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Quantum computer

About: Quantum computer is a research topic. Over the lifetime, 30043 publications have been published within this topic receiving 907276 citations.


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Journal ArticleDOI
Frank Arute1, Kunal Arya1, Ryan Babbush1, Dave Bacon1, Joseph C. Bardin1, Joseph C. Bardin2, Rami Barends1, Rupak Biswas3, Sergio Boixo1, Fernando G. S. L. Brandão1, Fernando G. S. L. Brandão4, David A. Buell1, B. Burkett1, Yu Chen1, Zijun Chen1, Ben Chiaro5, Roberto Collins1, William Courtney1, Andrew Dunsworth1, Edward Farhi1, Brooks Foxen1, Brooks Foxen5, Austin G. Fowler1, Craig Gidney1, Marissa Giustina1, R. Graff1, Keith Guerin1, Steve Habegger1, Matthew P. Harrigan1, Michael J. Hartmann6, Michael J. Hartmann1, Alan Ho1, Markus R. Hoffmann1, Trent Huang1, Travis S. Humble7, Sergei V. Isakov1, Evan Jeffrey1, Zhang Jiang1, Dvir Kafri1, Kostyantyn Kechedzhi1, Julian Kelly1, Paul V. Klimov1, Sergey Knysh1, Alexander N. Korotkov1, Alexander N. Korotkov8, Fedor Kostritsa1, David Landhuis1, Mike Lindmark1, E. Lucero1, Dmitry I. Lyakh7, Salvatore Mandrà3, Jarrod R. McClean1, Matt McEwen5, Anthony Megrant1, Xiao Mi1, Kristel Michielsen9, Kristel Michielsen10, Masoud Mohseni1, Josh Mutus1, Ofer Naaman1, Matthew Neeley1, Charles Neill1, Murphy Yuezhen Niu1, Eric Ostby1, Andre Petukhov1, John Platt1, Chris Quintana1, Eleanor Rieffel3, Pedram Roushan1, Nicholas C. Rubin1, Daniel Sank1, Kevin J. Satzinger1, Vadim Smelyanskiy1, Kevin J. Sung1, Kevin J. Sung11, Matthew D. Trevithick1, Amit Vainsencher1, Benjamin Villalonga12, Benjamin Villalonga1, Theodore White1, Z. Jamie Yao1, Ping Yeh1, Adam Zalcman1, Hartmut Neven1, John M. Martinis5, John M. Martinis1 
24 Oct 2019-Nature
TL;DR: Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.
Abstract: The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor1. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits2-7 to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 253 (about 1016). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times-our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy8-14 for this specific computational task, heralding a much-anticipated computing paradigm.

2,527 citations

Journal ArticleDOI
TL;DR: A class of problems is described which can be solved more efficiently by quantum computation than by any classical or stochastic method.
Abstract: A class of problems is described which can be solved more efficiently by quantum computation than by any classical or stochastic method. The quantum computation solves the problem with certainty in exponentially less time than any classical deterministic computation.

2,509 citations

Journal ArticleDOI
TL;DR: In this article, the authors reviewed the original theory and its improvements, and a few examples of experimental two-qubit gates are given, and the use of realistic components, the errors they induce in the computation, and how these errors can be corrected is discussed.
Abstract: Linear optics with photon counting is a prominent candidate for practical quantum computing. The protocol by Knill, Laflamme, and Milburn [2001, Nature (London) 409, 46] explicitly demonstrates that efficient scalable quantum computing with single photons, linear optical elements, and projective measurements is possible. Subsequently, several improvements on this protocol have started to bridge the gap between theoretical scalability and practical implementation. The original theory and its improvements are reviewed, and a few examples of experimental two-qubit gates are given. The use of realistic components, the errors they induce in the computation, and how these errors can be corrected is discussed.

2,483 citations

Journal ArticleDOI
12 Apr 2001-Nature
TL;DR: In this article, an implementation of Grover's algorithm that uses molecular magnets was proposed, which can be used to build dense and efficient memory devices based on the Grover algorithm, in which one single crystal can serve as a storage unit of a dynamic random access memory device.
Abstract: Shor and Grover demonstrated that a quantum computer can outperform any classical computer in factoring numbers1 and in searching a database2 by exploiting the parallelism of quantum mechanics. Whereas Shor's algorithm requires both superposition and entanglement of a many-particle system3, the superposition of single-particle quantum states is sufficient for Grover's algorithm4. Recently, the latter has been successfully implemented5 using Rydberg atoms. Here we propose an implementation of Grover's algorithm that uses molecular magnets6,7,8,9,10, which are solid-state systems with a large spin; their spin eigenstates make them natural candidates for single-particle systems. We show theoretically that molecular magnets can be used to build dense and efficient memory devices based on the Grover algorithm. In particular, one single crystal can serve as a storage unit of a dynamic random access memory device. Fast electron spin resonance pulses can be used to decode and read out stored numbers of up to 105, with access times as short as 10-10 seconds. We show that our proposal should be feasible using the molecular magnets Fe8 and Mn12.

2,378 citations

Journal ArticleDOI
14 Sep 2017-Nature
TL;DR: The experimental optimization of Hamiltonian problems with up to six qubits and more than one hundred Pauli terms is demonstrated, determining the ground-state energy for molecules of increasing size, up to BeH2.
Abstract: The ground-state energy of small molecules is determined efficiently using six qubits of a superconducting quantum processor. Quantum simulation is currently the most promising application of quantum computers. However, only a few quantum simulations of very small systems have been performed experimentally. Here, researchers from IBM present quantum simulations of larger systems using a variational quantum eigenvalue solver (or eigensolver), a previously suggested method for quantum optimization. They perform quantum chemical calculations of LiH and BeH2 and an energy minimization procedure on a four-qubit Heisenberg model. Their application of the variational quantum eigensolver is hardware-efficient, which means that it is optimized on the given architecture. Noise is a big problem in this implementation, but quantum error correction could eventually help this experimental set-up to yield a quantum simulation of chemically interesting systems on a quantum computer. Quantum computers can be used to address electronic-structure problems and problems in materials science and condensed matter physics that can be formulated as interacting fermionic problems, problems which stretch the limits of existing high-performance computers1. Finding exact solutions to such problems numerically has a computational cost that scales exponentially with the size of the system, and Monte Carlo methods are unsuitable owing to the fermionic sign problem. These limitations of classical computational methods have made solving even few-atom electronic-structure problems interesting for implementation using medium-sized quantum computers. Yet experimental implementations have so far been restricted to molecules involving only hydrogen and helium2,3,4,5,6,7,8. Here we demonstrate the experimental optimization of Hamiltonian problems with up to six qubits and more than one hundred Pauli terms, determining the ground-state energy for molecules of increasing size, up to BeH2. We achieve this result by using a variational quantum eigenvalue solver (eigensolver) with efficiently prepared trial states that are tailored specifically to the interactions that are available in our quantum processor, combined with a compact encoding of fermionic Hamiltonians9 and a robust stochastic optimization routine10. We demonstrate the flexibility of our approach by applying it to a problem of quantum magnetism, an antiferromagnetic Heisenberg model in an external magnetic field. In all cases, we find agreement between our experiments and numerical simulations using a model of the device with noise. Our results help to elucidate the requirements for scaling the method to larger systems and for bridging the gap between key problems in high-performance computing and their implementation on quantum hardware.

2,348 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,640
20223,474
20213,313
20203,338
20192,537
20181,976