S. A. Grillo | Searcharxiv

arXiv2018

Fourier 1-norm and quantum speed-up

Understanding quantum speed-up over classical computing is fundamental for the development of efficient quantum algorithms. In this paper, we study such problem within the framework of the Quantum Query Model, which represents the probability of output $x \in \{0,1\}^n$ as a function $π(x)$. We present a classical simulation for output probabilities $π$, whose error depends on the Fourier $1$-norm of $π$. Such dependence implies upper-bounds for the quotient between the number of queries applied by an optimal classical algorithm and our quantum algorithm, respectively. These upper-bounds show a strong relation between Fourier $1$-norm and quantum parallelism. We show applications to query complexity.

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arXiv2016

Quantum Query as a State Decomposition

The Quantum Query Model is a framework that allows us to express most known quantum algorithms. Algorithms represented by this model consist on a set of unitary operators acting over a finite Hilbert space, and a final measurement step consisting on a set of projectors. In this work, we prove that the application of these unitary operators before the measurement step is equivalent to decomposing a unit vector into a sum of vectors and then inverting some of their relative phases. We also prove that the vectors of that sum must fulfill a list of properties and we call such vectors a Block Set. If we define the measurement step for the Block Set Formulation similarly to the Quantum Query Model, then we prove that both formulations give the same Gram matrix of output states, although the Block Set Formulation allows a much more explicit form. Therefore, the Block Set reformulation of the Quantum Query Model gives us an alternative interpretation on how quantum algorithms works. Finally, we apply our approach to the analysis and complexity of quantum exact algorithms.

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