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Quantum-Classical Hybrid Algorithm for the Simulation of All-Electron Correlation

Computational Physics

While the treatment of chemically relevant systems containing hundreds or even thousands of electrons remains beyond the reach of quantum devices, the development of quantum-classical hybrid algorithm

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Adaptation and Self-Organizing Systems

Physics and Society

Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning

The future of mobility-as-a-Service (Maas)should embrace an integrated system of ride-hailing, street-hailing and ride-sharing with optimised intelligent vehicle routing in response to a real-time, st

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Data Structures and Algorithms

Deep Learning-based Hybrid Graph-Coloring Algorithm for Register Allocation

Stats Machine Learning

Register allocation, which is a crucial phase of a good optimizing compiler, relies on graph coloring. Hence, an efficient graph coloring algorithm is of paramount importance. In this work we try to l

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Chemical Physics

Dynamical Self-energy Mapping (DSEM) for quantum computing

For noisy intermediate-scale quantum (NISQ) devices only a moderate number of qubits with a limited coherence is available thus enabling only shallow circuits and a few time evolution steps in the cur

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Artificial Intelligence

Improving probability selecting based weights for Satisfiability Problem

The Boolean Satisfiability problem (SAT) is important on artificial intelligence community and the impact of its solving on complex problems. Recently, great breakthroughs have been made respectively

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Logic in Computer Science

Hybrid Gradient Descent and Linear Programming for Deep Neural Networks

A Solver + Gradient Descent Training Algorithm for Deep Neural Networks

We present a novel hybrid algorithm for training deep neural networks that combines the state-of-the-art gradient descent (gd) method with a mixed integerlinear programming (milp) solver, outperforming gd and variants in terms of accuracy as well as resource and data efficiency for both regression and classification tasks.

Our algorithm, called gdsolver, works as follows : given a dnn as input, gdsolver invokes gd to partially train until it gets stuck in a local minima, at which point it invokes an milpsolver to exhaustively search a region of the loss landscape around the weightassignments of final layer parameters with the goal of tunnelling through and escaping the local minima.

EESS Systems and Control

A Machine Learning Approach to Boosting Computational Efficiency in Quantum Control Algorithms

Recommender System Expedited Quantum Control Optimization

Quantum control optimization algorithms are routinely used to generate optimal quantum gates or efficient quantum state transfers.

However, there are two main challenges in designing efficient optimization algorithms, namely overcoming the sensitivity to local optima and improving the computationalspeed.

Computer Vision

Image and Video Processing

Neural and Evolutionary Computing

NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector

Vertebrate retinas are highly-efficient in processing trivial visual tasks such as detecting moving objects, yet a complex task for modern computers. The detection of object motion is done by speciali

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HybridSVD: When Collaborative Information is Not Enough

Information Retrieval

Stats Machine Learning

We propose a new hybrid algorithm that allows incorporating both user and item side information within the standard collaborative filtering technique. One of its key features is that it naturally exte

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Emerging Technologies

EESS Systems and Control

Quadratic and Higher-Order Unconstrained Binary Optimization of Railway Dispatching Problem for Quantum Computing

The consequences of disruptions in railway traffic are the primary cause of passengers' dissatisfaction. Hence, appropriate dispatching decisions are necessary (e.g., by assigning the order of trains)

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The Algorithm of Shor for Prime Factorization

Continued Fractions and Probability Estimations in the Shor Algorithm -- A Detailed and Self-Contained Treatise

We present the relevant results and proofs from the theory of continued fractions in detail (even in more detail than in text books) filling the gap to allow a complete comprehension of the algorithm of shor for prime factorization.

Strongly Correlated Electrons

Hybrid quantum--classical algorithm for computing imaginary-time correlation functions

Quantitative descriptions of strongly correlated materials pose a considerable challenge in condensed matter physics and chemistry. A promising approach to address this problem is quantum embedding me

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Neural and Evolutionary Computing

Towards an Optimal Hybrid Algorithm for EV Charging Stations Placement using Quantum Annealing and Genetic Algorithms

Quantum Annealing is a heuristic for solving optimization problems that have seen a recent surge in usage owing to the success of D-Wave Systems. This paper aims to find a good heuristic for solving t

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Disordered Systems and Neural Networks

Statistical Mechanics

Quantum Annealers for Low-Temperature Boltzmann Monte Carlo Simulations

Computational Physics

Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning

We show how to exploit quantum annealers to accelerate equilibrium markov chain monte carlo simulations of spin glasses at low temperature.

In particular, we explore hybrid schemes by combining single spin-flip and neuralproposals, as well as classical and quantum annealer training data.

Emerging Technologies

Multi-car paint shop optimization with quantum annealing

We present a generalization of the binary paint shop problem (BPSP) to tackle an automotive industry application, the multi-car paint shop (MCPS) problem. The objective of the optimization is to minim

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Unsupervised Machine Learning on a Hybrid Quantum Computer

Machine learning techniques have led to broad adoption of a statistical model of computing. The statistical distributions natively available on quantum processors are a superset of those available cla

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Optimization and Control

A Hybrid Quantum-Classical Algorithm for Loan Collection

Hybrid Quantum-Classical Algorithms for Loan Collection Optimization with Loan Loss Provisions

We propose a hybrid quantum-classical algorithm to solve quadratic-constrained binary optimization models for loan collection optimization.

The objective is to find a set of optimal loan collection actions that maximizes the expected net profit presenteded to the bank as well as the financial welfare in the financial network of loanees, while keeping the loan loss provision at its minimum.

Strongly Correlated Electrons

Measurement-Based Time Evolution for Quantum Simulation of Fermionic Systems

Quantum simulation using time evolution in phase estimation-based quantum algorithms can yield unbiased solutions of classically intractable models. But long runtimes open such algorithms to decoheren

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