Programmable Superconducting Processor with Native Three-Qubit Gates

Roy, Tanay; Hazra, Sumeru; Kundu, Suman; Chand, Madhavi; Patankar, Meghan P.; Vijay, R.

doi:10.1103/physrevapplied.14.014072

Cited by 36 publications

(23 citation statements)

References 64 publications

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“…The state tomography is obtained by a maximum likelihood estimation similar to the one in Ref. [52] using an overcomplete set of 36 tomography pulses. We represent the quantum process [53] in the Pauli basis through the process tomography matrix χ [46] [Fig.…”

Section: Gate Calibration and Metrologymentioning

confidence: 99%

Fast logic with slow qubits: microwave-activated controlled-Z gate on low-frequency fluxoniums

Ficheux,

Nguyen,

Somoroff

et al. 2020

Preprint

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We demonstrate a controlled-Z gate between capacitively coupled fluxonium qubits with transition frequencies 72.3 MHz and 136.3 MHz. The gate is activated by a 61.6 ns long pulse at the frequency between non-computational transitions |10 −|20 and |11 −|21 , during which the qubits complete only 4 and 8 Larmor periods, respectively. The measured gate error of (8 ± 1) × 10 −3 is limited by decoherence in the non-computational subspace, which will likely improve in the next generation devices. Although our qubits are about fifty times slower than transmons, the two-qubit gate is faster than microwave-activated gates on transmons, and the gate error is on par with the lowest reported. Architectural advantages of low-frequency fluxoniums include long qubit coherence time, weak hybridization in the computational subspace, suppressed residual ZZ-coupling rate (here 46 kHz), and absence of either excessive parameter matching or complex pulse shaping requirements.

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Section: Gate Calibration and Metrologymentioning

confidence: 99%

Fast logic with slow qubits: microwave-activated controlled-Z gate on low-frequency fluxoniums

Ficheux,

Nguyen,

Somoroff

et al. 2020

Preprint

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“…We remark that spin Hamiltonians with tri-linear coupling similar to those in H SUSY,OBC spin have been proposed in the context of superconducting circuits [37] and recently realized experimentally [38]. It would be thus interesting to investigate whether one could engineer and quantum simulate the specific Hamiltonian (11) in these systems.…”

Section: Mapping To Spinsmentioning

confidence: 94%

Supersymmetry and multicriticality in a ladder of constrained fermions

2021

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Supersymmetric lattice models of constrained fermions are known to feature exotic phenomena such as superfrustration, with an extensive degeneracy of ground states, the nature of which is however generally unknown. Here we address this issue by considering a superfrustrated model, which we deform from the supersymetric point. By numerically studying its two-parameter phase diagram, we reveal a rich phenomenology. The vicinity of the supersymmetric point features period-4 and period-5 density waves which are connected by a floating phase (incommensurate Luttinger liquid) with smoothly varying density. The supersymmetric point emerges as a multicritical point between these three phases. Inside the period-4 phase we report a valence-bond solid type ground state that persists up to the supersymmetric point. Our numerical data for transitions out of density-wave phases are consistent with the Pokrovsky-Talapov universality class. Furthermore, our analysis unveiled a period-3 phase with a boundary determined by a competition between single and two-particle instabilities accompanied by a doubling of the wavevector of the density profiles along a line in the phase diagram.

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“…[ 151 ] However, it is expected that the required engineering would be much lesser for obtaining QRN than quantum computers. Existing quantum technologies, such as, small scale quantum computers [ 152,153 ] and D‐wave quantum annealers [ 154 ] could be readily used as QRNs. These systems can be coupled to the external input signals in the form of microwaves.…”

Section: Physical Systemsmentioning

confidence: 99%

Quantum Neuromorphic Computing with Reservoir Computing Networks

et al. 2021

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Quantum reservoir networks combine the intelligence of neural networks with the potential of quantum computing in a single platform. This platform operates on the architecture of reservoir computing, which can function even with random connections between neural nodes. This is a major advantage for hardware implementation. Herein is described how reservoir computing is brought into the quantum domain to perform various tasks, including characterization of quantum states, quantum estimation, quantum state preparation, and quantum computing. It shows quantum enhancement in classical data processing, and creates the opportunity for quantum information processing within the robust paradigm of neural networks. It is friendly for implementation in a wide range of physical systems, including quantum dots, superconductors, trapped ions, cold atoms, and exciton‐polaritons.

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Programmable Superconducting Processor with Native Three-Qubit Gates

Cited by 36 publications

References 64 publications

Fast logic with slow qubits: microwave-activated controlled-Z gate on low-frequency fluxoniums

Fast logic with slow qubits: microwave-activated controlled-Z gate on low-frequency fluxoniums

Supersymmetry and multicriticality in a ladder of constrained fermions

Quantum Neuromorphic Computing with Reservoir Computing Networks

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