Quantum probability metrics improve distribution comparison in high dimensions.
problem Challenges in comparing probability distributions, especially in high-dimensional and non-compact domains.
method Quantum probability metrics (QPMs) derived from quantum state spaces, overcoming limitations of MMD.
result QPMs offer enhanced sensitivity to subtle distributional differences in high dimensions and improve performance in generative modeling.
We prove the existence of a quantum isometry groups for new classes of metric spaces: (i) geodesic metrics for compact connected Riemannian manifolds (possibly with boundary) and (ii) metric spaces admitting a uniformly distributed probability measure. In the former case it also follows from recent results of the secon…
New metrics improve quantum ensemble learning efficiency and power.
problem Quantum ensembles' distances poorly understood due to measurement constraints.
method Introduce MMD-k hierarchy of integral probability metrics for quantum ensembles. result MMD-k requires fewer samples for full discriminative power at higher k. Jordan algebras in information geometry linked to metrics on probability distributions.
problem Understanding Jordan algebras in information geometry.
method Inspired by Kirillov's coadjoint orbits, a pseudo-Riemannian metric is constructed on Jordan algebra leaves.
result Not all points in the dual space lie on a leaf, and the metric structure depends on the cone of positive functionals.
The idea is considered that a quantum wormhole in a spacetime foam can be described as a Ricci flow. In this interpretation the Ricci flow is a statistical system and every metric in the Ricci flow is a microscopical state. The probability density of the microscopical state is connected with a Perelman's functional of …
This paper applies quantum probability theory to model asset returns, avoiding assumptions about quantum effects.
problem Modeling asset returns with classical probability theory.
method Derives a Schrödinger-like trading equation using quantum probability, linking it to traders' decisions and market behaviors.
result Quantum probability can describe multimodal distributions of asset returns without assuming quantum effects.
Let (M,g) be a compact, connected and oriented Riemannian manifold. We denote D the space of smooth probability density functions on M. In this paper, we show that the Frechet manifold D is equipped with a Riemannian metric g^{D} and an affine connection \nabla^{D} which are infinite dimensional analogues of the Fisher…
This work proposes efficient classical training protocols for IQP circuits to train quantum generative models.
problem Training quantum generative models on industrially relevant probability distributions is challenging due to high computational cost.
method Developed protocols for classical training of IQP circuits, which are hard to sample but have efficient gradient computation.
result Classically trained IQP circuits can efficiently sample from target probability distributions, demonstrating practical quantum advantage.
A quantum walk-based method for generating precise probability distributions efficiently.
problem Generating high-precision probability distributions for various applications.
method Integrates variational quantum circuits with split-step quantum walks to dynamically tune coin parameters and evolve quantum states.
result Achieves high simulation fidelity and reduces computational overhead compared to conventional methods.
Quantum walk algorithm optimizes quantum state preparation for financial simulations.
problem Efficiently loading classical data into quantum states for quantum computers.
method Split-step quantum walks (SSQW) to design parameterized quantum circuits (PQC).
result SSQW facilitates generating desired probability amplitude distributions for quantum simulations.
Quantum probability theory reveals hidden structure in joint probability distributions.
problem Understanding hidden structure in joint probability distributions.
method Modeling joint probability distributions as density operators and applying partial trace.
result Decoding extra information in reduced density operators that captures subsystem interactions.
Novel quantum algorithm for financial market modeling.
problem Accurate quantum state preparation for financial simulation.
method Multi-Split-Steps Quantum Walk (multi-SSQW) with PQC and variational solver.
result Highly accurate modeling of complex financial distributions.
A quantum system can be entirely described by the Kähler structure of the projective space P(H) associated to the Hilbert space H of possible states; this is the so-called geometrical formulation of quantum mechanics. In this paper, we give an explicit link between the geometrical formulation (of finite dimensional qua…
Quantum computing improves fill probability estimation in bond trading.
problem Estimating fill probabilities in complex financial markets with uncertainties.
method Quantum learning algorithms applied to real bond trading data.
result Quantum-enhanced models achieve up to 34% better performance in fill prediction.
