Qiskit: An Open-source Framework for Quantum Computing

Aleksandrowicz, Gadi; Alexander, Thomas; Barkoutsos, Panagiotis Kl.; Bello, Luciano; Ben-Haim, Yael; Bücher, David; Cabrera-Hernández, Francisco Jose; Carballo-Franquis, Jorge; Chen, Adrian; Chen, Chun-Fu; Chow, Jerry M.; Córcoles-Gonzales, Antonio D.; Cross, Abigail J.; Cross, Andrew W.; Cruz-Benito, Juan; Culver, Chris; González, Salvador De La Puente; Torre, Enrique De La; Ding, Delton; Dumitrescu, Eugene; Ďuran, I.; Eendebak, Pieter; Everitt, Mark S.; Sertage, Ismael Faro; Frisch, Albert; Fuhrer, Andreas; Gambetta, Jay; Gago, Borja Godoy; Gomez-Mosquera, Juan; Greenberg, Donny; Hamamura, Ikko; Havlíček, Vojtěch; Hellmers, Joe; Herok, Ł.; Horii, Hiroshi; Hu, Shaohan; Imamichi, Takashi; Itoko, Toshinari; Javadi-Abhari, Ali; Kanazawa, Naoki; Karazeev, Anton; Krsulich, Kevin; Liu, Peng; Luh, Yang; Maeng, Yunho; Marques, Manoel; Martín-Fernández, Francisco; McClure, Douglas; McKay, David; Meesala, Srujan; Mezzacapo, Antonio; Moll, Nikolaj; Rodríguez, Diego Moreda; Nannicini, Giacomo; Nation, Paul D.; Ollitrault, Pauline J.; O’Riordan, Lee J.; Paik, Hanhee; Pérez, Jesús Enrique Velázquez; Phan, A.; Pistoia, Marco; Prutyanov, Viktor; Reuter, M.; Rice, Julia E.; Davila, Abdón Rodríguez; Rudy, Raymond; Ryu, Mingi; Sathaye, Ninad D.; Schnabel, Chris; Schoute, Eddie; Setia, Kanav; Shi, Yunong; Silva, Adenilton J. da; Siraichi, Yukio; Sivarajah, Seyon; Smolin, John A.; Soeken, Mathias; Takahashi, Hiroyuki; Tavernelli, Ivano; Taylor, C.; Taylour, Pete; Trabing, Kenso; Treinish, Matthew; Turner, Wes; Vogt-Lee, Desiree; Vuillot, Christophe; Wildstrom, Jonathan A.; Wilson, Jessica; Winston, Erick; Wood, Christopher J.; Wood, Stephen; Wörner, Stefan; Akhalwaya, Ismail Yunus; Zoufal, Christa

doi:10.5281/zenodo.2562111

Cited by 267 publications

(173 citation statements)

References 0 publications

Supporting

Mentioning

166

Contrasting

Order By: Relevance

“…The QAOA circuits we consider are deep; therefore there is no benefit to using the MPS simulator. We use the highperformance simulator Qiskit Aer [71] to simulate QAOA circuits due to its convenience. Because of the simulation complexity and the need to optimize parameters for the benchmark instances, we limit the simulations of QAOA to 20 qubits.…”

Section: A L-vqe and Qaoamentioning

confidence: 99%

Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers

Liu

Angone

Shaydulin

et al. 2022

IEEE Trans. Quantum Eng.

View full text Add to dashboard Cite

show abstract

Section: A L-vqe and Qaoamentioning

confidence: 99%

Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers

Liu

Angone

Shaydulin

et al. 2022

IEEE Trans. Quantum Eng.

