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Open source variational quantum eigensolver extension of the quantum learning machine for quantum chemistry

Authors: Haidar, Mohammad; Rančić, Marko J.; Ayral, Thomas; Maday, Yvon; Piquemal, Jean-Philip;

Open source variational quantum eigensolver extension of the quantum learning machine for quantum chemistry

Abstract

AbstractQuantum 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

Keywords

Chemical Physics (physics.chem-ph), Quantum Physics, Chemical Physics, FOS: Physical sciences, Quantum computing, [INFO] Computer Science [cs], [CHIM.THEO] Chemical Sciences/Theoretical and/or physical chemistry, Quantum chemistry computation, Unitary coupled cluster, Coupled cluster theory, Quantum Learning Machine, Variational quantum eigensolver, Quantum Physics (quant-ph), [PHYS.QPHY] Physics [physics]/Quantum Physics [quant-ph]

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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