Transport implementation of the Bernstein–Vazirani algorithm with ion qubits

Fallek, Spencer D.; Herold, Creston; McMahon, Brian J.; Maller, Kara; Brown, Kenneth R.; Amini, Jason M.

doi:10.1088/1367-2630/18/8/083030

Cited by 32 publications

(18 citation statements)

References 38 publications

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“…To benchmark our system, we implement two wellknown algorithms: the Bernstein-Vazirani (BV) and Hidden Shift (HS). Both of these algorithms have previously been run on trapped-ion [10,25,33] and superconducting [11,25,26] systems of up to 5 qubits. By comparing the results of this algorithm to the ideal result, we obtain a direct measure of the system performance, which accounts for our native gates, connectivity, coherence times, gate duration, and all other isolated metrics of system performance.…”

Section: Fig 2 Fidelity Of Native Gatesmentioning

confidence: 99%

Benchmarking an 11-qubit quantum computer

et al. 2019

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The field of quantum computing has grown from concept to demonstration devices over the past 20 years. Universal quantum computing offers efficiency in approaching problems of scientific and commercial interest, such as factoring large numbers, searching databases, simulating intractable models from quantum physics, and optimizing complex cost functions. Here, we present an 11-qubit fully-connected, programmable quantum computer in a trapped ion system composed of 13 171Yb+ ions. We demonstrate average single-qubit gate fidelities of 99.5, average two-qubit-gate fidelities of 97.5, and SPAM errors of 0.7. To illustrate the capabilities of this universal platform and provide a basis for comparison with similarly-sized devices, we compile the Bernstein-Vazirani and Hidden Shift algorithms into our native gates and execute them on the hardware with average success rates of 78 and 35, respectively. These algorithms serve as excellent benchmarks for any type of quantum hardware, and show that our system outperforms all other currently available hardware.

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Section: Fig 2 Fidelity Of Native Gatesmentioning

confidence: 99%

Benchmarking an 11-qubit quantum computer

et al. 2019

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“…Note that we have omitted errors introducing ion crosstalk during individual ion addressing. This is because systems can be built to minimize this effect and control sequences exist that can reduce the effect of such errors [109][110][111][112].…”

Section: Ms Gate Control Errorsmentioning

confidence: 99%

Simulating the performance of a distance-3 surface code in a linear ion trap

Trout

Gutiérrez

et al. 2018

New J. Phys.

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We explore the feasibility of implementing a small surface code with 9 data qubits and 8 ancilla qubits, commonly referred to as surface-17, using a linear chain of 171 Yb+ ions. Two-qubit gates can be performed between any two ions in the chain with gate time increasing linearly with ion distance. Measurement of the ion state by fluorescence requires that the ancilla qubits be physically separated from the data qubits to avoid errors on the data due to scattered photons. We minimize the time required to measure one round of stabilizers by optimizing the mapping of the two-dimensional surface code to the linear chain of ions. We develop a physically motivated Pauli error model that allows for fast simulation and captures the key sources of noise in an ion trap quantum computer including gate imperfections and ion heating. Our simulations showed a consistent requirement of a two-qubit gate fidelity of 99.9% for the logical memory to have a better fidelity than physical two-qubit operations. Finally, we perform an analysis of the error subsets from the importance sampling method used to bound the logical error rates to gain insight into which error sources are particularly detrimental to error correction.

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“…In these calculations we assumed the gates were driven by copropagating linearly polarized Raman beams with a laser frequency of 355 nm and a two qubit gate time of 200 µs. These parameters minimize spontaneous scattering and reflect parameters used in recent experiments [41][42][43][44].…”

Section: B Memory Effectsmentioning

confidence: 99%

Leakage mitigation for quantum error correction using a mixed qubit scheme

Brown

2019

Phys. Rev. A

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Leakage errors take qubits out of the computational subspace and will accumulate if not addressed. A leaked qubit will reduce the effectiveness of quantum error correction protocols due to the cost of implementing leakage reduction circuits and the harm caused by interacting leaked states with qubit states. Ion trap qubits driven by Raman gates have a natural choice between qubits encoded in magnetically insensitive hyperfine states that can leak and qubits encoded in magnetically sensitive Zeeman states of the electron spin that cannot leak. In our previous work, we compared these two qubits in the context of the toric code with a depolarizing leakage error model and found that for magnetic field noise with a standard deviation less than 32 µG that the 174 Yb + Zeeman qubit outperforms the 171 Yb + hyperfine qubit. Here we examine a physically motivated leakage error model based on ions interacting via the Mølmer-Sørenson gate. We find that this greatly improves the performance of hyperfine qubits but the Zeeman qubits are more effective for magnetic field noise with a standard deviation less than 10 µG. At these low magnetic fields, we find that the best choice is a mixed qubit scheme where the hyperfine qubits are the ancilla and the leakage is handled without the need of an additional leakage reduction circuit.

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Transport implementation of the Bernstein–Vazirani algorithm with ion qubits

Cited by 32 publications

References 38 publications

Benchmarking an 11-qubit quantum computer

Benchmarking an 11-qubit quantum computer

Simulating the performance of a distance-3 surface code in a linear ion trap

Leakage mitigation for quantum error correction using a mixed qubit scheme

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