A model of discrete quantum computation

Tom Wollschläger, Aman Saxena, Nicola Franco +2 more

Breakthroughs in machine learning (ML) and advances in quantum computing (QC) drive the interdisciplinary field of quantum machine learning to new levels. However, due to the susceptibility of ML models to adversarial attacks, practical use raises safety-critical concerns. Existing Randomized Smoothing (RS) certification methods for classical machine learning models are computationally intensive. In this paper, we propose the combination of QC and the concept of discrete randomized smoothing to speed up the stochastic certification of ML models for discrete data. We show how to encode all the perturbations of the input binary data in superposition and use Quantum Amplitude Estimation (QAE) to obtain a quadratic reduction in the number of calls to the model that are required compared to traditional randomized smoothing techniques. In addition, we propose a new binary threat model to allow for an extensive evaluation of our approach on images, graphs, and text.

Mingsheng Ying

Jesús García López de Lacalle

The main challenge we face in making quantum computing a reality is error control. For this reason it is necessary to study whether the hypotheses on which the threshold theorem has been proved capture all the characteristics of quantum errors. The extraordinary difficulties that we find to control quantum errors effectively together with the little progress in this endeavor, compared to the enormous effort deployed by the scientific community and by companies and governments, should make us reflect on the road map to quantum computing. In this work we analyze error control in quantum computing and suggest that discrete quantum computing models should be explored. In this sense, we present a concrete model but, above all, we propose that Quantum Physics should be taken one step further, in order to allow discretization of the quantum computing model.

Peter P. Rohde

Tetsuro Nishino

Sophie Choe

In this Near Intermediate-Scale Quantum era, there are two types of near-term quantum devices available on cloud: superconducting quantum processing units (QPUs) based on the discrete variable model and linear optics (photonics) QPUs based on the continuous variable (CV) model. Quantum computation in the discrete variable model is performed in a finite dimensional quantum state space and the CV model in an infinite dimensional space. In implementing quantum algorithms, the CV model offers more quantum gates that are not available in the discrete variable model. CV-based photonic quantum computers provide additional flexibility of controlling the length of the output vectors of quantum circuits, using different methods of measurement and the notion of cutoff dimension.