We report results for simulating an effective field theory to compute the binding energy of the deuteron nucleus using a hybrid algorithm on a trapped-ion quantum computer. Two increasingly complex unitary coupled-cluster ansaetze have been used to compute the binding energy to within a few percent for successively more complex Hamiltonians. By increasing the complexity of the Hamiltonian, allowing more terms in the effective field theory expansion and calculating their expectation values, we present a benchmark for quantum computers based on their ability to scalably calculate the effective field theory with increasing accuracy. Our result of E4=\−2.220\±0.179MeV may be compared with the exact Deuteron ground-state energy \−2.224MeV. We also demonstrate an error mitigation technique using Richardson extrapolation on ion traps for the first time. The error mitigation circuit represents a record for deepest quantum circuit on a trapped-ion quantum computer.\

}, url = {https://arxiv.org/abs/1904.04338}, author = {Omar Shehab and Kevin A. Landsman and Yunseong Nam and Daiwei Zhu and Norbert M. Linke and Matthew J. Keesan and Raphael C. Pooser and Christopher R. Monroe} } @article {2412, title = {Two-qubit entangling gates within arbitrarily long chains of trapped ions}, year = {2019}, month = {05/28/2019}, abstract = {Ion trap systems are a leading platform for large scale quantum computers. Trapped ion qubit crystals are fully-connected and reconfigurable, owing to their long range Coulomb interaction that can be modulated with external optical forces. However, the spectral crowding of collective motional modes could pose a challenge to the control of such interactions for large numbers of qubits. Here, we show that high-fidelity quantum gate operations are still possible with very large trapped ion crystals, simplifying the scaling of ion trap quantum computers. To this end, we present analytical work that determines how parallel entangling gates produce a crosstalk error that falls off as the inverse cube of the distance between the pairs. We also show experimental work demonstrating entangling gates on a fully-connected chain of seventeen 171Yb+ ions with fidelities as high as 97(1)\%.

}, url = {https://arxiv.org/abs/1905.10421}, author = {Kevin A. Landsman and Yukai Wu and Pak Hong Leung and Daiwei Zhu and Norbert M. Linke and Kenneth R. Brown and Luming Duan and Christopher R. Monroe} } @article {2288, title = {Demonstration of Bayesian quantum game on an ion trap quantum computer}, year = {2018}, abstract = {We demonstrate a Bayesian quantum game on an ion trap quantum computer with five qubits. The players share an entangled pair of qubits and perform rotations on their qubit as the strategy choice. Two five-qubit circuits are sufficient to run all 16 possible strategy choice sets in a game with four possible strategies. The data are then parsed into player types randomly in order to combine them classically into a Bayesian framework. We exhaustively compute the possible strategies of the game so that the experimental data can be used to solve for the Nash equilibria of the game directly. Then we compare the payoff at the Nash equilibria and location of phase-change-like transitions obtained from the experimental data to the theory, and study how it changes as a function of the amount of entanglement.

}, url = {https://arxiv.org/abs/1802.08116}, author = {Neal Solmeyer and Norbert M. Linke and Caroline Figgatt and Kevin A. Landsman and Radhakrishnan Balu and George Siopsis and Christopher Monroe} } @article {2287, title = {Machine learning assisted readout of trapped-ion qubits}, journal = { J. Phys. B: At. Mol. Opt. Phys.}, volume = {51}, year = {2018}, month = {2018/05/01}, chapter = {174006}, abstract = {We reduce measurement errors in a quantum computer using machine learning techniques. We exploit a simple yet versatile neural network to classify multi-qubit quantum states, which is trained using experimental data. This flexible approach allows the incorporation of any number of features of the data with minimal modifications to the underlying network architecture. We experimentally illustrate this approach in the readout of trapped-ion qubits using additional spatial and temporal features in the data. Using this neural network classifier, we efficiently treat qubit readout crosstalk, resulting in a 30\% improvement in detection error over the conventional threshold method. Our approach does not depend on the specific details of the system and can be readily generalized to other quantum computing platforms.

}, doi = {https://doi.org/10.1088/1361-6455/aad62b}, url = {https://arxiv.org/abs/1804.07718}, author = {Alireza Seif and Kevin A. Landsman and Norbert M. Linke and Caroline Figgatt and C. Monroe and Mohammad Hafezi} } @article {2052, title = {Robust two-qubit gates in a linear ion crystal using a frequency-modulated driving force}, journal = {Physical Review Letters}, volume = {120}, year = {2018}, month = {2018/01/09}, pages = {020501}, abstract = {In an ion trap quantum computer, collective motional modes are used to entangle two or more qubits in order to execute multi-qubit logical gates. Any residual entanglement between the internal and motional states of the ions will result in decoherence errors, especially when there are many spectator ions in the crystal. We propose using a frequency-modulated (FM) driving force to minimize such errors and implement it experimentally. In simulation, we obtained an optimized FM gate that can suppress decoherence to less than 10\−4 and is robust against a frequency drift of more than \±1 kHz. The two-qubit gate was tested in a five-qubit trapped ion crystal, with 98.3(4)\% fidelity for a M{\o}lmer-S{\o}rensen entangling gate and 98.6(7)\% for a controlled-not (CNOT) gate. We also show an optimized FM two-qubit gate for 17 ions, proving the scalability of our method.

}, doi = {10.1103/PhysRevLett.120.020501}, url = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.020501}, author = {Pak Hong Leung and Kevin A. Landsman and Caroline Figgatt and Norbert M. Linke and Christopher Monroe and Kenneth R. Brown} } @article {2252, title = {Verified Quantum Information Scrambling}, year = {2018}, abstract = {Quantum scrambling is the dispersal of local information into many-body quantum entanglements and correlations distributed throughout the entire system. This concept underlies the dynamics of thermalization in closed quantum systems, and more recently has emerged as a powerful tool for characterizing chaos in black holes. However, the direct experimental measurement of quantum scrambling is difficult, owing to the exponential complexity of ergodic many-body entangled states. One way to characterize quantum scrambling is to measure an out-of-time-ordered correlation function (OTOC); however, since scrambling leads to their decay, OTOCs do not generally discriminate between quantum scrambling and ordinary decoherence. Here, we implement a quantum circuit that provides a positive test for the scrambling features of a given unitary process. This approach conditionally teleports a quantum state through the circuit, providing an unambiguous litmus test for scrambling while projecting potential circuit errors into an ancillary observable. We engineer quantum scrambling processes through a tunable 3-qubit unitary operation as part of a 7-qubit circuit on an ion trap quantum computer. Measured teleportation fidelities are typically \∼80\%, and enable us to experimentally bound the scrambling-induced decay of the corresponding OTOC measurement.

}, url = {https://arxiv.org/abs/1806.02807}, author = {Kevin A. Landsman and Caroline Figgatt and Thomas Schuster and Norbert M. Linke and Beni Yoshida and Norman Y. Yao and Christopher Monroe} }