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.
Quantum systems learn like machine learning models, influenced by dissipation.
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…
In this paper we present some conditions for the (strong) stabilizability of an n-D Quantum MIMO system P(X). It contains two parts. The first part is to introduce the n-D Quantum MIMO systems where the coefficients vary in the algebra of Q-meromorphic functions. Then we introduce some conditions for the stabilizabilit…
Extended quantum state result for gl_n weight systems.
We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety…
The paper studies distributions and controllability in quantum mechanical systems.
Study Vassiliev invariants for virtual knots, expanding quantum theory.
This review covers quantum computing applications in finance and blockchain.
The abstract explores a new wave equation linking quantum mechanics and complex adaptive systems.
Quantum control is valuable for various quantum technologies such as high-fidelity gates for universal quantum computing, adaptive quantum-enhanced metrology, and ultra-cold atom manipulation. Although supervised machine learning and reinforcement learning are widely used for optimizing control parameters in classical …
Unified framework for robust causal directionality in quantum systems under MNAR observation.
A decentralized online quantum cash system, called qBitcoin, is given. We design the system which has great benefits of quantization in the following sense. Firstly, quantum teleportation technology is used for coin transaction, which prevents from the owner of the coin keeping the original coin data even after sending…
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…
Study on learning quantum dynamics without direct interaction.
New MBL hidden Born machine learns various tasks.
Fundamental weight systems identified as quantum states.
Physicists use quantum models to describe the behavior of physical systems. Quantum models owe their success to their interpretability, to their relation to probabilistic models (quantization of classical models) and to their high predictive power. Beyond physics, these properties are valuable in general data science. …
Quantum mechanics models for financial Black-Scholes model.
Quantum memory limits set by relativity theory.
New method uses single quantum state for machine learning tasks, improving accuracy.
Quantum learning complexity reviewed using information theory.
The financial market entropy is modeled using open quantum systems.
Paper tackles interpretability issues in deep learning models.
Quantum systems with scrambling improve temporal information processing, but scaling requires exponential overhead.
Abstract reviews symmetry and reduction in dynamical systems.
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers …
Enhances quantum computing for symmetrical systems, proving a new class of problems.
Generative neural samplers estimate quantum spin system properties.
This paper deals with a general method for the reduction of quantum systems with symmetry. For a Riemannian manifold M admitting a compact Lie group G as an isometry group, the quotient space Q = M/G is not a smooth manifold in general but stratified into a collection of smooth manifolds of various dimensions. If the a…
Recommender systems play an essential role in the modern business world. They recommend favorable items like books, movies, and search queries to users based on their past preferences. Applying similar ideas and techniques to Monte Carlo simulations of physical systems boosts their efficiency without sacrificing accura…
New quantum kernels avoid overfitting by combining local and global components.
Researchers propose a non-monotone quantum natural gradient for quantum systems.
We study the quantum synchronization between a pair of two-level systems inside two coupled cavities. By using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum synchronization between both two-level systems. Moreover, we can identify in th…
Predicts coherence from quantum heat engine noise using machine learning.
How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question …
Quantum Gaussian processes enable scalable quantum learning.
This work explores using deep NNs to learn quantum systems from probability distributions.
We propose a formulation of a Lorentzian quantum geometry based on the framework of causal fermion systems. After giving the general definition of causal fermion systems, we deduce space-time as a topological space with an underlying causal structure. Restricting attention to systems of spin dimension two, we derive th…
To formulate the universal constraints of quantum statistics data of generic long-range entangled quantum systems, we introduce the geometric-topology surgery theory on spacetime manifolds where quantum systems reside, cutting and gluing the associated quantum amplitudes, specifically in 2+1 and 3+1 spacetime dimension…
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…
Flow-VQE uses generative flows to optimize VQE efficiently.
Study symmetry breaking in quantum mechanics to understand many-body physics.
Investigates quantum vs classical portfolio optimization of 60 stocks.
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…
Quantum machine learning improves satellite image alignment.
Quantum reservoir computing needs coherence influx for effective information processing.
Some simple examples from quantum physics and control theory are used to illustrate the application of the theory of Lie systems. We will show, in particular, that for certain physical models both of the corresponding classical and quantum problems can be treated in a similar way, may be up to the replacement of the in…
We introduce systems of objects and operators in linear monoidal categories called -systems. A -system satisfying several additional assumptions gives rise to a topological invariant of triples (a closed oriented 3-manifold , a principal bundle over , a link in ). This construction generalizes …