Implementation of measurement reduction for the variational quantum eigensolver

Alexis Ralli; Peter J. Love; Andrew Tranter; Peter V. Coveney

doi:10.1103/physrevresearch.3.033195

Mathematical Processing of Quantum Images in a Biphoton Setup via Measurement Reduction

Moscow University Physics Bulletin ◽

10.3103/s0027134920040037 ◽

2020 ◽

Vol 75 (4) ◽

pp. 295-303

Author(s):

D. A. Balakin ◽

A. V. Belinsky

Keyword(s):

Measurement Reduction ◽

Mathematical Processing ◽

Quantum Images

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Sectorized approach and measurement reduction for mutual coupling calibration of non-omnidirectional antenna arrays

Radio Science ◽

10.1002/rds.20017 ◽

2013 ◽

Vol 48 (2) ◽

pp. 102-110 ◽

Cited By ~ 3

Author(s):

Taylan Aksoy ◽

T. Engin Tuncer

Keyword(s):

Antenna Arrays ◽

Mutual Coupling ◽

Measurement Reduction ◽

Omnidirectional Antenna

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Contextual Subspace Variational Quantum Eigensolver

Quantum ◽

10.22331/q-2021-05-14-456 ◽

2021 ◽

Vol 5 ◽

pp. 456

Author(s):

William M. Kirby ◽

Andrew Tranter ◽

Peter J. Love

Keyword(s):

Small Molecules ◽

Ground State Energy ◽

Ground State ◽

Original Problem ◽

State Energy ◽

Quantum Devices ◽

Intermediate Scale ◽

Measurement Reduction ◽

Quantum Processor

We describe the contextual subspace variational quantum eigensolver (CS-VQE), a hybrid quantum-classical algorithm for approximating the ground state energy of a Hamiltonian. The approximation to the ground state energy is obtained as the sum of two contributions. The first contribution comes from a noncontextual approximation to the Hamiltonian, and is computed classically. The second contribution is obtained by using the variational quantum eigensolver (VQE) technique to compute a contextual correction on a quantum processor. In general the VQE computation of the contextual correction uses fewer qubits and measurements than the VQE computation of the original problem. Varying the number of qubits used for the contextual correction adjusts the quality of the approximation. We simulate CS-VQE on tapered Hamiltonians for small molecules, and find that the number of qubits required to reach chemical accuracy can be reduced by more than a factor of two. The number of terms required to compute the contextual correction can be reduced by more than a factor of ten, without the use of other measurement reduction schemes. This indicates that CS-VQE is a promising approach for eigenvalue computations on noisy intermediate-scale quantum devices.

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Multi-Operational Machining Processes Modeling for Sequential Root Cause Identification and Measurement Reduction

Manufacturing Engineering and Materials Handling Engineering ◽

10.1115/imece2004-59330 ◽

2004 ◽

Cited By ~ 1

Author(s):

Hui Wang ◽

Qiang Huang ◽

Reuven Katz

Keyword(s):

Machine Tool ◽

Propagation Model ◽

Machining Processes ◽

Error Sources ◽

Variation Reduction ◽

Measurement Reduction ◽

Propagation Modeling ◽

Root Cause ◽

Variation Propagation ◽

Root Cause Identification

Variation propagation modeling has been proved to be an effective way for variation reduction and design synthesis in multi-operational manufacturing processes (MMP). However, previously developed approaches for machining processes did not directly model the process physics regarding how fixture, and datum, and machine tool errors generate the same pattern on part features. Consequently, it is difficult to distinguish error sources at each operation. This paper formulates the variation propagation model using the proposed equivalent fixture error (EFE) concept. With this concept, datum error and machine tool error are transformed to equivalent fixture locator errors at each operation. As a result, error sources can be grouped and root cause identification can be conducted in a sequential manner. The case studies demonstrate the model validity through a real cutting experiment and model advantage in measurement reduction for root cause identification.

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