Publications

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Frontiers in Quantum Science and Technology

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We demonstrate an order of magnitude reduction in the sensitivity to optical crosstalk for neighboring trapped-ion qubits during simultaneous single-qubit gates driven with individual addressing beams. Gates are implemented via two-photon Raman transitions, where crosstalk is mitigated by offsetting the drive frequencies for each qubit to avoid first-order crosstalk effects from inter-beam two-photon resonance. The technique is simple to implement, and we find that phase-dependent crosstalk due to optical interference is reduced on the most impacted neighbor from a maximal fractional rotation error of 0.185 ( 4 ) without crosstalk mitigation to ≤ 0.006 with the mitigation strategy. Furthermore, we characterize first-order crosstalk in the two-qubit gate and avoid the resulting rotation errors for the arbitrary-axis Mølmer-Sørensen gate via a phase-agnostic composite gate. Finally, we demonstrate holistic system performance by constructing a composite CNOT gate using the improved single-qubit gates and phase-agnostic two-qubit gate. This work is done on the Quantum Scientific Computing Open User Testbed; however, our methods are widely applicable for individual addressing Raman gates and impose no significant overhead, enabling immediate improvement for quantum processors that incorporate this technique.

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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 near term algorithms can be performed with proximate accuracy (like chemical accuracy for quantum chemistry problems). While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development. Demonstrations of these 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, 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 code-sign, in which the most important noise sources in specific applications, like quantum chemistry, become foci of improvement in subsequent hardware generations.

Electronic control systems used for quantum computing have become increasingly complex as multiple qubit technologies employ larger numbers of qubits with higher fidelity target. Whereas the control systems for different technologies share some similarities, parameters, such as pulse duration, throughput, real-time feedback, and latency requirements, vary widely depending on the qubit type. In this article, we evaluate the performance of modern system-on-chip (SoC) architectures in meeting the control demands associated with performing quantum gates on trapped-ion qubits, particularly focusing on communication within the SoC. A principal focus of this article is the data transfer latency and throughput of several high-speed on-chip mechanisms on Xilinx multiprocessor SoCs, including those that utilize direct memory access (DMA). They are measured and evaluated to determine an upper bound on the time required to reconfigure a gate parameter. Worst-case and average-case bandwidth requirements for a custom gate sequencer core are compared with the experimental results. The lowest variability, highest throughput data-transfer mechanism is DMA between the real-time processing unit (RPU) and the programmable logic, where bandwidths up to 19.2 GB/s are possible. For context, this enables the reconfiguration of qubit gates in less than 2 μs, comparable to the fastest gate time. Though this article focuses on trapped-ion control systems, the gate abstraction scheme and measured communication rates are applicable to a broad range of quantum computing technologies.

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 sample-efficient verification of continuously-parameterized quantum gates for small quantum processors of up to approximately 10 qubits. 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. This provides an experimentally-relevant advantage in sample efficiency when estimating the fidelity loss to some desired precision. We describe the experimental realization of this technique using continuously-parameterized quantum gate sets on a trapped-ion quantum processor from Sandia QSCOUT and a superconducting quantum processor from IBM Q, and we demonstrate the sample efficiency advantage of this technique both numerically and experimentally.

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Quantum information processing has reached an inflection point, transitioning from proof-of-principle scientific experiments to small, noisy quantum processors. To accelerate this process and eventually move to fault-tolerant quantum computing, it is necessary to provide the scientific community with access to whitebox testbed systems. The Quantum Scientific Computing Open User Testbed (QSCOUT) provides scientists unique access to an innovative system to help advance quantum computing science.

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In this report we describe the testing of a novel scheme for state preparation of trapped ions in a quantum computing setup. This technique optimally would allow for similar precision and speed of state preparation while allowing for individual addressability of single ions in a chain using technology already available in a trapped ion experiment. As quantum computing experiments become more complicated, mid-experiment measurements will become necessary to achieve algorithms such as quantum error correction. Any mid-experiment measurement then requires the measured qubit to be re-prepared to a known quantum state. Currently this involves the protected qubits to be moved a sizeable distance away from the qubit being re-prepared which can be costly in terms of experiment length as well as introducing errors. Theoretical calculations predict that a three-photon process would allow for state preparation without qubit movement with similar efficiencies to current state preparation methods.

Scalable coherent control hardware for quantum information platforms is rapidly growing in priority as their number of available qubits continues to increase. As these systems scale, more calibration steps are needed, leading to challenges with system instability as calibrated parameters drift. Moreover, the sheer amount of data required to run circuits with large depth tends to balloon, especially when implementing state-of-the-art dynamical-decoupling gates which require advanced modulation techniques. We present a control system that addresses these challenges for trapped-ion systems, through a combination of novel features that eliminate the need for manual bookkeeping, reduction in data transfer bandwidth requirements via gate compression schemes, and other automated error handling techniques. Moreover, we describe an embedded pulse compiler that applies staged optimization, including compressed intermediate representations of parsed output products, performs in-situ mutation of compressed gate data to support high-level algorithmic feedback to account for drift, and can be run entirely on chip.

