Quantum computation - alternative models and algorithms

Nicolas Bui, Alain Reineix, Christophe Guiffaut

Numerical simulation may benefits from quantum computing (QC) potential to solve particular problems faster than its classical counterpart. This motivates recent works [1, 2, 3] to solve the wave equation with a quantum computer. Here, we consider 2D wave propagation in free space based on an alternative implementation of the quantum algorithm developed in [2, 3]. We also propose a way to model quasi perfect electrical conductor (PEC). An example of a plane wave reflection is given, with QC on a simulator.

Chien‐Hung Cho, Chih-Yu Chen, Kuo-Chin Chen +6 more

William J. Zeng

We construct abstract models of blackbox quantum algorithms using a model of quantum computation in sets and relations, a setting that is usually considered for nondeterministic classical computation. This alternative model of quantum computation (QCRel), though unphysical, nevertheless faithfully models its computational structure. Our main results are models of the Deutsch-Jozsa, single-shot Grovers, and GroupHomID algorithms in QCRel. These results provide new tools to analyze the semantics of quantum computation and improve our understanding of the relationship between computational speedups and the structure of physical theories. They also exemplify a method of extending physical/computational intuition into new mathematical settings.

A. K. Ekert

Mahn‐Soo Choi

Ufuk Korkmaz, Deniz Türkpençe

Today, the competition to build a quantum computer continues, and the number of qubits in hardware is increasing rapidly. However, the quantum noise that comes with this process reduces the performance of algorithmic applications, so alternative ways in quantum computer architecture and implementation of algorithms are discussed on the one hand. One of these alternative ways is the hybridization of the circuit-based quantum computing model with the dissipative-based computing model. Here, the goal is to apply the part of the algorithm that provides the quantum advantage with the quantum circuit model, and the remaining part with the dissipative model, which is less affected by noise. This scheme is of importance to quantum machine learning algorithms that involve highly repetitive processes and are thus susceptible to noise. In this study, we examine dissipative information transfer to a qubit model called Cat-Qubit. This model is especially important for the dissipative-based version of the binary quantum classification, which is the basic processing unit of quantum machine learning algorithms. On the other hand, Cat-Qubit architecture, which has the potential to easily implement activation-like functions in artificial neural networks due to its rich physics, also offers an alternative hardware opportunity for quantum artificial neural networks. Numerical calculations exhibit successful transfer of quantum information from reservoir qubits by a repeated-interactions-based dissipative scheme.