Synergistic pretraining of parametrized quantum circuits via tensor networks

Rudolph, Manuel S.; Miller, Jacob; Motlagh, Danial; Chen, Jing; Acharya, Atithi; Perdomo-Ortiz, Alejandro

doi:10.1038/s41467-023-43908-6

Cited by 18 publications

(1 citation statement)

References 55 publications

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“…Several approaches to limit or avoid barren plateaus have been proposed and applied to specific tasks [52][53][54][55][56][57][58]. At the same time, developing smart initializations [59] or iterative optimization schemes [60] may prove essential to successfully train a VQA.…”

Section: Introductionmentioning

confidence: 99%

Beyond quantum annealing: optimal control solutions to maxcut problems

Pecci,

Wang,

Torta

et al. 2024

Quantum Sci. Technol.

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Quantum Annealing (QA) relies on mixing two Hamiltonian terms, a simple driver and a complexproblem Hamiltonian, in a linear combination. The time-dependent schedule for this mixing is oftentaken to be linear in time: improving on this linear choice is known to be essential and has provento be difficult. Here, we present different techniques for improving on the linear-schedule QA alongtwo directions, conceptually distinct but leading to similar outcomes: 1) the first approach consistsof constructing a Trotter-digitized QA (dQA) with schedules parameterized in terms of Fouriermodes or Chebyshev polynomials, inspired by the Chopped Random Basis algorithm (CRAB) foroptimal control in continuous time; 2) the second approach is technically a Quantum ApproximateOptimization Algorithm (QAOA), whose solutions are found iteratively using linear interpolationor expansion in Fourier modes. Both approaches emphasize finding smooth optimal schedule pa-rameters, ultimately leading to hybrid quantum-classical variational algorithms of the alternatingHamiltonian Ansatz type. We apply these techniques to MaxCut problems on weighted 3-regulargraphs with N = 14 sites, focusing on hard instances that exhibit a small spectral gap, for whicha standard linear-schedule QA performs poorly. We characterize the physics behind the optimalprotocols for both the dQA and QAOA approaches, discovering shortcuts to adiabaticity-like dy-namics. Furthermore, we study the transferability of such smooth solutions among hard instances ofMaxCut at different circuit depths. Finally, we show that the smoothness pattern of these protocolsobtained in a digital setting enables us to adapt them to continuous-time evolution, contrarily togeneric non-smooth solutions. This procedure results in an optimized quantum annealing schedulethat is implementable on analog devices.

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Section: Introductionmentioning

confidence: 99%

Beyond quantum annealing: optimal control solutions to maxcut problems

Pecci,

Wang,

Torta

et al. 2024

Quantum Sci. Technol.

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Advancements in Quantum Computing—Viewpoint: Building Adoption and Competency in Industry

Pfaendler,

Konson,

Greinert

2024

Datenbank Spektrum

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The narrative around quantum computing is evolving quickly. First reports of quantum computers able to solve certain scientific problems on-par with the precision of High-Performance Computers are persuading end-users and industry leaders to shift from passive observation to active exploration. Insights are provided here to enable organization and technology leaders in the database and data science community an entry into the field of quantum computing. This article provides an introduction of key concepts, insights into the rapid advancement of the state-of-the-art, an overview of how companies in Germany are approaching the development of competency and adoption of quantum computing, including an overview of European activities and learning materials in the German language.

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Parallel Implementation of the Density Matrix Renormalization Group Method Achieving a Quarter petaFLOPS Performance on a Single DGX-H100 GPU Node

Menczer,

van Damme,

Rask

et al. 2024

J. Chem. Theory Comput.

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We report cutting edge performance results on a single node hybrid CPU-multi-GPU implementation of the spin adapted ab initio Density Matrix Renormalization Group (DMRG) method on current state-of-the-art NVIDIA DGX-H100 architectures. We evaluate the performance of the DMRG electronic structure calculations for the active compounds of the FeMoco, the primary cofactor of nitrogenase, and cytochrome P450 (CYP) enzymes with complete active space (CAS) sizes of up to 113 electrons in 76 orbitals [CAS(113,76)] and 63 electrons in 58 orbitals [CAS(63, 58)], respectively. We achieve 246 teraFLOPS of sustained performance, an improvement of more than 2.5× compared to the performance achieved on the DGX-A100 architectures and an 80× acceleration compared to an OpenMP parallelized implementation on a 128-core CPU architecture. Our work highlights the ability of tensor network algorithms to efficiently utilize high-performance multi-GPU hardware and shows that the combination of tensor networks with modern large-scale GPU accelerators can pave the way toward solving some of the most challenging problems in quantum chemistry and beyond.

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Synergistic pretraining of parametrized quantum circuits via tensor networks

Cited by 18 publications

References 55 publications

Beyond quantum annealing: optimal control solutions to maxcut problems

Beyond quantum annealing: optimal control solutions to maxcut problems

Advancements in Quantum Computing—Viewpoint: Building Adoption and Competency in Industry

Parallel Implementation of the Density Matrix Renormalization Group Method Achieving a Quarter petaFLOPS Performance on a Single DGX-H100 GPU Node

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