An algorithmic benchmark for quantum information processing

Christopher Granade, Christopher Ferrie, David G. Cory

Quantum information processing offers promising advances for a wide range of fields and applications, provided that we can efficiently assess the performance of the control applied in candidate systems. That is, we must be able to determine whether we have implemented a desired gate, and refine accordingly. Randomized benchmarking reduces the difficulty of this task by exploiting symmetries in quantum operations. Here, we bound the resources required for benchmarking and show that, with prior information, we can achieve several orders of magnitude better accuracy than in traditional approaches to benchmarking. Moreover, by building on state-of-the-art classical algorithms, we reach these accuracies with near-optimal resources. Our approach requires an order of magnitude less data to achieve the same accuracies and to provide online estimates of the errors in the reported fidelities. We also show that our approach is useful for physical devices by comparing to simulations.

Colleen Farrelly, Uchenna Chukwu

Jincheng Zhang

This paper investigates a novel geometric information retrieval algorithm based on self-organized quantum states and entanglement. Traditional information retrieval methods often struggle with complex geometric datasets, necessitating significant manual effort. This research proposes a quantum geometry information retrieval algorithm that leverages quantum states and entanglement to significantly enhance the efficiency and accuracy of information retrieval, particularly for complex geometric data. The algorithm's core mechanism centers around a dynamic self-organization process within quantum states, allowing for more nuanced and efficient exploration of data space. We present a preliminary implementation and analyze the performance of the algorithm on a benchmark dataset, demonstrating its potential to outperform existing methods.

Robson Giles

Timothy J Proctor, Kevin C Young, Andrew Baczewski +1 more

Liu Xiu-Mei, Luo Jun, Xianping Sun

We report the first experimental demonstration of a nuclear magnetic resonance (NMR) realization of iterative phase estimation algorithms. Using feedback and iterations, we experimentally obtain the phase with 6 bits of precision on a two-qubit NMR quantum computer. Furthermore, we experimentally demonstrate the effect of gate noise on the iterative phase estimation algorithm. Our experimental results show that errors of measurements of the phase depend strongly on the precision of coupling gates. This experiment can be used as a benchmark for multi-qubit realizations of quantum information processing and precision measurements.