Discrete optimization: Limitations of existing quantum algorithms

Sergio Boixo, Rolando D. Somma

Boris Kryzhanovsky, M. V. Kryzhanovsky, M. Yu. Malsagov

The capability of discretization of matrix elements in the problem of quadratic functional minimization with linear member built on matrix in N-dimensional configuration space with discrete coordinates is researched. It is shown, that optimal procedure of replacement matrix elements by the integer quantities with the limited number of gradations exist, and the efficient of minimization does not reduce. Parameter depends on matrix properties, which allows estimate the capability of using described procedure for given type of matrix, is found. Computational complexities of algorithm and RAM requirements are reduced by 16 times, correct using of integer elements allows increase minimization algorithm speed by the orders.

Stefan Creemers, Luis Fernando Pérez Armas

Hartmut Neven, Vasil S. Denchev, Geordie Rose +1 more

We introduce a novel discrete optimization method for training in the context of a boosting framework for large scale binary classifiers. The motivation is to cast the training problem into the format required by existing adiabatic quantum hardware. First we provide theoretical arguments concerning the transformation of an originally continuous optimization problem into one with discrete variables of low bit depth. Next we propose QBoost as an iterative training algorithm in which a subset of weak classifiers is selected by solving a hard optimization problem in each iteration. A strong classifier is incrementally constructed by concatenating the subsets of weak classifiers. We supplement the findings with experiments on one synthetic and two natural data sets and compare against the performance of existing boosting algorithms. Finally, by conducting a quantum Monte Carlo simulation we gather evidence that adiabatic quantum optimization is able to handle the discrete optimization problems generated by QBoost.

Stefan Creemers, Luis Fernando Pérez Armas

Martin Ekerå, Joel Gärtner

We provide a high-level cost comparison between Regev's quantum algorithm with Ekerå–Gärtner's extensions on the one hand, and existing state-of-the-art quantum algorithms for factoring and computing discrete logarithms on the other. This when targeting cryptographically relevant problem instances, and when accounting for the space-saving optimizations of Ragavan and Vaikuntanathan that apply to Regev's algorithm, and optimizations such as windowing that apply to the existing algorithms. Our conclusion is that Regev's algorithm without the space-saving optimizations may achieve a per-run advantage, but not an overall advantage, if non-computational quantum memory is cheap. Regev's algorithm with the space-saving optimizations does not achieve an advantage, since it uses more computational memory, whilst also performing more work, per run and overall, compared to the existing state-of-the-art algorithms. As such, further optimizations are required for it to achieve an advantage for cryptographically relevant problem instances.