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.
Unified framework for robust causal directionality in quantum systems under MNAR observation.
This paper compares classical shadows and direct quantum measurement for efficient information extraction.
Sketch Tomography improves quantum state estimation accuracy.
We propose a version of the non-relativistic quantum mechanics in which the pure states of a quantum system are described as sections of a Hilbert (generally infinitely-dimensional) fibre bundle over the space-time. There evolution is governed via (a kind of) a parallel transport in this bundle. Some problems concernin…
In this article we model a financial derivative price as an observable on the market state function. We apply geometric techniques to integrating the Heisenberg Equation of Motion. We illustrate how the non-commutative nature of the model introduces quantum interference effects that can act as either a drag or a boost …
Enhances quantum sensing by eliminating multiple oscillations in field amplitude estimation.
The problem of using observed correlations to infer causal relations is relevant to a wide variety of scientific disciplines. Yet given correlations between just two classical variables, it is impossible to determine whether they arose from a causal influence of one on the other or a common cause influencing both, unle…
Quantum Portfolios of quantum algorithms encoded on qbits have recently been reported. In this paper a discussion of the continuous variables version of quantum portfolios is presented. A risk neutral valuation model for options dependent on the measured values of the observables, analogous to the traditional Black-Sch…
Quantum ELMs use a quantum reservoir to learn from data, with limits on expressivity and scalability.
It is postulated that quantum gravity is a sum over causal structures coupled to matter via scale evolution. Quantized causal structures can be described by studying simple matrix models where matrices are replaced by an algebra of quantum mechanical observables. In particular, previous studies constructed quantum grav…
Analyzes quantization of flux observables in gauge theories.
Geometric quantization shows compatibility of symmetries on coadjoint orbits and Kähler-Einstein manifolds.
Short note observes quantum Hochschild homology as a composition of known operations.
Diffusion maps help learn complex quantum phase transitions from data.
We construct a new type of quantum walks on simplicial complexes as a natural extension of the well-known Szegedy walk on graphs. One can numerically observe that our proposing quantum walks possess linear spreading and localization as in the case of the Grover walk on lattices. Moreover, our numerical simulation sugge…
Study shows observability for Schrödinger equations on product manifolds with specific conditions.
New quantum integrals discovered for a spin chain model.
Quantum calculus models stock liquidity issues.
This paper applies quantum theory to cost accounting, focusing on WIP valuation.
Quantum machine learning improves hedging in finance.
We present a generally covariant approach to quantum mechanics in which generalized positions, momenta and time variables are treated as coordinates on a fundamental "phase-spacetime." We show that this covariant starting point makes quantization into a purely geometric flatness condition. This makes quantum mechanics …
Quantum algorithm speeds up Lasso regression by quadratically faster per iteration.
Quantum model improves safety in machine learning.
We investigate the perturbative aspects of Rozansky-Witten's 3d -model using Costello's approach to the Batalin-Vilkovisky (BV) formalism. We show that the BV quantization (in Costello's sense) of the model, which produces a perturbative quantum field theory, can be obtained via the configuration space method of reg…
The Accardi-Boukas quantum Black-Scholes framework, provides a means by which one can apply the Hudson-Parthasarathy quantum stochastic calculus to problems in finance. Solutions to these equations can be modelled using nonlocal diffusion processes, via a Kramers-Moyal expansion, and this provides useful tools to under…
Quantum computing speeds up interest rate derivative pricing using LMM.
Fundamental weight systems identified as quantum states.
Improves VQAs by balancing classical and quantum training resources.
Quantum systems are viewed as emergent systems from the fundamental degrees of freedom. The laws and rules of quantum mechanics are understood as an effective description, valid for the emergent systems and specially useful to handle probabilistic predictions of observables. After introducing the geometric theory of Ha…
Quantum walks are at the heart of modern quantum technologies. They allow to deal with quantum transport phenomena and are an advanced tool for constructing novel quantum algorithms. Quantum walks on graphs are fundamentally different from classical random walks analogs, in particular, they walk faster than classical o…
The geometry of cosets in the subgroups H of the two-generator free group G =\textless{} a, b \textgreater{} nicely fits, via Grothendieck's dessins d'enfants, the geometry of commutation for quantum observables. Dessins stabilize point-line incidence geometries that reflect the commutation of (generalized) Pauli opera…
Generative neural samplers estimate quantum spin system properties.
Quantum models show improved performance in overparameterized regimes.
Quantum classification robustness improved via quantum hypothesis testing.
In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interp…
Study uses supervised learning to classify quantum phases with limited measurements.
Quantum theory reinterprets financial pricing by focusing on observable price transitions.
In this paper, we give a precise and workable definition of a quantum knot system, the states of which are called quantum knots. This definition can be viewed as a blueprint for the construction of an actual physical quantum system. Moreover, this definition of a quantum knot system is intended to represent the "quantu…
Quantum algorithms for financial derivatives and credit risk.
In this paper we propose a geometrization of the non-relativistic quantum mechanics for mixed states. Our geometric approach makes use of the Uhlmann's principal fibre bundle to describe the space of mixed states and as a novelty tool, to define a dynamic-dependent metric tensor on the principal manifold, such that the…
Quantum federated learning improves with non-IID data using one-shot communication.
Study quantum diffusion on spectral triples and spinor bundles.
Quantum model generates financial data with fewer parameters.
QCircuitBench provides a dataset for evaluating AI's ability to design quantum algorithms.
We present an axiomatic modification of quaternionic quantum mechanics with a possible-worlds semantics capable of predicting essential "nonquantum" features of an observable universe model - the dimensionality and topology of spacetime, the existence, the signature and a specific form of a metric on it, and certain na…
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…
Quantum oracles help identify counterfactuals better than classical ones.
Quantum models face barren plateaus, but specific losses can be trainable.