Engineering the Quantum Scientific Computing Open User Testbed

Clark, Susan M.; Lobser, Daniel; Revelle, Melissa; Yale, Christopher G.; Bossert, David; Burch, Ashlyn D.; Chow, Matthew N. H.; Hogle, Craig W.; Ivory, Megan; Pehr, Jessica; Bradley, Salzbrenner,; Stick, Daniel Lynn; Sweatt, William C.; Wilson, Joshua M.; Winrow, Edward G.; Maunz, Peter

doi:10.1109/tqe.2021.3096480

Cited by 31 publications

(19 citation statements)

References 54 publications

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“…The beams pass through acousto-optic modulators (AOMs) driven by Radio Frequency System on Chip (RFSoC), which provides the ability to change the amplitude, frequency, and phase of each beam. The RFSoC firmware is provided by Sandia National Laboratories QSCOUT project [51]. By controlling the duration of the pulse and the phase of one of the two Raman beams we can perform arbitrary single qubit rotations, R(θ, φ).…”

Section: Appendix B: Experimental Methodsmentioning

confidence: 99%

Hidden Inverses: Coherent Error Cancellation at the Circuit Level

Zhang,

Majumder,

Leung

et al. 2021

Preprint

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Coherent gate errors are a concern in many proposed quantum computing architectures. These errors can be effectively handled through composite pulse sequences for single-qubit gates, however, such techniques are less feasible for entangling operations. In this work, we benchmark our coherent errors by comparing the actual performance of composite single-qubit gates to the predicted performance based on characterization of individual single-qubit rotations. We then propose a compilation technique, which we refer to as hidden inverses, that creates circuits robust to these coherent errors. We present experimental data showing that these circuits suppress both overrotation and phase misalignment errors in our trapped ion system.

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Section: Appendix B: Experimental Methodsmentioning

confidence: 99%

Hidden Inverses: Coherent Error Cancellation at the Circuit Level

Zhang,

Majumder,

Leung

et al. 2021

Preprint

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“…The Quantum Scientific Computing Open User Testbed (QSCOUT) is a quantum processor based on trapped ions housed at Sandia National Laboratories [21]. For the experiments shown here, two 171 Yb + ions were used, in which the qubit states are defined by the hyperfine 'clock' transition of a 171 Yb + ion,…”

Section: Qscout Experimental Setupmentioning

confidence: 99%

“…As a first experimental demonstration, we executed 50 RAV and 50 XEB sequences on the two-qubit trappedion quantum processor at the Quantum Scientific Computing Open User Testbed (QSCOUT) operated by Sandia National Laboratories [21]. Details of the experiment are provided in Section II C. We note that this device directly implements the parameterized native gate set {R(θ, ϕ), R Z (θ), M S(θ, ϕ)} that we used to generate these sequences.…”

Section: Qscout Trapped-ion Processormentioning

confidence: 99%

“…In this work, we discuss the application of the randomized analog verification (RAV) technique [20] to the task of verifying continuouslyparameterized quantum gates. In Section II, we provide an overview of the RAV technique and compare it to cross-entropy benchmarking, one of the primary existing techniques for this task; we describe the stochastic compilation scheme used in constructing the RAV sequences; and we provide details on the experimental setup of the trapped-ion quantum processor, the Quantum Scientific Computing Open User Testbed (QSCOUT) operated by Sandia National Laboratories [21], including the technical details of the functionality required to support RAV sequences. In Section III, we provide numerical simulations demonstrating the conditions under which we expect RAV to provide an efficiency advantage over existing techniques, and we report experimental demonstrations of this efficiency advantage on the QSCOUT trapped-ion device and on a superconducting quantum processor from the publicly-available IBM Q service [22].…”

Section: Introductionmentioning

confidence: 99%

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Efficient verification of continuously-parameterized quantum gates

Shaffer¹,

Ren²,

Dyrenkova³

et al. 2022

Preprint

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Most near-term quantum information processing devices will not be capable of implementing quantum error correction and the associated logical quantum gate set. Instead, quantum circuits will be implemented directly using the physical native gate set of the device. These native gates often have a parameterization (e.g., rotation angles) which provide the ability to perform a continuous range of operations. Verification of the correct operation of these gates across the allowable range of parameters is important for gaining confidence in the reliability of these devices. In this work, we demonstrate a procedure for efficient verification of continuously-parameterized quantum gates. This procedure involves generating random sequences of randomly-parameterized layers of gates chosen from the native gate set of the device, and then stochastically compiling an approximate inverse to this sequence such that executing the full sequence on the device should leave the system near its initial state. We show that fidelity estimates made via this technique have a lower variance than fidelity estimates made via cross-entropy benchmarking, which thus provides an efficiency advantage when estimating the error rate to some desired precision. We describe the experimental realization of this technique using a continuously-parameterized quantum gate set on the Sandia QSCOUT trapped-ion processor, and we demonstrate the efficiency advantage of this technique both numerically and experimentally.

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“…We investigate these hidden inverse protocols on the Quantum Scientific Computing Open User Testbed (QS-COUT), a room-temperature quantum processor based on trapped ions [19] housed at Sandia National Laboratories. This investigation consisted of either one or two qubits, in which the qubit states comprise the hyperfine 'clock' transition of a 171 Yb + ion, 2 S 1/2 |F=0, m F = 0 (|0 ) and |F=1, m F = 0 (|1 ) [20].…”

Section: Experimental Implementation Of Quantum Circuits On a Trapped...mentioning

confidence: 99%

Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses

Majumder¹,

Yale²,

Morris³

et al. 2022

Preprint

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Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that small algorithms can be performed reliably. While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development. Demonstrations of these small algorithms over the past few years, coupled with the idea that imperfect algorithm performance can be caused by several dominant noise sources in the quantum processor, which can be measured and calibrated during algorithm execution or in post-processing, has led to the use of noise mitigation to improve typical computational results. Conversely, small benchmark algorithms coupled with noise mitigation can help diagnose the nature of the noise, whether systematic or purely random. Here, we outline the use of coherent noise mitigation techniques as a characterization tool in trapped-ion testbeds. We perform model-fitting of the noisy data to determine the noise source based on realistic physics focused noise models and demonstrate that systematic noise amplification coupled with error mitigation schemes provides useful data for noise model deduction. Further, in order to connect lower level noise model details with application specific performance of near term algorithms, we experimentally construct the loss landscape of a variational algorithm under various injected noise sources coupled with error mitigation techniques. This type of connection enables application-aware hardware codesign, in which the most important noise sources in specific applications, like quantum chemistry, become foci of improvement in subsequent hardware generations.

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Engineering the Quantum Scientific Computing Open User Testbed

Cited by 31 publications

References 54 publications

Hidden Inverses: Coherent Error Cancellation at the Circuit Level

Hidden Inverses: Coherent Error Cancellation at the Circuit Level

Efficient verification of continuously-parameterized quantum gates

Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses

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