Construction of model Hamiltonians for adiabatic quantum computation and its application to finding low-energy conformations of lattice protein models

Perdomo, Alejandro; Truncik, C. J. S.; Tubert‐Brohman, Ivan; Rose, Geordie; Aspuru‐Guzik, Alán

doi:10.1103/physreva.78.012320

Cited by 111 publications

(100 citation statements)

References 36 publications

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“…In order to get a clue of how well ASP will perform for systems in which we have only a very poor initial guess of the exact wave function, we also numerically simulated ASP with initial Hamiltonian of the following form [21] where σ i x denotes Pauli x matrix acting on ith qubit. Such a Hamiltonian has a non-degenerate ground state equal to the homogeneous superposition of all the computational basis states and thus does not presume any information about the true wave function.…”

Section: A Computational Detailsmentioning

confidence: 99%

“…calculations of excited states [17], quantum chemical dynamics [18], calculations * libor.veis@jh-inst.cas.cz † jiri.pittner@jh-inst.cas.cz of molecular properties [19], or calculations of relativistic systems [20] were published. Application of adiabatic quantum computing for finding low energy conformations of proteins was described in [21,22].…”

Section: Introductionmentioning

confidence: 99%

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Adiabatic state preparation study of methylene

Veis

Pittner

2014

The Journal of Chemical Physics

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Quantum computers attract much attention as they promise to outperform their classical counterparts in solving certain type of problems. One of them with practical applications in quantum chemistry is simulation of complex quantum systems. An essential ingredient of efficient quantum simulation algorithms are initial guesses of the exact wave functions with high enough fidelity. As was proposed in [Aspuru-Guzik et al., Science 309, 1704], the exact ground states can in principle be prepared by the adiabatic state preparation method. Here, we apply this approach to preparation of the lowest lying multireference singlet electronic state of methylene and numerically investigate preparation of this state at different molecular geometries. We then propose modifications that lead to speeding up the preparation process. Finally, we decompose the minimal adiabatic state preparation employing the direct mapping in terms of two-qubit interactions.

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Section: A Computational Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adiabatic state preparation study of methylene

Veis

Pittner

2014

The Journal of Chemical Physics

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“…This finite-temperature, open system model is expected to more accurately describe the dynamics underlying the QPU [8]. Nevertheless, several experimental tests of the D-Wave QPU have been carried out including applications of machine learning, binary classification, protein folding, graph analysis, and network analysis [9][10][11][12][13][14][15][16][17][18]. Demonstrations of enhanced performance using the D-Wave QPU have been found only for a few selected and highly contrived problem instances [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization

Schrock

McCaskey

Hamilton

et al. 2017

Entropy

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A content-addressable memory (CAM) stores key-value associations such that the key is recalled by providing its associated value. While CAM recall is traditionally performed using recurrent neural network models, we show how to solve this problem using adiabatic quantum optimization. Our approach maps the recurrent neural network to a commercially available quantum processing unit by taking advantage of the common underlying Ising spin model. We then assess the accuracy of the quantum processor to store key-value associations by quantifying recall performance against an ensemble of problem sets. We observe that different learning rules from the neural network community influence recall accuracy but performance appears to be limited by potential noise in the processor. The strong connection established between quantum processors and neural network problems supports the growing intersection of these two ideas.

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“…20 Typically this is done iteratively, with an N-body interaction being reduced to a two-body interaction using a complete graph on N logical bits and (N−2) ancilla bits. 23 This necessitates (N−1) (2N−3) two-body couplers.…”

Section: Introductionmentioning

confidence: 99%

Circuit design for multi-body interactions in superconducting quantum annealing systems with applications to a scalable architecture

2017

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Quantum annealing provides a way of solving optimization problems by encoding them as Ising spin models which are implemented using physical qubits. The solution of the optimization problem then corresponds to the ground state of the system. Quantum tunneling is harnessed to enable the system to move to the ground state in a potentially high non-convex energy landscape. A major difficulty in encoding optimization problems in physical quantum annealing devices is the fact that many real world optimization problems require interactions of higher connectivity, as well as multi-body terms beyond the limitations of the physical hardware. In this work we address the question of how to implement multi-body interactions using hardware which natively only provides two-body interactions. The main result is an efficient circuit design of such multi-body terms using superconducting flux qubits in which effective N-body interactions are implemented using N ancilla qubits and only two inductive couplers. It is then shown how this circuit can be used as the unit cell of a scalable architecture by applying it to a recently proposed embedding technique for constructing an architecture of logical qubits with arbitrary connectivity using physical qubits which have nearest-neighbor four-body interactions. It is further shown that this design is robust to non-linear effects in the coupling loops, as well as mismatches in some of the circuit parameters.npj Quantum Information (2017) 3:21 ; doi:10.1038/s41534-017-0022-6 INTRODUCTION Solving machine learning and optimization problems by casting them as an Ising spin glass, and then using a physical device to take advantage of quantum fluctuations has been a subject of much recent interest. [1][2][3][4][5][6][7][8][9] This interest is due in a large part to demonstration of the underlying principles of quantum annealing in condensed matter systems 10 and the more recent development of a programmable annealing device by D-Wave Systems Inc.

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Construction of model Hamiltonians for adiabatic quantum computation and its application to finding low-energy conformations of lattice protein models

Cited by 111 publications

References 36 publications

Adiabatic state preparation study of methylene

Adiabatic state preparation study of methylene

Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization

Circuit design for multi-body interactions in superconducting quantum annealing systems with applications to a scalable architecture

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