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 $N+1$ level atoms with $N$ 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

Bir davranışsal iktisat paradoksunun kuantum çözümlemesi

Bu tez çalışması klasik olmayan olasılık kuramları kullanılarak paradoksal sonuç veren, belirsizlik altında insan karar vermesi ile ilgili kuramların çözümlenmesi ve yeni öngörüler geliştirilmesini hedeflemiştir. Daha ayrıntılı bir tanımlama ile bu çalışma subjektif beklenen fayda teorisini kurgulayan aksiyomlardan en az birinin ihlal edildiğini ima eden Ellsberg düşünce deneyini ele almaktadır. Çalışmamız son yıllarda önemini gittikçe arttıran paradoksal iktisat problemlerine kuantum olasılık yaklaşımı ile çözüm getirmeyi amaçlayan çalışmalardan birini ayrıntılı incelemiş ve sunduğu katkı ile belirsizlik altındaki karar vericilerin bilişsel durumları ile ilgili yorum yapılabilme olanağını ortaya koymuştur. Çalışmamız çeşitli bilişsel ilk durumların zamanla ilerlemesini incelemekte ve paradoksu çözen bilişsel durumları geçerli bilişsel durumlar olarak kabul eden bir metot benimsemektedir. Benimsenen metot ile elde edilen numerik bulgular tutarlı olup genel bir beklenen kuantum fayda teorisini oluşturma yolunda katkı sağlamaktadır.--------------------This thesis study aims to resolve the theories giving paradoxical results about human decision-making under uncertainty by using non-classical probability theories and to develop novel predictions. More particularly, this study considers the Ellsberg thought experiment implies that at least one of the main axioms of the subjective expected utility theory has been violated. Our study has been investigated one of the studies in detail aiming to resolve the paradoxical economic problems using the quantum probability approach that has been increasingly significant recent years and enabled the capability to develop predictions about the cognitive states of the decision-makers under uncertainty. This study also investigates the temporal evolution of several initial cognitive states and adopts a method that accepts the cognitive states resolves the paradox as the valid cognitive states. The numerical findings obtained by this method is self-consistent and contributes paving the way to construct a quantum version of the expected utility theory as a generalized utility theory