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.
AI in finance uses quantum logic for better decision-making.
Paper tackles interpretability issues in deep learning models.
Quantum Signal Processing reduces derivative pricing quantum resource requirements.
Generative AI decodes quantum codes without labeled data.
New fault-tolerant quantum gates for homological LDPC codes with constant or almost-constant rate.
Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference …
Quantum algorithms accelerate financial risk computation.
Quantum codes linked to abelian varieties, providing mathematical rigor.
Study on topological order on fractal geometries, proving no-go theorem and fault-tolerant gates.
A basic question in the theory of fault-tolerant quantum computation is to understand the fundamental resource costs for performing a universal logical set of gates on encoded qubits to arbitrary accuracy. Here we consider qubits encoded with constant space overhead (i.e. finite encoding rate) in the limit of arbitrari…
Gaussian processes (GP) are a widely used model for regression problems in supervised machine learning. Implementation of GP regression typically requires logic gates. We show that the quantum linear systems algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] can be applied to Gaussian process regre…
Quantum tech speeds up financial risk assessment.
Optimizes train schedules and maintenance using CP and QA.
We introduce a differential geometric framework for describing families of quantum error-correcting codes and for understanding quantum fault tolerance. This work unifies the notion of topological fault tolerance with fault tolerance in other kinds of quantum error-correcting codes. In particular, we use fibre bundles …
Topological theory for qLDPC codes enables non-Clifford gates and magic state injection.
Quantum advantage in derivative pricing requires 8k qubits and 54M T-depth.
Quantum memory limits set by relativity theory.
The relationship between expectation and price is commonly established with two principles: no-arbitrage, which asserts that both maps are positive; and equivalence, which asserts that the maps share the same null events. Constructed from the Arrow-Debreu securities, classical and quantum models of economics are then d…
Quantum computing offers new solutions for financial optimization, pricing, risk, and security.
Quantum algorithms improve VaR and CVaR estimation for financial derivatives.
Paper presents a new VMBQC model with fewer parameters for better generative modeling.
We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer. In this regression algorithm, we concatenate the independent and dependent variables as a combined vector, and encode the high-order correlations between them into a dictionary optimized for…
Quantum codes on hyperbolic lattices outperform Euclidean ones with higher rates and lower overhead.
GKP codes connect quantum gates to algebraic curves, enabling fault-tolerant quantum computation.
The abstract explores a new wave equation linking quantum mechanics and complex adaptive systems.
We introduce the hemicubic codes, a family of quantum codes obtained by associating qubits with the -faces of the -cube (for ) and stabilizer constraints with faces of dimension . The quantum code obtained by identifying antipodal faces of the resulting complex encodes one logical qubit into $N = 2^…
AlphaLogics mines market logic to generate interpretable alpha factors.
Starting from the Fermat's principle of least action, which governs classical and quantum mechanics and from the theory of exterior differential forms, which governs the geometry of curved manifolds, we show how to derive the equations governing neural networks in an intrinsic, coordinate invariant way, where the loss …
Explaining neural network computation in terms of probabilistic/fuzzy logical operations has attracted much attention due to its simplicity and high interpretability. Different choices of logical operators such as AND, OR and XOR give rise to another dimension for network optimization, and in this paper, we study the o…
Polyhedral semantics for intermediate logics; Nerve Criterion ensures completeness.
Recent years have witnessed the great success of deep neural networks in many research areas. The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of logical reasoning. However, the concrete ability of logical reas…
Boolean logic used for neural network training and inference, with convergence analysis.
Neural Logic Reasoning integrates deep learning and symbolic logic for better prediction tasks.
Quantum algorithm speeds up MIP solving by a near-quadratic factor.
New quantum code breaks distance barrier with transversal non-Clifford gates.
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…
The purpose of this paper is to discuss how topology and geometry provide, in many instances, the connective tissue that enables logical comprehension. We illustrate this theme with many examples including Venn diagrams, knot diagrams, knot-logical diagrams and an arrow of reference that elucidates self-reference and G…
This study proposes a logic architecture for the high-speed and power efficiently training of a gradient boosting decision tree model of binary classification. We implemented the proposed logic architecture on an FPGA and compared training time and power efficiency with three general GBDT software libraries using CPU a…
D-Wave hybrid quantum-classical portfolio optimization shows classical decomposition is key, not quantum sampling.
Kernel for STL formulae enables machine learning in temporal logic.
Transformers learn to predict temporal logic solutions from classical solver outputs.
Many software analysis methods have come to rely on machine learning approaches. Code segmentation - the process of decomposing source code into meaningful blocks - can augment these methods by featurizing code, reducing noise, and limiting the problem space. Traditionally, code segmentation has been done using syntact…
While neural networks are good at learning unspecified functions from training samples, they cannot be directly implemented in hardware and are often not interpretable or formally verifiable. On the other hand, logic circuits are implementable, verifiable, and interpretable but are not able to learn from training data …
Knowledge graph reasoning, which aims at predicting the missing facts through reasoning with the observed facts, is critical to many applications. Such a problem has been widely explored by traditional logic rule-based approaches and recent knowledge graph embedding methods. A principled logic rule-based approach is th…
We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as function approximators, and logic programming---as a symbolic processor for objects with properties, relations, logic connectives, and quantifier…
Study logical generalization in GNNs using a new benchmark.
The logic of uncertainty is not the logic of experience and as well as it is not the logic of chance. It is the logic of experience and chance. Experience and chance are two inseparable poles. These are two dual reflections of one essence, which is called co~event. The theory of experience and chance is the theory of c…
Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Probabilistic logic reasoning, on the other hand, is capable to take consistent and robust decisions in complex environments. The integration of deep learn…