### Quantum-computing related developments

On this page we post about interesting quantum-computing related research and news which we are following.

On this page we post about interesting quantum-computing related research and news which we are following.

July 14, 2020

- Source: arXiv

Various heuristic quantum optimization approaches have been suggested to solve combinatorial optimization problems, since the NP hardness of such problems makes heuristics the only viable option for many problems that need to be routinely solved in real-world applications. Usually the performance of the heuristic approach is examined; however, an equally important aspect is their application to current hardware. During the NISQ era, hardware with only a limited number of qubits is available and quantum error correction cannot be fully exploited. Furthermore, the error rates of the quantum circuitry is still high (10^{-3} – 10^{-4}), which makes the computation inefficient.

This paper reviews existing approaches and develop new methods or improvements of many prominent approaches to combinatorial optimization on a small quantum computer by compiling circuits that implement them and optimizing their fault-tolerant realizations. Many of these methods are bottlenecked by calls to the same subroutines; thus, optimized circuits for those primitives should be of interest regardless of which heuristic is most effective in practice. In essentially all heuristic approaches to quantum optimization there is a primitive that is repeated many times in order to perform the optimization. Instead of investigating how many times those primitives must be repeated, one should focus on the best strategies for realizing those primitives within a fault-tolerant cost model. The goal of this paper is to estimate the performance of an early universal quantum computer for key steps of combinatorial optimization.

These bottlenecks are compiled for several families of optimization problems and it is reported for how long and for what size systems one can perform these heuristics in the surface code given a range of resource budgets. The obtained results discourage the notion that any quantum optimization heuristic realizing only a quadratic speedup will achieve an advantage over classical algorithms on modest superconducting qubit surface code processors without significant improvements in the implementation of the surface code. The essential reason for this is the substantial constant factor slowdown between error-corrected quantum computation and classical computation. Based on these results, we will either need quantum optimization algorithms that afford speedups which are much better than quadratic, or we will need significant improvements in the way that we realize error-correction.

For example, the authors calculate that to implement problems between N = 64 and N = 1024, hundreds of thousands of physical qubits are required when physical gate error rates are on the order of 10^{-4} and sometimes over a million are required for physical gate error rates on the order of 10^{-3}. Even more concerning is that the number of updates achieved in a day (given realistic cycle times for the error correcting codes) is relatively low, on the order of about ten thousand updates for the smallest instances considered of the cheapest cost functions. With such overheads, these heuristics would need to yield dramatically better improvements in the objective function per step than classical optimization heuristics. Therefore, barring significant advances in the implementation of the surface code (e.g., much faster state distillation), quantum optimization algorithms offering only a quadratic speedup are unlikely to produce any quantum advantage on the first few generations of superconducting qubit surface code processors.

June 27, 2020

- Source: arXiv

One hallmark problem in computational linear algebra is the binary linear least squares (BLLS), which is formally in the NP-Hard complexity class. Efficient classical methods for solving this problem exists with limited approximations to the solution. Quantum computing may solve these problems with a better approximation ratio and/or in a faster runtime scaling. So-far, this problem has only been considered on a quantum annealing by mapping it to a QUBO. In this paper, the problem is solved using a QAOA approach on the gate-based model of quantum computing. The performance is assessed both on a wavefunction simulator, shotnoise simulator and on the 5-qubit IBM cloud computing quantum device ibmq_london. As an outlook: BLLS may serve as a building block for other problems such as Non-negative Binary Matrix Factorization, or clubbed together for a fixed-point approximation of real variables. This paper was partially supervised by Vincent Elfving from Qu & Co.