Abstract: Systems, computer-implemented methods or computer program products to facilitate mitigating quantum errors associated with one or more quantum gates. A noise modeling component can generate a sparse error model of noise associated with one or more quantum gates; employ the sparse error model; and draw samples from an inverse noise model. An insertion component can insert the samples to mitigate errors associated with the one or more quantum gates. The insertion component can reduce the noise by running circuit instances augmented with samples from the inverse noise model. The noise modeling component includes a noise shaping component that can shape the noise affecting one or more quantum gates by twirling to form a Pauli channel.

Abstract: Systems and techniques that facilitate Hamiltonian simulation based on simultaneous-diagonalization are provided. In various embodiments, a partition component can partition one or more Pauli operators of a Hamiltonian into one or more subsets of commuting Pauli operators. In various embodiments, a diagonalization component can generate one or more simultaneous-diagonalization circuits corresponding to the one or more subsets. In various aspects, a one of the one or more simultaneous-diagonalization circuits can diagonalize the commuting Pauli operators in a corresponding one of the one or more subsets. In various embodiments, an exponentiation component can generate one or more exponentiation circuits corresponding to the one or more subsets. In various aspects, a one of the one or more exponentiation circuits can exponentiate the simultaneously diagonalized commuting Pauli operators in a corresponding one of the one or more subsets.

Abstract: Techniques for mitigating readout error for quantum expectation are presented. Calibration component applies first random Pauli gates to qubits at first output of first circuit prior to first readout measurements of the qubits. Estimation component applies second random Pauli gates to qubits at second output of second circuit prior to second readout measurements of the qubits, and generates an error-mitigated readout determination based on first random Pauli gates applied to qubits at first circuit output and second random Pauli gates applied to qubits at second circuit output. Calibration component determines calibration data based on first readout measurements. Estimation component determines estimation data based on second readout measurements.

Abstract: A method of determining a quantum phase of quantum device including performing a plurality of measurements for cosine and sine components of the quantum phase; counting a number of measurements in a vertical axis for the sine component and counting a number of measurements in a horizontal axis for the cosine component; and determining the quantum phase based on a majority of a number of 0 measurements and a number of 1 measurements of the sine component and the cosine component.

Abstract: Systems and techniques that facilitate Hamiltonian simulation based on simultaneous-diagonalization are provided. In various embodiments, a partition component can partition one or more Pauli operators of a Hamiltonian into one or more subsets of commuting Pauli operators. In various embodiments, a diagonalization component can generate one or more simultaneous-diagonalization circuits corresponding to the one or more subsets. In various aspects, a one of the one or more simultaneous-diagonalization circuits can diagonalize the commuting Pauli operators in a corresponding one of the one or more subsets. In various embodiments, an exponentiation component can generate one or more exponentiation circuits corresponding to the one or more subsets. In various aspects, a one of the one or more exponentiation circuits can exponentiate the simultaneously diagonalized commuting Pauli operators in a corresponding one of the one or more subsets.

Abstract: A method of determining a quantum phase of quantum device including performing a plurality of measurements for cosine and sine components of the quantum phase; counting a number of measurements in a vertical axis for the sine component and counting a number of measurements in a horizontal axis for the cosine component; and determining the quantum phase based on a majority of a number of 0 measurements and a number of 1 measurements of the sine component and the cosine component.

Abstract: Systems and methods for training a neural network to optimize network performance, including sampling an applied dropout rate for one or more nodes of the network to evaluate a current generalization performance of one or more training models. An optimized annealing schedule may be generated based on the sampling, wherein the optimized annealing schedule includes an altered dropout rate configured to improve a generalization performance of the network. A number of nodes of the network may be adjusted in accordance with a dropout rate specified in the optimized annealing schedule. The steps may then be iterated until the generalization performance of the network is maximized.

Abstract: Systems and methods for training a neural network to optimize network performance, including sampling an applied dropout rate for one or more nodes of the network to evaluate a current generalization performance of one or more training models. An optimized annealing schedule may be generated based on the sampling, wherein the optimized annealing schedule includes an altered dropout rate configured to improve a generalization performance of the network. A number of nodes of the network may be adjusted in accordance with a dropout rate specified in the optimized annealing schedule. The steps may then be iterated until the generalization performance of the network is maximized.

Abstract: Systems and methods for training a neural network to optimize network performance, including sampling an applied dropout rate for one or more nodes of the network to evaluate a current generalization performance of one or more training models. An optimized annealing schedule may be generated based on the sampling, wherein the optimized annealing schedule includes an altered dropout rate configured to improve a generalization performance of the network. A number of nodes of the network may be adjusted in accordance with a dropout rate specified in the optimized annealing schedule. The steps may then be iterated until the generalization performance of the network is maximized.

Abstract: Systems and methods for training a neural network to optimize network performance, including sampling an applied dropout rate for one or more nodes of the network to evaluate a current generalization performance of one or more training models. An optimized annealing schedule may be generated based on the sampling, wherein the optimized annealing schedule includes an altered dropout rate configured to improve a generalization performance of the network. A number of nodes of the network may be adjusted in accordance with a dropout rate specified in the optimized annealing schedule. The steps may then be iterated until the generalization performance of the network is maximized.

Abstract: A method for providing an image from a device with a plurality of sensors and a plurality of time to digital converters (TDC) is provided. Data signals are generated by some of the plurality of sensors, wherein each sensor of the plurality of sensors provides output in parallel to more than one TDC of the plurality of TDCs and wherein each TDC of the plurality of TDCs receives in parallel input from more than one sensor of the plurality of sensors and where a binary matrix indicates which sensors are connected to which TDC. The data signals are transmitted from the sensors to the TDCs. TDC signals are generated from the data signals. Group testing is used to decode the TDC signals based on the binary matrix.

Abstract: A method for providing an image from a device with a plurality of sensors and a plurality of time to digital converters (TDC) is provided. Data signals are generated by some of the plurality of sensors, wherein each sensor of the plurality of sensors provides output in parallel to more than one TDC of the plurality of TDCs and wherein each TDC of the plurality of TDCs receives in parallel input from more than one sensor of the plurality of sensors and where a binary matrix indicates which sensors are connected to which TDC. The data signals are transmitted from the sensors to the TDCs. TDC signals are generated from the data signals. Group testing is used to decode the TDC signals based on the binary matrix.