01552nas a2200169 4500008004100000245007500041210006900116520100400185100001901189700002301208700002201231700002401253700002401277700002001301700002401321856003701345 2018 eng d00aDemonstration of Bayesian quantum game on an ion trap quantum computer0 aDemonstration of Bayesian quantum game on an ion trap quantum co3 a

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.

1 aSolmeyer, Neal1 aLinke, Norbert, M.1 aFiggatt, Caroline1 aLandsman, Kevin, A.1 aBalu, Radhakrishnan1 aSiopsis, George1 aMonroe, Christopher uhttps://arxiv.org/abs/1802.0811601356nas a2200181 4500008004100000245006000041210005900101260001500160490000700175520083200182100001801014700002401032700002301056700002201079700001501101700002101116856003701137 2018 eng d00aMachine learning assisted readout of trapped-ion qubits0 aMachine learning assisted readout of trappedion qubits c2018/05/010 v513 a

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.

1 aSeif, Alireza1 aLandsman, Kevin, A.1 aLinke, Norbert, M.1 aFiggatt, Caroline1 aMonroe, C.1 aHafezi, Mohammad uhttps://arxiv.org/abs/1804.0771801532nas a2200193 4500008004100000245009300041210006900134260001500203300001100218490000800229520089100237100002101128700002401149700002201173700002301195700002401218700002301242856007301265 2018 eng d00aRobust two-qubit gates in a linear ion crystal using a frequency-modulated driving force0 aRobust twoqubit gates in a linear ion crystal using a frequencym c2018/01/09 a0205010 v1203 a

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 Mølmer-Sørensen 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.

1 aLeung, Pak, Hong1 aLandsman, Kevin, A.1 aFiggatt, Caroline1 aLinke, Norbert, M.1 aMonroe, Christopher1 aBrown, Kenneth, R. uhttps://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.02050101858nas a2200169 4500008004100000245004400041210004400085520137000129100002401499700002201523700002101545700002301566700001801589700002001607700002401627856003701651 2018 eng d00aVerified Quantum Information Scrambling0 aVerified Quantum Information Scrambling3 a

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.

1 aLandsman, Kevin, A.1 aFiggatt, Caroline1 aSchuster, Thomas1 aLinke, Norbert, M.1 aYoshida, Beni1 aYao, Norman, Y.1 aMonroe, Christopher uhttps://arxiv.org/abs/1806.02807