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Quantum machine learning

About: Quantum machine learning is a research topic. Over the lifetime, 776 publications have been published within this topic receiving 35819 citations.


Papers
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Proceedings ArticleDOI
Lov K. Grover1
01 Jul 1996
TL;DR: In this paper, it was shown that a quantum mechanical computer can solve integer factorization problem in a finite power of O(log n) time, where n is the number of elements in a given integer.
Abstract: were proposed in the early 1980’s [Benioff80] and shown to be at least as powerful as classical computers an important but not surprising result, since classical computers, at the deepest level, ultimately follow the laws of quantum mechanics. The description of quantum mechanical computers was formalized in the late 80’s and early 90’s [Deutsch85][BB92] [BV93] [Yao93] and they were shown to be more powerful than classical computers on various specialized problems. In early 1994, [Shor94] demonstrated that a quantum mechanical computer could efficiently solve a well-known problem for which there was no known efficient algorithm using classical computers. This is the problem of integer factorization, i.e. testing whether or not a given integer, N, is prime, in a time which is a finite power of o (logN) . ----------------------------------------------

6,335 citations

Journal ArticleDOI
TL;DR: The proposed approach drastically reduces the coherence time requirements and combines this method with a new approach to state preparation based on ansätze and classical optimization, enhancing the potential of quantum resources available today and in the near future.
Abstract: Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer. For quantum systems, where the physical dimension grows exponentially, finding the eigenvalues of certain operators is one such intractable problem and remains a fundamental challenge. The quantum phase estimation algorithm efficiently finds the eigenvalue of a given eigenvector but requires fully coherent evolution. Here we present an alternative approach that greatly reduces the requirements for coherent evolution and combine this method with a new approach to state preparation based on ansatze and classical optimization. We implement the algorithm by combining a highly reconfigurable photonic quantum processor with a conventional computer. We experimentally demonstrate the feasibility of this approach with an example from quantum chemistry--calculating the ground-state molecular energy for He-H(+). The proposed approach drastically reduces the coherence time requirements, enhancing the potential of quantum resources available today and in the near future.

3,114 citations

Journal ArticleDOI
06 Aug 2018
TL;DR: Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future, and the 100-qubit quantum computer will not change the world right away - but it should be regarded as a significant step toward the more powerful quantum technologies of the future.
Abstract: Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away --- we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing.

2,598 citations

Journal ArticleDOI
Frank Arute1, Kunal Arya1, Ryan Babbush1, Dave Bacon1, Joseph C. Bardin2, Joseph C. Bardin1, 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. Hartmann1, Michael J. Hartmann6, 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. Korotkov8, Alexander N. Korotkov1, 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. Sung11, Kevin J. Sung1, Matthew D. Trevithick1, Amit Vainsencher1, Benjamin Villalonga1, Benjamin Villalonga12, 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
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
20241
2023139
2022219
2021282
2020212
201999