scholarly journals Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers - Artificial Intelligence to Heuristics

2021 ◽  
pp. 1-1
Author(s):  
Janusz Kusyk ◽  
Samah Saeed ◽  
Muharrem Umit Uyar
Keyword(s):  
Artificial Intelligence ◽  
Quantum Circuit ◽  
Quantum Computers ◽  
Intermediate Scale

scholarly journals Test of the unitary coupled-cluster variational quantum eigensolver for a simple strongly correlated condensed-matter system

2020 ◽  
Vol 34 (19n20) ◽  
pp. 2040049
Author(s):  
Luogen Xu ◽  
J. T. Lee ◽  
J. K. Freericks
Keyword(s):  
Condensed Matter ◽  
Quantum Circuit ◽  
Coupled Cluster ◽  
Quantum Computers ◽  
Strongly Correlated ◽  
Intermediate Scale ◽  
Condensed Matter System ◽  
Matter System ◽  
Nontrivial Behavior ◽  
Half Filling

The variational quantum eigensolver has been proposed as a low-depth quantum circuit that can be employed to examine strongly correlated systems on today’s noisy intermediate-scale quantum computers. We examine details associated with the factorized form of the unitary coupled-cluster variant of this algorithm. We apply it to a simple strongly correlated condensed-matter system with nontrivial behavior — the four-site Hubbard model at half-filling. This work show some of the subtle issues one needs to take into account when applying this algorithm in practice, especially to condensed-matter systems.

scholarly journals A divide-and-conquer algorithm for quantum state preparation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Israel F. Araujo ◽  
Daniel K. Park ◽  
Francesco Petruccione ◽  
Adenilton J. da Silva
Keyword(s):  
Computational Cost ◽  
Quantum Circuit ◽  
Divide And Conquer ◽  
Computational Time ◽  
Quantum Computers ◽  
Dimensional Vector ◽  
Quantum Devices ◽  
Divide And Conquer Algorithm ◽  
Quantum State Preparation ◽  
Quantum Device

AbstractAdvantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.

scholarly journals Comparison of quantum-walk implementations on noisy intermediate-scale quantum computers

2021 ◽  
Vol 103 (2) ◽  
Author(s):  
Konstantinos Georgopoulos ◽  
Clive Emary ◽  
Paolo Zuliani
Keyword(s):  
Quantum Walk ◽  
Quantum Computers ◽  
Intermediate Scale

scholarly journals Deep neural networks for quantum circuit mapping

2021 ◽  
Author(s):  
Giovanni Acampora ◽  
Roberto Schiattarella
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Machine Learning Techniques ◽  
Quantum Computers ◽  
Physical Constraints ◽  
Proper Mapping ◽  
Correct Execution

AbstractQuantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy.

scholarly journals Decoherence dynamics estimation for superconducting gate-model quantum computers

2020 ◽  
Vol 19 (10) ◽  
Author(s):  
Laszlo Gyongyosi
Keyword(s):  
Gaussian Noise ◽  
Quantum Computer ◽  
Computer Architectures ◽  
Quantum Circuits ◽  
Optimal Placement ◽  
Quantum Computers ◽  
Intermediate Scale ◽  
Layout Generation ◽  
Quantum Technology ◽  
Physical Layout

Abstract Superconducting gate-model quantum computer architectures provide an implementable model for practical quantum computations in the NISQ (noisy intermediate scale quantum) technology era. Due to hardware restrictions and decoherence, generating the physical layout of the quantum circuits of a gate-model quantum computer is a challenge. Here, we define a method for layout generation with a decoherence dynamics estimation in superconducting gate-model quantum computers. We propose an algorithm for the optimal placement of the quantum computational blocks of gate-model quantum circuits. We study the effects of capacitance interference on the distribution of the Gaussian noise in the Josephson energy.

scholarly journals Open shell electronic state calculations on quantum computers: A quantum circuit for the preparation of configuration state functions based on Serber construction

2019 ◽  
Vol 1 ◽  
pp. 100002 ◽  
Author(s):  
Kenji Sugisaki ◽  
Satoru Yamamoto ◽  
Shigeaki Nakazawa ◽  
Kazuo Toyota ◽  
Kazunobu Sato ◽  
...  
Keyword(s):  
Electronic State ◽  
Quantum Circuit ◽  
Open Shell ◽  
Quantum Computers ◽  
State Functions

scholarly journals Quantum chemistry on quantum computers: quantum simulations of the time evolution of wave functions under the S2 operator and determination of the spin quantum number S

2019 ◽  
Vol 21 (28) ◽  
pp. 15356-15361 ◽  
Author(s):  
Kenji Sugisaki ◽  
Shigeaki Nakazawa ◽  
Kazuo Toyota ◽  
Kazunobu Sato ◽  
Daisuke Shiomi ◽  
...  
Keyword(s):  
Time Evolution ◽  
Quantum Number ◽  
Quantum Circuit ◽  
Wave Functions ◽  
Quantum Computers ◽  
Phase Estimation ◽  
Quantum Simulations ◽  
Spin Quantum ◽  
Spin Quantum Number

A quantum circuit to simulate time evolution of wave functions under an S2 operator is provided, and integrated it to the quantum phase estimation circuit to calculate the spin quantum number S of arbitrary wave functions on quantum computers.

Sign in / Sign up

Export Citation Format

Share Document