Thermodynamics of a quantum annealer

被引:29
作者
Buffoni, Lorenzo [1 ,2 ]
Campisi, Michele [1 ,3 ,4 ,5 ]
机构
[1] Univ Florence, Dept Phys & Astron, Via Sansone 1, I-50019 Sesto Fiorentino, FI, Italy
[2] Univ Florence, Dept Informat Engn, Via S Marta 3, I-50139 Florence, Italy
[3] CNR, Ist Nanosci, NEST, Piazza S Silvestro 12, I-56127 Pisa, Italy
[4] Scuola Normale Super Pisa, Ist Nanosci, NEST, Piazza S Silvestro 12, I-56127 Pisa, Italy
[5] Ist Nazl Fis Nucl, Sez Firenze, Via G Sansone 1, I-50019 Sesto Fiorentino, FI, Italy
关键词
quantum thermodynamics; quantum annealing; thermodynamic uncertainty relations; D-Wave;
D O I
10.1088/2058-9565/ab9755
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The D-wave processor is a partially controllable open quantum system that exchanges energy with its surrounding environment (in the form of heat) and with the external time dependent control fields (in the form of work). Despite being rarely thought as such, it is a thermodynamic machine. Here we investigate the properties of the D-Wave quantum annealers from a thermodynamical perspective. We performed a number of reverse-annealing experiments on the D-Wave 2000Q via the open access cloud server Leap, with the aim of understanding what type of thermal operation the machine performs, and quantifying the degree of dissipation that accompanies it, as well as the amount of heat and work that it exchanges. The latter is a challenging task in view of the fact that one can experimentally access only the overall energy change occurring in the processor, (which is the sum of heat and work it receives). However, recent results of non-equilibrium thermodynamics (namely, the fluctuation theorem and the thermodynamic uncertainty relations), allow to calculate lower bounds on the average entropy production (which quantifies the degree of dissipation) as well as the average heat and work exchanges. The analysis of the collected experimental data shows that (1) in a reverse annealing process the D-Wave processor works as a thermal accelerator and (2) its evolution involves an increasing amount of dissipation with increasing transverse field.
引用
收藏
页数:10
相关论文
共 32 条
[1]   Inverse Ising Inference Using All the Data [J].
Aurell, Erik ;
Ekeberg, Magnus .
PHYSICAL REVIEW LETTERS, 2012, 108 (09)
[2]  
Ayanzadeh R, 2020, ARXIV200100234
[3]   Thermodynamic Uncertainty Relation for Biomolecular Processes [J].
Barato, Andre C. ;
Seifert, Udo .
PHYSICAL REVIEW LETTERS, 2015, 114 (15)
[4]   Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices [J].
Benedetti, Marcello ;
Realpe-Gomez, John ;
Perdomo-Ortiz, Alejandro .
QUANTUM SCIENCE AND TECHNOLOGY, 2018, 3 (03)
[5]   Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning [J].
Benedetti, Marcello ;
Realpe-Gomez, John ;
Biswas, Rupak ;
Perdomo-Ortiz, Alejandro .
PHYSICAL REVIEW A, 2016, 94 (02)
[6]  
Buffoni L, 2020, D WAVE NOTEBOOKS
[7]   Nonequilibrium fluctuations in quantum heat engines: theory, example, and possible solid state experiments [J].
Campisi, Michele ;
Pekola, Jukka ;
Fazio, Rosario .
NEW JOURNAL OF PHYSICS, 2015, 17 :1-14
[8]   Colloquium: Quantum fluctuation relations: Foundations and applications (vol 83, pg 771, 2011) [J].
Campisi, Michele ;
Haenggi, Peter ;
Talkner, Peter .
REVIEWS OF MODERN PHYSICS, 2011, 83 (04) :1653-1653
[9]   Colloquium: Quantum fluctuation relations: Foundations and applications [J].
Campisi, Michele ;
Haenggi, Peter ;
Talkner, Peter .
REVIEWS OF MODERN PHYSICS, 2011, 83 (03) :771-791
[10]   Minor-embedding in adiabatic quantum computation: I. The parameter setting problem [J].
Choi, Vicky .
QUANTUM INFORMATION PROCESSING, 2008, 7 (05) :193-209