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arXiv:2304.06492

Effect of pure dephasing quantum noise in the quantum search algorithm: an undergraduate approach using Atos Quantum Assembly

Maria Heloísa Fraga da Silva, Gleydson Fernandes de Jesus, Clebson Cruz

Published Jan 17, 2023• 0 citations
arXivHTMLPDF

Abstract

Quantum computing is tipped to lead the future of global technological progress. However, the obstacles related to quantum software development are an actual challenge to overcome. In particular, there is a discrepant lack of trained and skilled workforce to implement new quantum software on the recent quantum hardware. In this scenario, this work presents a didactic implementation of the quantum search algorithm in Atos Quantum Assembly Language (AQASM). In addition, we present the creation of a virtual quantum processor whose configurable architecture allows the analysis of induced quantum noise effects on the quantum algorithms. The codes are available throughout the manuscript so that readers, even those with little scientific computing experience, can replicate them and apply the methods discussed in this article to solve their own quantum computing projects. The presented results are consistent with theoretical predictions and demonstrate that AQASM and myQLM are powerful tools for teaching quantum computing and building, implementing, and simulating quantum hardware.

DOI
10.48550/arxiv.2304.06492

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Quantum computing is tipped to lead the future of global technological progress. However, the obstacles related to quantum software development are an actual challenge to overcome. In particular, there is a discrepant lack of trained and skilled workforce to implement new quantum software on the recent quantum hardware. In this scenario, this work presents a didactic implementation of the quantum search algorithm in Atos Quantum Assembly Language (AQASM). In addition, we present the creation of a virtual quantum processor whose configurable architecture allows the analysis of induced quantum noise effects on the quantum algorithms. The codes are available throughout the manuscript so that readers, even those with little scientific computing experience, can replicate them and apply the methods discussed in this article to solve their own quantum computing projects. The presented results are consistent with theoretical predictions and demonstrate that AQASM and myQLM are powerful tools for teaching quantum computing and building, implementing, and simulating quantum hardware.
Quantum computing is tipped to lead the future of global technological progress. However, the obstacles related to quantum software development are an actual challenge to overcome. In this scenario, this work presents an implementation of the quantum search algorithm in Atos Quantum Assembly Language (AQASM) using the quantum software stack my Quantum Learning Machine (myQLM) and the programming development platform Quantum Learning Machine (QLM). We present the creation of a virtual quantum processor whose configurable architecture allows the analysis of induced quantum noise effects on the quantum algorithms. The codes are available throughout the manuscript so that readers can replicate them and apply the methods discussed in this article to solve their own quantum computing projects. The presented results are consistent with theoretical predictions and demonstrate that AQASM and QLM are powerful tools for building, implementing, and simulating quantum hardware.
In this paper, we present the Quantum Information Software Developer Kit - Qiskit, for teaching quantum computing to undergraduate students, with basic knowledge of quantum mechanics postulates. We focus on presenting the construction of the programs on any common laptop or desktop computer and their execution on real quantum processors through the remote access to the quantum hardware available on the IBM Quantum Experience platform. The codes are made available throughout the text so that readers, even with little experience in scientific computing, can reproduce them and adopt the methods discussed in this paper to address their own quantum computing projects. The results presented are in agreement with theoretical predictions and show the effectiveness of the Qiskit package as a robust classroom working tool for the introduction of applied concepts of quantum computing and quantum information theory.
Abstract Advancing quantum technologies necessitates an in-depth exploration of how operations generate quantum resources and respond to noise. Crucial are gates generating quantum coherence and the challenge of mitigating gate dephasing noise. Precisely, we study the dephasing noise that reduces the coherence-generating power of quantum gates, its simulation, and critical factors. Our primary contribution lies in a theorem characterizing the full set of dephasing noises in gates, adaptable to simulation by any predefined operation set. In particular, we apply our result to quantify the memory adaptability required for a dephasing noise to arise. Furthermore, we analytically calculate the quantifier for gates acting on qubit systems, thereby fully characterizing this scenario. Next, we show how our results reveal the structure of non-trivial dephasing noise affecting qubit gates and apply them to experimental data, conclusively demonstrating the existence of a gate’s dephasing noise, which is irreducible to dephasing of either input or output states. Finally, we show how our study contributes to addressing an open question in the resource theory of coherence generation.
Abstract Quantum chemistry (QC) is one of the most promising applications of quantum computing. However, present quantum processing units (QPUs) are still subject to large errors. Therefore, noisy intermediate‐scale quantum (NISQ) hardware is limited in terms of qubit counts/circuit depths. Variational quantum eigensolver (VQE) algorithms can potentially overcome such issues. Here, we introduce the OpenVQE open‐source QC package. It provides tools for using and developing chemically‐inspired adaptive methods derived from unitary coupled cluster (UCC). It facilitates the development and testing of VQE algorithms and is able to use the Atos Quantum Learning Machine (QLM), a general quantum programming framework enabling to write/optimize/simulate quantum computing programs. We present a specific, freely available QLM open‐source module, myQLM‐fermion. We review its key tools for facilitating QC computations (fermionic second quantization, fermion‐spin transforms, etc.). OpenVQE largely extends the QLM's QC capabilities by providing: (i) the functions to generate the different types of excitations beyond the commonly used UCCSD ansatz; (ii) a new Python implementation of the “adaptive derivative assembled pseudo‐Trotter method” (ADAPT‐VQE). Interoperability with other major quantum programming frameworks is ensured thanks to the myQLM‐interop package, which allows users to build their own code and easily execute it on existing QPUs. The combined OpenVQE/myQLM‐fermion libraries facilitate the implementation, testing and development of variational quantum algorithms, while offering access to large molecules as the noiseless Schrödinger‐style dense simulator can reach up to 41 qubits for any circuit. Extensive benchmarks are provided for molecules associated to qubit counts ranging from 4 to 24. We focus on reaching chemical accuracy, reducing the number of circuit gates and optimizing parameters and operators between “fixed‐length” UCC and ADAPT‐VQE ansätze. This article is categorized under: Software > Quantum Chemistry Quantum Computing > Algorithms
One of the most exciting potential applications of a quantum computer is the abilityto efficiently simulate quantum systems, a task that is out of the reach of even thelargest classical supercomputers. Such simulations require a quantum algorithm capableof efficiently representing and manipulating a quantum system, as well as a device withsufficient coherence to execute it. In this work, we describe experiments advancing bothof these goals. First, we discuss dephasing—currently a leading cause of decoherencein superconducting qubits—and present measurements accurately quantifying both low-and high-frequency phase noise sources. We then discuss two quantum algorithms forthe simulation of chemical Hamiltonians, and experimentally contrast their performance.These results show that with continuing improvement in quantum devices we may soonbe able to apply quantum computers to practical chemistry problems.