Pablo Antonio Moreno Casares | Universidad Complutense de Madrid (Facultad de Psicología) (original) (raw)
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Papers by Pablo Antonio Moreno Casares
We develop quantum computational tools to predict how proteins fold in 3D, one of the most import... more We develop quantum computational tools to predict how proteins fold in 3D, one of the most important problems in current biochemical research. We explain how to combine recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The result, QFold, is a fully scalable hybrid quantum algorithm that in contrast to previous quantum approaches does not require a lattice model simplification and instead relies on the much more realistic assumption of parameterization in terms of torsion angles of the amino acids. We compare it with its classical analog for different annealing schedules and find a polynomial quantum advantage, and validate a proof-of-concept realization of the quantum Metropolis in IBMQ Casablanca quantum processor.
arXiv: General Relativity and Quantum Cosmology, 2018
The aim of this dissertation is to review `Loop Quantum Gravity', explaining the main structu... more The aim of this dissertation is to review `Loop Quantum Gravity', explaining the main structure of the theory and indicating its main open issues. We will develop the two main lines of research for the theory: the canonical quantization (first two chapters) and spin foams (third). The final chapter will be devoted to studying some of the problems of the theory and what things remain to be developed. In chapter 3 we will also include an example of a simple calculation done in the frame of LQG: Schwarzschild black hole entropy.
arXiv: Quantum Physics, 2020
In this short review I aim to explain how we can construct a circuit implementation of the bucket... more In this short review I aim to explain how we can construct a circuit implementation of the bucketbrigade qRAM first proposed in [1]. Used with classical data, this qRAM model can be used incombination with the quantum accessible data structure [2] to prepare arbitrary quantum statesquickly and repeatedly, once the data to be prepared is in memory
We develop quantum computational tools to predict how proteins fold in 3D, one of the most import... more We develop quantum computational tools to predict how proteins fold in 3D, one of the most important problems in current biochemical research. We explain how to combine recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The result, QFold, is a fully scalable hybrid quantum algorithm that in contrast to previous quantum approaches does not require a lattice model simplification and instead relies on the much more realistic assumption of parameterization in terms of torsion angles of the amino acids. We compare it with its classical analog for different annealing schedules and find a polynomial quantum advantage, and validate a proof-of-concept realization of the quantum Metropolis in IBMQ Casablanca quantum processor.
arXiv: General Relativity and Quantum Cosmology, 2018
The aim of this dissertation is to review `Loop Quantum Gravity', explaining the main structu... more The aim of this dissertation is to review `Loop Quantum Gravity', explaining the main structure of the theory and indicating its main open issues. We will develop the two main lines of research for the theory: the canonical quantization (first two chapters) and spin foams (third). The final chapter will be devoted to studying some of the problems of the theory and what things remain to be developed. In chapter 3 we will also include an example of a simple calculation done in the frame of LQG: Schwarzschild black hole entropy.
arXiv: Quantum Physics, 2020
In this short review I aim to explain how we can construct a circuit implementation of the bucket... more In this short review I aim to explain how we can construct a circuit implementation of the bucketbrigade qRAM first proposed in [1]. Used with classical data, this qRAM model can be used incombination with the quantum accessible data structure [2] to prepare arbitrary quantum statesquickly and repeatedly, once the data to be prepared is in memory