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.
CSM-NN uses neural networks to speed up and improve the accuracy of logic circuit simulations.
Combines neural networks and logic circuits for interpretable, accurate, and cost-effective learning.
Deep networks can be understood as logical circuits, improving interpretability and generalization.
BOiLS optimizes circuit quality using Bayesian optimization.
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…
The focus of this paper is on intrinsic methods to detect overfitting. By intrinsic methods, we mean methods that rely only on the model and the training data, as opposed to traditional methods (we call them extrinsic methods) that rely on performance on a test set or on bounds from model complexity. We propose a famil…
Quantum tech speeds up financial risk assessment.
In this paper, a spintronic neuromorphic reconfigurable Array (SNRA) is developed to fuse together power-efficient probabilistic and in-field programmable deterministic computing during both training and evaluation phases of restricted Boltzmann machines (RBMs). First, probabilistic spin logic devices are used to devel…
Quantum Signal Processing reduces derivative pricing quantum resource requirements.
Evolutionary strategy optimizes quantum circuit design and parameters.
We constructed an analog electrical circuit which generates fluctuations in which probability density function has power law tails. In the circuit fluctuations with an arbitrary exponent of the power law can be obtained by adjusting the resistance. With this low cost circuit the random fluctuations which have the simil…
COLEP improves robustness of conformal prediction via probabilistic circuits.
Quantum mechanics fundamentally forbids deterministic discrimination of quantum states and processes. However, the ability to optimally distinguish various classes of quantum data is an important primitive in quantum information science. In this work, we train near-term quantum circuits to classify data represented by …
Active sampling improves design space exploration for analog circuits.
Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (eg, radiation effects) into an existing compact model is not triv…
In this paper, we firstly introduce a method to efficiently implement large-scale high-dimensional convolution with realistic memristor-based circuit components. An experiment verified simulator is adapted for accurate prediction of analog crossbar behavior. An improved conversion algorithm is developed to convert conv…
Quantum circuits reveal pathways to dequantization in machine learning models.
Generating eye diagrams by using a circuit simulator can be very computationally intensive, especially in the presence of nonlinearities. It often involves multiple Newton-like iterations at every time step when a SPICE-like circuit simulator handles a nonlinear system in the transient regime. In this paper, we leverag…
Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural net…
Compact models learn photocurrent dynamics from radiation-induced excess carrier density.
Quantum circuit Born machines are generative models which represent the probability distribution of classical dataset as quantum pure states. Computational complexity considerations of the quantum sampling problem suggest that the quantum circuits exhibit stronger expressibility compared to classical neural networks. O…
This work shows how to efficiently simulate parts of quantum landscapes using classical computers.
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…
Quantum neural tangent kernels help understand variational quantum circuits in machine learning.
New fault-tolerant quantum gates for homological LDPC codes with constant or almost-constant rate.
The manual design of analog circuits is a tedious task of parameter tuning that requires hours of work by human experts. In this work, we make a significant step towards a fully automatic design method that is based on deep learning. The method selects the components and their configuration, as well as their numerical …
We propose a neural information processing system which is obtained by re-purposing the function of a biological neural circuit model, to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce Neuronal Circuit Policies (NCPs), defined as…
Study bounds VAR model's circuit complexity, showing it's limited to TC^0 circuits.
Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for a subset of classically simulable quantum circuit models. The gradient-free algorithm, presented as a sequence of exactly solvable …
This work proposes efficient classical training protocols for IQP circuits to train quantum generative models.
VQAs use classical optimization to train quantum circuits, promising quantum advantage.
Quantum advantage in derivative pricing requires 8k qubits and 54M T-depth.
Bayesian approach optimizes quantum circuits for noisy hardware.
The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. How…
Quantum circuits represent binary classification trees with binary features.
Quantum models avoiding barren plateaus can also be efficiently simulated classically.
SGLBO optimizes quantum circuits with fewer measurements, improving accuracy and noise resilience.
A quantum model classifies financial sentiment by mapping text chunks to quantum circuits.
Common nonlinear activation functions used in neural networks can cause training difficulties due to the saturation behavior of the activation function, which may hide dependencies that are not visible to vanilla-SGD (using first order gradients only). Gating mechanisms that use softly saturating activation functions t…
Quantum circuits explained using Shapley values for better understanding.
New method reduces uncertainty in high-dimensional circuits by automatically determining tensor rank and adaptive sampling.
UKM framework optimizes VQCs, showing QCL performance is bounded.
We introduce SIM-CE, an advanced, user-friendly modeling and simulation environment in Simulink for performing multi-scale behavioral analysis of the nervous system of Caenorhabditis elegans (C. elegans). SIM-CE contains an implementation of the mathematical models of C. elegans's neurons and synapses, in Simulink, whi…
Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data. The quantum computing community has recently begun to generalize this idea and to look for potential applications. In this work, we derive an adversarial algorithm for the problem of approxim…
Quantum computing promises faster insurance contract valuation.
A quantum walk-based method for generating precise probability distributions efficiently.
LLMs optimize quantum circuits by iteratively improving proposals with feedback and memory traces.
Metalearned neural circuit performs inference over open classes.