Collective performance of a finite-time quantum Otto cycle

We study the finite-time effects in a quantum Otto cycle where a collective spin system is used as the working fluid. Starting from a simple one-qubit system we analyze the transition to the limit cycle in the case of a finite-time thermalization. If the system consists of a large sample of independent qubits interacting coherently with the heat bath, the superradiant equilibration is observed. We show that this phenomenon can boost the power of the engine. Mutual interaction of qubits in the working fluid is modeled by the Lipkin-Meshkov-Glick Hamiltonian. We demonstrate that in this case the quantum phase transitions for the ground and excited states may have a strong negative effect on the performance of the machine. Reversely, by analyzing the work output we can distinguish between the operational regimes with and without a phase transition.Comment: 13 pages, 11 figure

Excited-state quantum phase transitions

We review the effects of excited-state quantum phase transitions (ESQPTs) in interacting many-body systems with finite numbers of collective degrees of freedom. We classify typical ESQPT signatures in the spectra of energy eigenstates with respect to the underlying classical dynamics and outline a variety of quantum systems in which they occur. We describe thermodynamic and dynamic consequences of ESQPTs, like those in microcanonical thermodynamics, quantum quench dynamics, and in the response to nearly adiabatic or periodic driving. We hint at some generalizations of the ESQPT concept in periodic lattices and in resonant tunneling systems.Comment: 78 pages, 21 figures, review articl

A differentiable programming method for quantum control

Optimal control is highly desirable in many current quantum systems, especially to realize tasks in quantum information processing. We introduce a method based on differentiable programming to leverage explicit knowledge of the differential equations governing the dynamics of the system. In particular, a control agent is represented as a neural network that maps the state of the system at a given time to a control pulse. The parameters of this agent are optimized via gradient information obtained by direct differentiation through both the neural network \emph{and} the differential equation of the system. This fully differentiable reinforcement learning approach ultimately yields time-dependent control parameters optimizing a desired figure of merit. We demonstrate the method's viability and robustness to noise in eigenstate preparation tasks for three systems: a~single qubit, a~chain of qubits, and a quantum parametric oscillator.Comment: 21 pages, 9 figure

Complex density of continuum states in resonant quantum tunneling

We introduce a complex-extended continuum level density and apply it to one-dimensional scattering problems involving tunneling through finite-range potentials. We show that the real part of the density is proportional to a real "time shift" of the transmitted particle, while the imaginary part reflects the imaginary time of an instanton-like tunneling trajectory. We confirm these assumptions for several potentials using the complex scaling method. In particular, we show that stationary points of the potentials give rise to specific singularities of both real and imaginary densities which represent close analogues of excited-state quantum phase transitions in bound systems.Comment: 6 pages, 3 figure

Control of stochastic quantum dynamics by differentiable programming

Control of the stochastic dynamics of a quantum system is indispensable in fields such as quantum information processing and metrology. However, there is no general ready-made approach to the design of efficient control strategies. Here, we propose a framework for the automated design of control schemes based on differentiable programming. We apply this approach to the state preparation and stabilization of a qubit subjected to homodyne detection. To this end, we formulate the control task as an optimization problem where the loss function quantifies the distance from the target state, and we employ neural networks (NNs) as controllers. The system's time evolution is governed by a stochastic differential equation (SDE). To implement efficient training, we backpropagate the gradient information from the loss function through the SDE solver using adjoint sensitivity methods. As a first example, we feed the quantum state to the controller and focus on different methods of obtaining gradients. As a second example, we directly feed the homodyne detection signal to the controller. The instantaneous value of the homodyne current contains only very limited information on the actual state of the system, masked by unavoidable photon-number fluctuations. Despite the resulting poor signal-to-noise ratio, we can train our controller to prepare and stabilize the qubit to a target state with a mean fidelity of around 85%. We also compare the solutions found by the NN to a hand-crafted control strategy

Experimental in-vitro bone cements disintegration with ultrasonic pulsating water jet for revision arthroplasty

The paper deals with the study of using the selective property of ultrasonic pulsating water jet for the disintegration of the interface created by bone cement between cemented femoral stem and trabecular bone tissue as a potential technique for revision arthroplasty. Six types of commercial bone cements based on Polymethyl Methacrylate were used for investigation. The cements were mixed using the DePuy - SmartMix (R) CTS / vacuum mixing bowl. Mechanical properties of hardened bone cements were determined by nanoindentation. The bone cement samples were disintegrated using the pulsating water jet technology. The water pressure varied between 8 divided by 20 MPa. A circular nozzle with an orifice diameter of 0,7 mm was used for water jetting. The stand-off distance from the target material was 2 mm and the traverse speed 1 mm/s. The volume of material removal and depth of created traces were measured by MicroProf FRT optical profilometer. The results positively support an assumption that pulsating water jet has a potential to be a suitable technique for the quick and safe disintegration of bone cement during revision arthroplasty

Photon strength functions in 196Pt from two-step gamma cascade measurement

Photon strength functions (PSFs), using the statistical approach to a nuclear decay, seem to be a suitable tool for the description of the electromagnetic transitions in a nucleus. Since the early 50's, when PSFs were first introduced, a plenty of theoretical models were proposed however their validity is still a question. In this work the data from the measurement of so called Two step cascades in the nucleus 196Pt using the reaction 195Pt(n,gamma)196Pt are processed. The experiment was performed at the Nuclear Physics Institute ASCR in Řež near Prague. We assume that these experimental spectra contain important information on PSFs. Comparison of the processed data with a few Monte Carlo simulations is also a part of this thesis

Photon strength functions in 196Pt from two-step gamma cascade measurement

Photon strength functions (PSFs), using the statistical approach to a nuclear decay, seem to be a suitable tool for the description of the electromagnetic transitions in a nucleus. Since the early 50's, when PSFs were first introduced, a plenty of theoretical models were proposed however their validity is still a question. In this work the data from the measurement of so called Two step cascades in the nucleus 196Pt using the reaction 195Pt(n,gamma)196Pt are processed. The experiment was performed at the Nuclear Physics Institute ASCR in Řež near Prague. We assume that these experimental spectra contain important information on PSFs. Comparison of the processed data with a few Monte Carlo simulations is also a part of this thesis