D-NISQ: A reference model for Distributed Noisy Intermediate-Scale Quantum computers

被引:11
作者
Acampora, Giovanni [1 ,2 ]
Di Martino, Ferdinando [3 ]
Massa, Alfredo [4 ]
Schiattarella, Roberto [1 ]
Vitiello, Autilia [1 ,2 ]
机构
[1] Univ Naples Federico II, Dept Phys Ettore Pancini, I-80126 Naples, Italy
[2] Ist Nazl Fis Nucleare, Sez Napoli, I-80126 Naples, Italy
[3] Univ Naples Federico II, Dept Architecture, I-80134 Naples, Italy
[4] QuantumNet, I-80143 Naples, Italy
关键词
Quantum computing; Distributed architectures; Quantum algorithms;
D O I
10.1016/j.inffus.2022.08.003
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Quantum computing has entered its mature life thanks to the availability of cloud-based Noisy Intermediate -Scale Quantum (NISQ) technologies. These devices allow quantum researchers and practitioners to design, develop and test quantum algorithms on actual hardware, paving the way toward new approaches in solving problems intractable by classical computers. However, in spite of these achievements in quantum technologies, the size of the problems that can be actually solved by quantum algorithms is still limited by the small number of qubits and the considerable noise in computation that still characterizes NISQ devices. As a consequence, there is a strong need of introducing innovative computing architectures able to interconnect a set of NISQ devices to increase the capabilities of current quantum computers of solving hard problems in a reliable way. In this paper, the concept of Distributed Noisy-Intermediate Scale Quantum (D-NISQ) is introduced as a reference computational model by which designing innovative frameworks where quantum devices interact to solve a complex problem by working cooperatively. In detail, the D-NISQ model consists of a hybrid and hierarchical architecture where classical and quantum processors interact to iteratively split up a problem into a collection of sub-problems, solve each one of them by means of a proper quantum algorithm, and fuse output quantum information so as to compute the final solution to the problem posed. As demonstrated by two case studies based on the well-known Grover's algorithm, a multi-threaded implementation of the D-NISQ reference model allows for greater reliability in solving problems by quantum computation.
引用
收藏
页码:16 / 28
页数:13
相关论文
共 32 条
[1]   Implementing evolutionary optimization on actual quantum processors [J].
Acampora, Giovanni ;
Vitiello, Autilia .
INFORMATION SCIENCES, 2021, 575 :542-562
[2]   Deep neural networks for quantum circuit mapping [J].
Acampora, Giovanni ;
Schiattarella, Roberto .
NEURAL COMPUTING & APPLICATIONS, 2021, 33 (20) :13723-13743
[3]  
[Anonymous], 2017, Technical report
[4]   Quantum transfer learning for breast cancer detection [J].
Azevedo, Vanda ;
Silva, Carla ;
Dutra, Ines .
QUANTUM MACHINE INTELLIGENCE, 2022, 4 (01)
[5]   Efficient distributed quantum computing [J].
Beals, Robert ;
Brierley, Stephen ;
Gray, Oliver ;
Harrow, Aram W. ;
Kutin, Samuel ;
Linden, Noah ;
Shepherd, Dan ;
Stather, Mark .
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2013, 469 (2153)
[6]   Distributed quantum computation over noisy channels [J].
Cirac, JI ;
Ekert, AK ;
Huelga, SF ;
Macchiavello, C .
PHYSICAL REVIEW A, 1999, 59 (06) :4249-4254
[7]   Validating quantum computers using randomized model circuits [J].
Cross, Andrew W. ;
Bishop, Lev S. ;
Sheldon, Sarah ;
Nation, Paul D. ;
Gambetta, Jay M. .
PHYSICAL REVIEW A, 2019, 100 (03)
[8]   Towards a distributed quantum computing ecosystem [J].
Cuomo, Daniele ;
Caleffi, Marcello ;
Cacciapuoti, Angela Sara .
IET QUANTUM COMMUNICATION, 2020, 1 (01) :3-8
[9]  
Denchev Vasil S., 2008, SIGACT News, V39, P77, DOI 10.1145/1412700.1412718
[10]   Distributed combination of belief functions [J].
Denoeux, Thierry .
INFORMATION FUSION, 2021, 65 :179-191