A key element of thermodynamics is small entropy fluctuations away from local maxima at equilibrium. These fluctuating entropy decreases become proportionally larger as the volume decreases within some thermodynamic system. Lessened entropy indicates the formation of organized fluctuating mesoscopic structures. These structures depend upon the microscopic interactions present among the atomic constituents of the system. Entropy fluctuations may be represented by a thermodynamic information metric yielding directly a thermodynamic Ricci curvature scalar R. R is a thermodynamic invariant that is a measure of mesoscopic structure formation within the system. In my talk, I discuss the calculation and the physical interpretation of R in several scenarios: fluid systems, including supercooled liquid water, simple solids, spin systems, quantum fluid models, the quark-meson plasma, and black hole thermodynamics. This range of applications offers a strong argument for the effectiveness of R within thermodynamics.

The entropy of the observable universe has been calculated as S_uni ~ 10¹⁰⁴ k and is dominated by the entropy of super massive black holes. Irreversible processes in the universe can only happen if there is an entropy gap between the entropy of the observable universe S_uni and its maximum entropy S_max: = S_max - S_uni. Thus, the entropy gap is a measure of the remaining potentially available free energy in the observable universe. To compute one needs to know the value of S_max. There is no consensus on whether S_max is a constant or is time-dependent. A time-dependent S_max(t) has been used to represent instantaneous upper limits on entropy growth. However, if we define S_max as a constant equal to the final entropy of the observable universe at its heat death: S_max S_max,HD, we can interpret T as a measure of the remaining potentially available (but not instantaneously available) free energy of the observable universe. The time-dependent slope dS_uni/dt (t) then becomes the best estimate of current entropy-production and T dS_uni/dt(t) is the upper limit to free energy extraction.

Systemic risks have to be vigilantly guided against at all times in order to prevent their contagion across stock markets. New policies also may not work as desired, instead even induce shocks to market, especially those emerging ones. Therefore, timely detection of systemic risks and policy-induced shocks is crucial to safeguard the health of stock markets. In this paper, we show that the relative entropy or Kullback-Liebler divergence can be used to identify systemic risks and policy-induced shocks in stock markets. Concretely, we analyzed the minutely data of two stock indices, the Dow Jones Industrial Average (DJIA) and the Shanghai Stock Exchange (SSE) Composite Index, and examined the temporal variation of relative entropy for them. We show that clustered peaks in relative entropy curves can accurately identify the timing of the 2007–2008 global financial crisis and its precursors, and the 2015 stock crashes in China. Moreover, a sharpest needle-like peak in relative entropy curves, especially for SSE market, always served as a precursor of an unusual market, a strong bull market or a bubble, thus possessing a certain ability of forewarning.

In recent years geologists have put a lot of effort trying to study the evolution of earth using different techniques studying rocks, gases, and water at different channels like mantle, lithosphere and atmosphere. Some of the ways are Estimation of heat flux between the atmosphere and sea ice, Modelling global temperature changes, Groundwater monitoring networks, etc. But algorithms involving the study of earth’s evolution have been a debated topic for decades. Also, there is distinct research on studying the mantle, lithosphere, and atmosphere using isotopic fractionation which this paper will take into consideration to form genes at the former stage. This factor of isotopic fractionation could be molded in QGA to study the earth’s evolution. We combined these factors because the gases containing these isotopes move from mantle to lithosphere or atmosphere through gaps or volcanic eruptions contributing to it. We are likely to use the Rb/Sr and Sm/Nd ratios to study the evolution of these channels. This paper, in general, provides the idea on gathering some information of temperature changes by using isotopic ratios as chromosomes, in QGA the chromosomes depict the characteristic of a generation. Here these ratios depict the temperature characteristic and other steps of QGA would be molded to study these ratios in the form of temperature changes, which would further signify the evolution of earth based on the study that temperature changes with the change in isotopic ratios. This paper will collect these distinct studies and embed them into an upgraded quantum genetic algorithm called Quantum Genetic Terrain Algorithm or Quantum GTA.

We are the verge of a technological revolution. Over the last couple of years the first computational devices have become commercially available that promise to exploit so-called quantum supremacy. Even though the thermodynamic cost for processing classical information has been known since the 1960s, the thermodynamic description of quantum computers is still at its infancy. This is due to the fact that many notions of classical thermodynamics, such as work and heat, do not readily generalize to quantum systems. In this keynote, we will outline a novel conceptual framework of an emerging theory, Quantum Thermodynamics, and illustrate its applicability, mindset, and questions with a few pedagogical examples.