A systematic analysis of 59 quantum software testing empirical studies reveals highly diverse designs, inconsistent reporting, and open methodological challenges, leading to recommendations for future work.
Mosca, Quantum algorithms (2008), arXiv:0808.0369 [quant-ph]
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abstract
This article surveys the state of the art in quantum computer algorithms, including both black-box and non-black-box results. It is infeasible to detail all the known quantum algorithms, so a representative sample is given. This includes a summary of the early quantum algorithms, a description of the Abelian Hidden Subgroup algorithms (including Shor's factoring and discrete logarithm algorithms), quantum searching and amplitude amplification, quantum algorithms for simulating quantum mechanical systems, several non-trivial generalizations of the Abelian Hidden Subgroup Problem (and related techniques), the quantum walk paradigm for quantum algorithms, the paradigm of adiabatic algorithms, a family of ``topological'' algorithms, and algorithms for quantum tasks which cannot be done by a classical computer, followed by a discussion.
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A Methodological Analysis of Empirical Studies in Quantum Software Testing
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AQUIRE is the first error-aware adaptive Bayesian protocol for simultaneously estimating the mean and error of observables on qudit quantum computers using generalized Pauli operators and overlap grouping.
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Machine learning methods in quantum computing theory
Authors present a multiclass tree tensor network algorithm demonstrated on IBM quantum processor and a neural network approach for noise-robust quantum state tomography.