arXiv subjects

Lizhi Xin

Publications and source records attributed to Lizhi Xin.

7 recordsLinked to original sources

arXiv2024

QxEAI: Quantum-like evolutionary algorithm for automated probabilistic forecasting

Forecasting, to estimate future events, is crucial for business and decision-making. This paper proposes QxEAI, a methodology that produces a probabilistic forecast that utilizes a quantum-like evolutionary algorithm based on training a quantum-like logic decision tree and a classical value tree on a small number of related time series. We demonstrate how the application of our quantum-like evolutionary algorithm to forecasting can overcome the challenges faced by classical and other machine learning approaches. By using three real-world datasets (Dow Jones Index, retail sales, gas consumption), we show how our methodology produces accurate forecasts while requiring little to none manual work.

physics.soc-ph↗

arXiv2023

A computational model for synaptic message transmission

A computational model incorporating insights from quantum theory is proposed to describe and explain synaptic message transmission. We propose that together, neurotransmitters and their corresponding receptors, function as a physical "quantum decision tree" to "decide" whether to excite or inhibit the synapse. When a neurotransmitter binds to its corresponding receptor, it is the equivalent of randomly choosing different "strategies"; a "strategy" has two actions to take: excite or inhibit the synapse with a certain probability. The genetic programming can be applied for learning the observed data sequence to simulate the synaptic message transmission.

q-bio.NC↗

arXiv2023

Machine learning for discovering laws of nature

Based on Darwin's natural selection, we developed "machine scientists" to discover the laws of nature by learning from raw data. "Machine scientists" construct physical theories by applying a logic tree (state Decision Tree) and a value tree (observation Function Tree); the logical tree determines the state of the entity, and the value tree determines the absolute value between the two observations of the entity. A logic Tree and a value tree together can reconstruct an entity's trajectory and make predictions about its future outcomes. Our proposed algorithmic model has an emphasis on machine learning - where "machine scientists" builds up its experience by being rewarded or punished for each decision they make - eventually leading to rediscovering Newton's equation (classical physics) and the Born's rule (quantum mechanics).

cs.LG↗

arXiv2023

Machine learning for decision-making under uncertainty

We live in a world brimming with uncertainty, where we constantly have to make a lot of decisions under incomplete information. We are firm believers that our subjective belief cannot be computed by rigorous mathematical formula; instead based on Darwin's natural selection (the evolution process is simulated by machine learning with genetic programming), a proposed computational model that incorporates insights from quantum theory to describe and explain decision-making under uncertainty. Unlike other decision-making theories that explain the decision-making process through probability theory, our proposed decision theory discovers "laws" of thought by learning observed historical data. There is no differential equation and no transition probability in our decision theory, our decision model has an emphasis on machine learning, where decision-makers build-up their experience by being rewarded or punished for each decision they make and prepare them for making better decisions in the future. We do not model with the usual utility function, but with quantum decision tree that simulates people's decision process. Each quantum decision tree includes a set of strategies; every time a decision is made, the decision-maker first chooses a strategy from the quantum decision tree's strategy pool, and then chooses an action based on the degree of belief which is obtained by genetic programming based on maximizing expected value.

physics.soc-ph↗

arXiv2022

Quantum measurement: a game between observer and nature?

What is the observer's role in quantum measurement? Obviously, observers prepare the apparatus, observe and interpret the measured results. Although the observer will have a certain influence on the measurement results by setting up the measuring apparatus, we don't believe human consciousness cause reducing of wave packet; also observers are certainly required to interpret the measured results with physical meanings. We believe observers build up their experience of the external world by playing games with nature, and then "decode" the nature based on their experiences. We propose a quantum decision theory approach to explain the role of the observer in quantum measurements, and pointed out that a set of quantum decision trees (strategies to answer natural questions with yes/no logic) can be optimized to deal with the challenges of nature through quantum genetic programming based on maximization of the expected value of the observers; Quantum decision trees can discover the dynamics rules of quantum entities and Just as classical mechanics uses the principle of least action to obtain the trajectories of particles, we use the principle of maximum expected value to approximately obtain the kinematic "trajectories" of quantum entities by learning from natural historical "events" (measured results); even we can "reconstruct" the past of quantum entity, because we don't know the prior information of quantum entity, it is very difficult to predict the future of the nature.

quant-ph↗

arXiv2022

Can the observer know the state of Schrodinger's cat without opening the box?

In order to know if the Schrodinger's cat is alive or dead without opening the box, observers have to play a game with nature. The observers have to "guess" (with degrees of belief) the state of the cat due to incomplete information; in other words, the observers' decision has to be made under uncertainty. We propose a quantum expected value theory for decision-making under uncertainty. Value operator is proposed to guide observers to choose corresponding actions based on their subjective beliefs through objective quantum world by maximizing the value from the measured historical results. The value operator, as a quantum decision tree, can be constructed from both quantum gates and logic operations. Genetic programming is applied to optimize quantum decision trees.

quant-ph↗

arXiv2022

Decision-making under uncertainty: a quantum value operator approach

We propose a quantum expected value theory for decision-making under uncertainty. Quantum density operator as value operator is proposed to simulate people's subjective beliefs. Value operator guides people to choose corresponding actions based on their subjective beliefs through objective world. The value operator can be constructed from quantum gates and logic operations as a quantum decision tree. The genetic programming is used to optimize and auto-generate quantum decision trees.

physics.soc-ph↗