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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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48 results for quantum economics

Quantum crypto-economics models price risks in blockchain technology.

problem Quantum technology's potential to undermine blockchain security.
method Building financial models to price quantum risk in blockchain scenarios.
result Quantum crypto-economics models can assess and price quantum risks in blockchain.

Chaos and nonlinear economic dynamics are addressed for a quantum coupled map lattice model of an artificial economy, with quantized supply and demand equilibrium conditions. The measure theoretic properties and the patterns that emerge in both the economic business volume dynamics' diagrams as well as in the quantum m…

2012-02-29abs ↗pdf ↗

A new constructivist approach to modeling in economics and theory of consciousness is proposed. The state of elementary object is defined as a set of its measurable consumer properties. A proprietor's refusal or consent for the offered transaction is considered as a result of elementary economic measurement. We were al…

2011-10-24abs ↗pdf ↗

We analyze complexity of financial (and general economic) processes by comparing classical and quantum-like models for randomness. Our analysis implies that it might be that a quantum-like probabilistic description is more natural for financial market than the classical one. A part of our analysis is devoted to study t…

2007-04-22abs ↗pdf ↗

Econophysics has developed as a research field that applies the formalism of Statistical Mechanics and Quantum Mechanics to address Economics and Finance problems. The branch of Econophysics that applies of Quantum Theory to Economics and Finance is called Quantum Econophysics. In Finance, Quantum Econophysics' contrib…

2015-08-26abs ↗pdf ↗

We analyze the relationships between game theory and quantum mechanics and the extensions to statistical physics and information theory. We use certain quantization relationships to assign quantum states to the strategies of a player. These quantum states are contained in a density operator which describes the new quan…

2006-09-11abs ↗pdf ↗
Quantum Econophysicsphysics.soc-ph

The relationships between game theory and quantum mechanics let us propose certain quantization relationships through which we could describe and understand not only quantum but also classical, evolutionary and the biological systems that were described before through the replicator dynamics. Quantum mechanics could be…

2006-09-28abs ↗pdf ↗

Quantum computing promises faster insurance contract valuation.

problem Computational intensity of insurance contract valuation.
method Investigation of quantum computing's applicability for insurance contracts using Amplitude Estimation.
result Quantum computing can significantly speed up insurance contract valuation.

QLSTM outperforms LSTM in predicting KSE 100 index movements.

problem Predicting stock market movement in uncertain economic conditions.
method Used LSTM and QLSTM models on monthly data of economic indicators.
result QLSTM provided more accurate predictions of KSE 100 index values.

By analyzing the relationships between a socioeconomical system modeled through evolutionary game theory and a physical system modeled through quantum mechanics we show how although both systems are described through two theories apparently different both are analogous and thus exactly equivalents. The extensions of qu…

2007-04-30abs ↗pdf ↗

Financial volatility risk and its relation to a business cycle-related intrinsic time is addressed through a multiple round evolutionary quantum game equilibrium leading to turbulence and multifractal signatures in the financial returns and in the risk dynamics. The model is simulated and the results are compared with …

2011-07-13abs ↗pdf ↗

Uncertainty in economics still poses some fundamental problems illustrated, e.g., by the Allais and Ellsberg paradoxes. To overcome these difficulties, economists have introduced an interesting distinction between 'risk' and 'ambiguity' depending on the existence of a (classical Kolmogorovian) probabilistic structure m…

2011-05-09abs ↗pdf ↗

A new constructivist approach to modeling in economics and theory of consciousness is proposed. The state of elementary object is defined as a set of its measurable consumer properties. A proprietor's refusal or consent for the offered transaction is considered as a result of elementary economic measurement. Elementary…

2011-10-21abs ↗pdf ↗

We pursue the quantum-mechanical challenge to the efficient market hypothesis for the stock market by employing the quantum Brownian motion model. We utilize the quantum Caldeira-Leggett master equation as a possible phenomenological model for the stock-market-prices fluctuations while introducing the external harmonic…

2018-12-01abs ↗pdf ↗

Quantum GBS boosts asset clustering for robust statistical arbitrage portfolios.

problem Identifying co-moving assets from correlation matrices for statistical arbitrage.
method Mapping S&P 500 correlation data to GBS-compatible adjacency matrices, benchmarking classical and quantum clustering algorithms.
result Quantum GBS generates superior alpha during high volatility periods, persisting under low-loss conditions.