Quantum computing offers a quadratic speedup for estimating non-linear functionals.
problem Estimating non-linear functionals of probability distributions.
method Proposes a quantum-inside-quantum Monte Carlo algorithm for a broad class of non-linear estimation problems.
result Achieves a quadratic speedup for non-linear estimation problems, including nested conditional expectations and stochastic optimization.
Quantum probability theory constructs Martingales for non-Brownian financial models.
problem Constructing Martingales for financial models using fractional Brownian motion.
method Quantum probability theory and Wick product.
result Quantum probability framework allows for Martingale construction without Brownian integrals.
Quantum approach models economic decisions with probabilistic and dynamic probabilities.
problem Traditional economic models fail to explain recent financial crises.
method Develops a quantum probabilistic framework for economics.
result Quantum circuits can model cognitive phenomena like preference reversal.
Quantum calculus models stock liquidity issues.
problem Capturing illiquidity in stock price distributions.
method Quantum stochastic calculus applied to finance.
result Modeling the impact of widened bid-ask spreads.
Quantum correlations enhance generative models, providing a new resource for machine learning.
problem Capturing complex probability distributions in unsupervised learning.
method Theoretical and numerical analysis of quantum correlations in generative models.
result Quantum nonlocality and contextuality provide an expressivity advantage over classical models.
We build metrized quantum vector bundles, over a generically transcendental quantum torus, from Riemannian metrics, using Rosenberg's Levi-Civita connections for these metrics. We also prove that two metrized quantum vector bundles, corresponding to positive scalar multiples of a Riemannian metric, have distance zero b…
A new distance metric compares probability distributions using kernel covariance operators.
problem Comparing probability distributions in machine learning tasks.
method Introduces a novel distance metric based on Schatten norm of kernel covariance operators.
result The new distance metric is more discriminative and robust to hyperparameters.
Quantum RNG improves financial risk metrics estimation.
problem Estimating financial risk metrics with high precision.
method Quantum-Enhanced Monte Carlo using QRNG.
result Improved accuracy in VaR and CVaR estimation.
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…
Quantum isometry groups extend to all countable metric spaces, and loose embeddings help understand metric space relationships.
problem Understanding the quantum isometry groups of all countable metric spaces.
method Defining and studying loose embeddability, showing that 0-dimensional compact metric spaces are generically loosely embeddable into the real line.
result 0-dimensional compact metric spaces are generically loosely embeddable into the real line.
Quantum MC simulations generate financial risk distributions efficiently.
problem High computational cost in traditional Monte Carlo simulations.
method Integrates quantum amplitude estimation with stochastic models for equity, rate, and credit risk factors.
result Quantum advantage in scenario generation for financial risk analytics.
Quantum circuits are hard to learn on average.
problem Learning the output distributions of quantum circuits is hard.
method Statistical query model analysis.
result Learning quantum circuits requires exponentially many queries.
The Machina thought experiments pose to major non-expected utility models challenges that are similar to those posed by the Ellsberg thought experiments to subjective expected utility theory (SEUT). We test human choices in the `Ellsberg three-color example', confirming typical ambiguity aversion patterns, and the `Mac…
This work explores using deep NNs to learn quantum systems from probability distributions.
problem Learning quantum systems from limited probability distribution data.
method Using deep neural networks to reconstruct quantum Hamiltonian from probability distributions.
result Deep neural networks can learn quantum Hamiltonians from probability distributions.
The applications of techniques from statistical (and classical) mechanics to model interesting problems in economics and finance has produced valuable results. The principal movement which has steered this research direction is known under the name of `econophysics'. In this paper, we illustrate and advance some of the…
The probability distribution function (PDF) for prices on financial markets is derived by extremization of Fisher information. It is shown how on that basis the quantum-like description for financial markets arises and different financial market models are mapped by quantum mechanical ones.
Quantum method generates unbiased samples from discrete graphical models.
problem Sampling from discrete graphical models is challenging and intractable in high dimensions.
method Embedding graphical models into unitary operators and using quantum circuits.
result Provably generates unbiased and independent samples from general discrete factor models.
