A large ongoing research effort focuses on variational quantum algorithms (VQAs), representing leading candidates to achieve computational speed-ups on current quantum devices. The scalability of VQAs to a large number of qubits, beyond the simulation capabilities of classical computers, is still debated. Two major hurdles are the proliferation of low-quality variational local minima, and the exponential vanishing of gradients in the cost-function landscape, a phenomenon referred to as barren plateaus. In this work, we show that by employing iterative search schemes, one can effectively prepare the ground state of paradigmatic quantum many-body models, also circumventing the barren plateau phenomenon. This is accomplished by leveraging the transferability to larger system sizes of a class of iterative solutions, displaying an intrinsic smoothness of the variational parameters, a result that does not extend to other solutions found via random-start local optimization. Our scheme could be directly tested on near-term quantum devices, running a refinement optimization in a favorable local landscape with nonvanishing gradients.

Avoiding barren plateaus via transferability of smooth solutions in a Hamiltonian variational ansatz / Mele, Antonio A.; Mbeng, Glen B.; Santoro, Giuseppe E.; Collura, Mario; Torta, Pietro. - In: PHYSICAL REVIEW A. - ISSN 2469-9926. - 106:6(2022), pp. 1-7. [10.1103/PHYSREVA.106.L060401]

Avoiding barren plateaus via transferability of smooth solutions in a Hamiltonian variational ansatz

Mele, Antonio A.
Membro del Collaboration group
;
Mbeng, Glen B.
Membro del Collaboration group
;
Santoro, Giuseppe E.
Membro del Collaboration group
;
Collura, Mario
Membro del Collaboration group
;
Torta, Pietro
Membro del Collaboration group
2022-01-01

Abstract

A large ongoing research effort focuses on variational quantum algorithms (VQAs), representing leading candidates to achieve computational speed-ups on current quantum devices. The scalability of VQAs to a large number of qubits, beyond the simulation capabilities of classical computers, is still debated. Two major hurdles are the proliferation of low-quality variational local minima, and the exponential vanishing of gradients in the cost-function landscape, a phenomenon referred to as barren plateaus. In this work, we show that by employing iterative search schemes, one can effectively prepare the ground state of paradigmatic quantum many-body models, also circumventing the barren plateau phenomenon. This is accomplished by leveraging the transferability to larger system sizes of a class of iterative solutions, displaying an intrinsic smoothness of the variational parameters, a result that does not extend to other solutions found via random-start local optimization. Our scheme could be directly tested on near-term quantum devices, running a refinement optimization in a favorable local landscape with nonvanishing gradients.
2022
106
6
1
7
L060401
https://arxiv.org/abs/2206.01982
Mele, Antonio A.; Mbeng, Glen B.; Santoro, Giuseppe E.; Collura, Mario; Torta, Pietro
File in questo prodotto:
File Dimensione Formato  
Mele_PRA2022.pdf

non disponibili

Tipologia: Versione Editoriale (PDF)
Licenza: Non specificato
Dimensione 427.2 kB
Formato Adobe PDF
427.2 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/135872
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 9
social impact