The abstract explores a new wave equation linking quantum mechanics and complex adaptive systems.

problem Understanding the underlying mechanism of distribution formation in complex quantum entanglement.
method Exploring the logical relationship between Schrödinger's wave equation and Shi's trading volume-price wave equation in finance.
result A non-localized wave equation in quantum mechanics reveals the invariance of interaction as a universal law.

Modern approaches to stock pricing in quantitative finance are typically founded on the 'Black-Scholes model' and the underlying 'random walk hypothesis'. Empirical data indicate that this hypothesis works well in stable situations but, in abrupt transitions such as during an economical crisis, the random walk model fa…

2011-10-24abs ↗pdf ↗

The Allais and Ellsberg paradoxes show that the expected utility hypothesis and Savage's Sure-Thing Principle are violated in real life decisions. The popular explanation in terms of 'ambiguity aversion' is not completely accepted. On the other hand, we have recently introduced a notion of 'contextual risk' to mathemat…

2011-05-09abs ↗pdf ↗

Current economic theories miss most of economic dynamics.

problem Accuracy of economic theories and policies depend on economic variables and processes.
method Identify and analyze overlooked economic variables and processes.
result Many economic variables and processes not accounted for in current theories.

Quantum ML promises faster data analysis but faces trainability challenges.

problem Challenges in training quantum machine learning models.
method Review of current methods and applications of quantum neural networks and quantum deep learning.
result Opportunities for quantum advantage in quantum machine learning.

Modeling business cycles via collective risk fluctuations in economic agents' risk space.

problem Understanding and predicting business cycles through economic agents' risk dynamics.
method Continuous numerical risk grades for economic agents, modeling collective economic variables and flows as functions of risk coordinates, deriving equations for their evolution.
result Business and credit cycles are explained as fluctuations of collective economic variables and their mean risks in the risk space of economic agents.

QGAA learns latent quantum states, reducing errors in quantum data generation.

problem Learning latent representations for quantum data generation.
method Quantum Generative Adversarial Autoencoder (QGAA) combining QAE and QGAN.
result Average errors in energies for H2 and LiH are 0.02 Ha and 0.06 Ha respectively, demonstrating QGAA's potential.

Quantum machine learning uses quantum cross entropy to minimize loss, but measurement loss affects this process.

problem Quantum machine learning's loss minimization through cross entropy is affected by measurement outcomes.
method Defined quantum cross entropy, proved its lower bounds, and investigated its relation to quantum fidelity and likelihood.
result Quantum cross entropy is lower-bounded by negative log-likelihood when derived from quantum data, but measurement outcomes can cause loss.

Quantum Earth Mover's distance improves stability and efficiency in quantum learning.

problem Quantum learning's loss landscapes often lead to poor local minima and gradients.
method Introduced the quantum Earth Mover's (EM) distance and proposed a quantum Wasserstein generative adversarial network (qWGAN).
result The quantum EM distance makes quantum learning more stable and efficient.

Quantum machine learning models can approximate any continuous function.

problem Theoretical understanding of quantum feature maps in machine learning.
method Proving universal approximation property of quantum machine learning models in quantum-enhanced feature spaces.
result Quantum machine learning models are universal approximators of continuous functions.

Quantum autoencoders allow for reducing the amount of resources in a quantum computation by mapping the original Hilbert space onto a reduced space with the relevant information. Recently, it was proposed to employ approximate quantum adders to implement quantum autoencoders in quantum technologies. Here, we carry out …

2018-07-27abs ↗pdf ↗

We define quantum exterior product wedge_h and quantum exterior differential d_h on Poisson manifolds, of which symplectic manifolds are an important class of examples. Quantum de Rham cohomology is defined as the cohomology of d_h. We also define quantum Dolbeault cohomology. Quantum hard Lefschetz theorem is proved. …

1998-06-30abs ↗pdf ↗

VQAs use classical optimization to train quantum circuits, promising quantum advantage.

problem High computational cost of quantum simulations and solving large-scale problems.
method Variational Quantum Algorithms (VQAs) use classical optimizers to train parametrized quantum circuits.
result VQAs are a promising strategy for obtaining quantum advantage.