4 citations · 4 across the 5 of their papers we have counts for
5 papers
From quantum to quantum-inspired: the LogQ algorithm as a non-linear continuous relaxation of variables method
Jérémie Messud, Yagnik Chatterjee
The LogQ algorithm encodes Quadratic Unconstrained Binary Optimization (QUBO) problems, which are often encountered in the industry (portfolio optimization, fleet optimization, cha…
Unified Approach to Portfolio Optimization using the `Gain Probability Density Function' and Applications
Jean-Patrick Mascomère, Jérémie Messud, Yagnik Chatterjee +1
This article proposes a unified framework for portfolio optimization (PO), recognizing an object called the `gain probability density function (PDF)' as the fundamental object of t…
Towards solving large QUBO problems using quantum algorithms: improving the LogQ scheme
Yagnik Chatterjee, Jérémie Messud
The LogQ algorithm encodes Quadratic Unconstrained Binary Optimization (QUBO) problems with exponentially fewer qubits than the Quantum Approximate Optimization Algorithm (QAOA). T…
A Hybrid Quantum-assisted Column Generation Algorithm for the Fleet Conversion Problem
Yagnik Chatterjee, Zaid Allybokus, Marko J. Rančić +1
The problem of Fleet Conversion aims to reduce the carbon emissions and cost of operating a fleet of vehicles for a given set of tours. It can be modelled as a column generation sc…
Solving various NP-Hard problems using exponentially fewer qubits on a Quantum Computer
Yagnik Chatterjee, Eric Bourreau, Marko J. Rančić
NP-hard problems are not believed to be exactly solvable through general polynomial time algorithms. Hybrid quantum-classical algorithms to address such combinatorial problems have…