Inductance of Circuit Structures for MIT LL Superconductor Electronics Fabrication Process With 8 Niobium Layers

Tolpygo, Sergey K.; Bolkhovsky, Vladimir; Weir, Terence J.; Galbraith, C.; Johnson, L. M.; Gouker, M.A.; Семенов, В.К.

doi:10.1109/tasc.2014.2369213

Cited by 77 publications

(68 citation statements)

References 25 publications

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“…Quantum adiabatic optimization (QAO) [1][2][3][4][5][6][7] is a paradigm of computing in which a slowly time-evolving Hamiltonian that uses continuously decreasing quantum fluctuations is employed in order to prepare the ground state of a target Hamiltonian in an analog, rather than digital, manner [8][9][10][11][12][13][14]. As such, it is expected by many to be a simpler way of carrying out quantum-assisted calculations experimentally [15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Analog nature of quantum adiabatic unstructured search

et al. 2019

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The quantum adiabatic unstructured search algorithm is one of only a handful of quantum adiabatic optimization algorithms to exhibit provable speedups over their classical counterparts. With no fault tolerance theorems to guarantee the resilience of such algorithms against errors, understanding the impact of imperfections on their performance is of both scientific and practical significance. We study the robustness of the algorithm against various types of imperfections: limited control over the interpolating schedule, Hamiltonian misspecification, and interactions with a thermal environment. We find that the unstructured search algorithm's quadratic speedup is generally not robust to the presence of any one of the above non-idealities, and in some cases we find that it imposes unrealistic conditions on how the strength of these noise sources must scale to maintain the quadratic speedup.Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. 7 We exclude here algorithms derived via the polynomial equivalence theorem between the circuit and adiabatic models of quantum computing [61] since the 'final' Hamiltonians are not necessarily diagonal in the computational basis.

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Section: Introductionmentioning

confidence: 99%

Analog nature of quantum adiabatic unstructured search

et al. 2019

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“…Fabrication of test structures enable this uncertainty to be corrected to around 1%. 35 Furthermore if necessary the mutual inductance can be adaptively corrected by applying a flux to a SQUID loop between the qubits or ancillae on one hand and the coupling loops on the other, following the approach of ref. 34.…”

Section: Resultsmentioning

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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“…We have employed Josephson junction non-dimensionless parameter values that are consistent with the current state-of-theart Josephson junction fabrication capabilities as presented in the references [13]. The principles of memory design and operation described in the paper can in principle be implemented for other Josephson junction based circuits.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, as long as the phases φ k are near the values of 2πn k while system is in equilibrium, their set of offsets θ k is approximated by the matrix equation in (13). …”

mentioning

confidence: 99%

Memory states in small arrays of Josephson junctions

et al. 2016

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Abstract:We studied memory states of a circuit consisting of a small inductively coupled Josephson junction array and introduced basic (Write, Read, Reset) memory operations logics of the circuit. The presented memory operation paradigm is fundamentally different from conventional single quantum flux operation logics. We calculated stability diagrams of the zero-voltage states and outlined memory states of the circuit. We have also calculated memory access times and power dissipation for basic memory operations.PACS: 05.45.Xt, 85.25.Cp, 85.25.Hv

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Inductance of Circuit Structures for MIT LL Superconductor Electronics Fabrication Process With 8 Niobium Layers

Cited by 77 publications

References 25 publications

Analog nature of quantum adiabatic unstructured search

Analog nature of quantum adiabatic unstructured search

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

Memory states in small arrays of Josephson junctions

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