View full text Add to dashboard Cite

show abstract

“…We conducted two experiments on the 65-qubit IBM Quantum Brooklyn system: the expectation values of GHZ states and the fidelity of GHZ states. The circuits are executed on IBM Quantum Brooklyn by mapping the virtual circuit qubits to physical qubits with [33,32,25,31,34,19,39,30,35,18,45,20,29,40,17,46,36,44,21,28,49,16,47,24,11,37,43,12,27,50,15,53,22,48,4,26,52,8,38,51,14,60,42,23,3,56,7,41,54,13,59, 5, 9, 61, 2, 55, 6, 64, 10, 58, 57, 62, 1, 63, 0] as shown in FIG. 7.…”

Section: Demonstrationsmentioning

confidence: 99%

“…In addition to the matrix inversion process, the mitigated frequency vector must satisfy the property of probability distributions, that is, its elements are nonnegative and the element sum exactly becomes one. Since the application of direct matrix inversion may cause negative elements in the mitigated vector, the open-source software package Qiskit [24] wraps the matrix inversion process by the constrained least square optimization method and find the closest probability vector to the rigorously mitigated vector. In Nation et al's method [21], the columns of the reduced matrix are normalized again so that each of them sums to one.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Quantum Readout Error Mitigation for Sparse Measurement Outcomes of Near-term Quantum Devices

Yang,

Raymond,

Uno

2022

Preprint

View full text Add to dashboard Cite

The readout error on the near-term quantum devices is one of the dominant noise factors, which can be mitigated by classical post-processing called quantum readout error mitigation (QREM). The standard QREM method applies the inverse of noise calibration matrix to the outcome probability distribution using exponential computational resources to the system size. Hence this standard approach is not applicable to the current quantum devices with tens of qubits and more. We propose two efficient QREM methods on such devices whose computational complexity is O(ns 2 ) for probability distributions on measuring n qubits with s shots. The main targets of the proposed methods are the sparse probability distributions where only a few states are dominant. We compare the proposed methods with several recent QREM methods on the following three cases: expectation values of GHZ state, its fidelities, and the estimation error of maximum likelihood amplitude estimation (MLAE) algorithm with modified Grover iterator. The two cases of the GHZ state are on real IBM quantum devices, while the third is by numerical simulation. The proposed methods can be applied to mitigate GHZ states up to 65 qubits on IBM Quantum devices within a few seconds to confirm the existence of a 29-qubit GHZ state with fidelity larger than 0.5. The proposed methods also succeed in the estimation of the amplitude in MLAE with the modified Grover operator where other QREM methods fail.

show abstract

“…For the future development of this field, it is important to further develop quantum classification theories and models to find more potential applications. With the help of the softwares used for simulating these quantum models [256][257][258][259][260][261][262][263][264][265][266][267][268][269], it gets easier to quantify the performance of these models. Moreover, the size of the quantum platforms, such as the superconducting platforms and the ion trap systems, is expected to grow fast to meet the requirement of the theories of the various quantum classifiers.…”

Section: Variational Quantum Classifiersmentioning

confidence: 99%

Recent advances for quantum classifiers

Deng

2021

Sci. China Phys. Mech. Astron.

View full text Add to dashboard Cite

Machine learning has achieved dramatic success in a broad spectrum of applications. Its interplay with quantum physics may lead to unprecedented perspectives for both fundamental research and commercial applications, giving rise to an emergent research frontier of quantum machine learning. Along this line, quantum classifiers, which are quantum devices that aim to solve classification problems in machine learning, have attracted tremendous attention recently. In this review, we give a relatively comprehensive overview for the studies of quantum classifiers, with a focus on recent advances. First, we will review a number of quantum classification algorithms, including quantum support vector machines, quantum kernel methods, quantum decision tree classifiers, quantum nearest neighbor algorithms, and quantum annealing based classifiers. Then, we move on to introduce the variational quantum classifiers, which are essentially variational quantum circuits for classifications. We will review different architectures for constructing variational quantum classifiers and introduce the barren plateau problem, where the training of quantum classifiers might be hindered by the exponentially vanishing gradient. In addition, the vulnerability aspect of quantum classifiers in the setting of adversarial learning and the recent experimental progress on different quantum classifiers will also be discussed.

show abstract

Qiskit: An Open-source Framework for Quantum Computing

Cited by 267 publications

References 0 publications

Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers

Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers

An Efficient Quantum Readout Error Mitigation for Sparse Measurement Outcomes of Near-term Quantum Devices

Recent advances for quantum classifiers

Contact Info

Product

Resources

About