algorithms or protocols for
the application of real-world problems on quantum computers. One of these
problems is the power flow problem, which helps us understand the generation,
distribution, and consumption of electricity in a system. In this study, the
solution of a balanced 4-bus power system supported by the Newton-Raphson
method is investigated using a newly developed dissipative quantum neural
network hardware. This study presents the findings on how the proposed quantum
network can be applied to the relevant problem and how the solution performance
varies depending on the network parameters.Comment: 5 Figures, 6 Page