At Sandia National Laboratories, QSCOUT (the Quantum Scientific Computing Open User Testbed) is an ion-trap based quantum computer built for the purpose of allowing users low-level access to quantum hardware. Commands are executed on the hardware using Jaqal (Just Another Quantum Assembly Language), a programming language designed in-house to support the unique capabilities of QSCOUT. In this work, we describe a batching implementation of our custom software that speeds the experimental run-time through the reduction of communication and upload times. Reducing the code upload time during experimental runs improves system performance by mitigating the effects of drift. We demonstrate this implementation through a set of quantum chemistry experiments using a variational quantum eigensolver (VQE). While developed specifically for this testbed, this idea finds application across many similar experimental platforms that seek greater hardware control or reduced overhead.

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The Quantum Scientific Computing Open User Testbed (QSCOUT) at Sandia National Laboratories is a trapped-ion qubit system designed to evaluate the potential of near-term quantum hardware in scientific computing applications for the U.S. Department of Energy and its Advanced Scientific Computing Research program. Similar to commercially available platforms, it offers quantum hardware that researchers can use to perform quantum algorithms, investigate noise properties unique to quantum systems, and test novel ideas that will be useful for larger and more powerful systems in the future. However, unlike most other quantum computing testbeds, the QSCOUT allows both quantum circuit and low-level pulse control access to study new modes of programming and optimization. The purpose of this article is to provide users and the general community with details of the QSCOUT hardware and its interface, enabling them to take maximum advantage of its capabilities.

If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.

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Nearly every protocol used to analyze the performance of quantum information processors is based on an assumption that the errors experienced by the device during logical operations are constant in time and are insensitive to external contexts. This assumption is pervasive, rarely stated, and almost always wrong. Quantum devices that do behave this way are termed "Markovian:' but nearly every system we have ever probed has displayed drift or crosstalk or memory effects they are all non-Markovian. Strong non-Markovianity introduces spurious effects in characterization protocols and violates assumptions of the fault-tolerance threshold theorems. This SAND report details a three year laboratory-directed research and development (LDRD) project entitled, "Diagnosing and Destroying non-Markovian Noise in Quantum Information Processors." This program was initiated to build tools to study non-Markovian dynamics and quantum systems and develop robust methodologies for eliminating it. The program achieved a number of notable successes, including the first statistically rigorous protocol for identifying and characterizing drift in quantum systems, a formalism for modeling memory effects in quantum devices, and the successful suppression of drift in a Sandia trapped-ion quantum processor.

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QSCOUT is the Quantum Scientific Computing Open User Testbed, a trapped-ion quantum computer testbed realized at Sandia National Laboratories on behalf of the Department of Energy's Office of Science and its Advanced Scientific Computing Research (ASCR) program.

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We demonstrate an experimental implementation of robust phase estimation (RPE) to learn the phase of a single-qubit rotation on a trapped Yb+ ion qubit. We show this phase can be estimated with an uncertainty below 4×10-4 rad using as few as 176 total experimental samples, and our estimates exhibit Heisenberg scaling. Unlike standard phase estimation protocols, RPE neither assumes perfect state preparation and measurement, nor requires access to ancillae. We crossvalidate the results of RPE with the more resource-intensive protocol of gate set tomography.

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In recent years, advanced network analytics have become increasingly important to na- tional security with applications ranging from cyber security to detection and disruption of ter- rorist networks. While classical computing solutions have received considerable investment, the development of quantum algorithms to address problems, such as data mining of attributed relational graphs, is a largely unexplored space. Recent theoretical work has shown that quan- tum algorithms for graph analysis can be more efficient than their classical counterparts. Here, we have implemented a trapped-ion-based two-qubit quantum information proces- sor to address these goals. Building on Sandia's microfabricated silicon surface ion traps, we have designed, realized and characterized a quantum information processor using the hyperfine qubits encoded in two 171 Yb + ions. We have implemented single qubit gates using resonant microwave radiation and have employed Gate set tomography (GST) to characterize the quan- tum process. For the first time, we were able to prove that the quantum process surpasses the fault tolerance thresholds of some quantum codes by demonstrating a diamond norm distance of less than 1 . 9 x 10 [?] 4 . We used Raman transitions in order to manipulate the trapped ions' motion and realize two-qubit gates. We characterized the implemented motion sensitive and insensitive single qubit processes and achieved a maximal process infidelity of 6 . 5 x 10 [?] 5 . We implemented the two-qubit gate proposed by Molmer and Sorensen and achieved a fidelity of more than 97 . 7%.

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