Study noncommutative Sobolev inequalities using quantum state metrics.
problem Establishing Sobolev inequalities in noncommutative settings.
method Generalizing monotone metrics in quantum states.
result Developed new matrix-valued Beckner inequalities.
New framework for cyclic quantum causal models with graph separation property.
problem Understanding causal relationships in feedback processes and exotic scenarios.
method Introducing a robust probability rule and a novel graph-separation property, p-separation.
result Established graph-separation properties for all consistent cyclic causal models.
A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits. The optimization dynamics is interpreted as moving in the steepest descent direction with respect to the Quantum Information Geometry, corresponding to the real part of…
Paper proves polynomial equivalence of quantum complexity metrics.
problem Quantum complexity metrics equivalence.
method Study of right-invariant metrics on unitary group.
result All metrics in the equivalence class have polynomial slowdown in approximation.
Develops a new geometric framework for quantum metrics.
problem Quantum metric generalization for pure two-qubit states.
method Support-projected Petz monotone geometry for pure two-qubit families.
result Strictly generalizes SLD/Bures case and includes other metrics.
Study shows quantum behavior near infinity in metric asymptotics.
problem Quantum behavior of metrics near infinity on quasi-projective manifolds.
method Analysis of Bergman kernel function near smooth divisor at infinity of Cheng-Yau metric.
result Quantum phenomenon observed for points very close to the divisor at infinity.
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 Reservoir Computing classifies complex probability distributions and identifies volatility regimes.
problem Statistical and financial classification problems with heavy-tailed distributions and correlated time series.
method Implemented QRC in a superconducting quantum circuit with Josephson junctions.
result QRC outperforms classical methods in limited information scenarios.
We investigate 17 digital currencies making an analogy with quantum systems and develop the concept of eigenportfolios. We show that the density of states of the correlation matrix of these assets shows a behavior between that of the Wishart ensemble and one whose elements are Cauchy distributed. A metric for the parti…
Optimal transport theory applied to quantum states on Grassmannians.
problem Developing optimal transport for quantum states.
method Metric geometry of Grassmannians and spectral theorem for density matrices.
result Wasserstein distance for normal states of von Neumann algebras.
Optimizing quantum graphs yields geodesic nets on surfaces.
problem Finding optimal quantum graphs for geodesic nets.
method Optimizing functionals from spectral theory to find geodesic nets.
result Critical metrics for eigenvalues give rise to geodesic nets.
Researchers propose a non-monotone quantum natural gradient for quantum systems.
problem Applying natural gradient methods to quantum systems without monotonicity.
method Introducing a non-monotone quantum natural gradient (QNG) and demonstrating its superiority over conventional QNG.
result Non-monotone QNG outperforms conventional QNG in terms of convergence speed.
Quantum computers can simulate flow models efficiently.
problem Efficiently simulating continuous flow models on quantum computers.
method Relating flow models to the Schrödinger equation and proving efficient Hamiltonian simulation.
result Quantum computers can prepare qsamples for flow models efficiently.
Quantum circuits represent binary classification trees with binary features.
problem Classifying data using binary classification trees with binary features.
method Quantum circuits and probabilistic approach for traversing decision trees.
result First realization of a decision tree classifier on a quantum device.
Study shows limitations and possibilities of learning quantum circuit output distributions.
problem Learnability of output distributions of local quantum circuits.
method Investigated within two oracle models: statistical query model and direct sample access model.
result Output distributions of super-logarithmic depth Clifford circuits are not efficiently learnable in the statistical query model.
Length metrics can be closely approximated by conformally flat metrics.
problem Approximating length metrics with conformally flat metrics.
method Uniform approximation of length metrics by conformally flat Riemannian metrics.
result Any length metric on \(\mathbb{R}^d\) can be uniformly approximated by conformally flat Riemannian metrics.
Adaptive Quantum Conformal Prediction improves reliability of quantum machine learning predictions.
problem Quantum machine learning lacks robust uncertainty quantification methods.
method Adaptive Conformal Inference applied to quantum conformal prediction to maintain validity over time.
result AQCP achieves target coverage levels and is more stable than standard quantum conformal prediction.