Quantum algorithms for grid-based variational time evolution

Ollitrault, Pauline J.; Jandura, Sven; Miessen, Alexander; Burghardt, Irène; Martinazzo, Rocco; Tacchino, Francesco; Tavernelli, Ivano

doi:10.48550/arxiv.2203.02521

Cited by 6 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, we investigate the prospects for accelerating chemical dynamics simulation on early fault-tolerant quantum computers using the first-quantized, real-space grid approach (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17). By "early," we mean machines that have a limited number of error-corrected qubits, as we presently explain.…”

Section: Introductionmentioning

confidence: 99%

Grid-based methods for chemistry simulations on a quantum computer

et al. 2023

View full text Add to dashboard Cite

First-quantized, grid-based methods for chemistry modeling are a natural and elegant fit for quantum computers. However, it is infeasible to use today’s quantum prototypes to explore the power of this approach because it requires a substantial number of near-perfect qubits. Here, we use exactly emulated quantum computers with up to 36 qubits to execute deep yet resource-frugal algorithms that model 2D and 3D atoms with single and paired particles. A range of tasks is explored, from ground state preparation and energy estimation to the dynamics of scattering and ionization; we evaluate various methods within the split-operator QFT (SO-QFT) Hamiltonian simulation paradigm, including protocols previously described in theoretical papers and our own techniques. While we identify certain restrictions and caveats, generally, the grid-based method is found to perform very well; our results are consistent with the view that first-quantized paradigms will be dominant from the early fault-tolerant quantum computing era onward.

show abstract

Section: Introductionmentioning

confidence: 99%

Grid-based methods for chemistry simulations on a quantum computer

et al. 2023

View full text Add to dashboard Cite

show abstract

“…There is thus a large interest in short-depth noiseresilient algorithms such as the variational quantum algorithm (VQA) [1]. VQAs can be applied to quantum chemistry [2][3][4][5][6], machine learning [7][8][9] and optimization [10,11] tasks. In a VQA, the expectation value ψ(θ)|O|ψ(θ) of an observable O is optimized by varying the parameters θ of a trial variational state |ψ(θ) .…”

Section: Introductionmentioning

confidence: 99%

A study of the pulse-based variational quantum eigensolver on cross-resonance based hardware

Egger¹,

Capecci²,

Pokharel³

et al. 2023

Preprint

View full text Add to dashboard Cite

“…Efficient encoding schemes that reduce the gate complexity while preserving the accuracy of the simulation will be instrumental in making potential energy simulations accessible on near-term quantum hardware [6,16,17]. By mitigating the computational challenges, these advancements will pave the way for furthering our understanding of atomic behaviour and unlocking the full potential of quantum technologies in various scientific applications [10,18].…”

Section: Introductionmentioning

confidence: 99%

“…State-of-the-art literature has highlighted the potential of machine learning techniques, such as neural network potential energy surfaces, to efficiently approximate complex potential energy landscapes [9]. Furthermore, quantum algorithms have been developed for grid-based variational time evolution and threshold gatebased quantum simulation, enabling accurate simulations of quantum systems with reduced computational resources [10][11][12][13][14]. The classical approach to solving the time evolution problem has been to consider a linear response for small time periods.…”

Section: Introductionmentioning

confidence: 99%