Abstract: A method, system, and computer program product for efficient noise model learning considering crosstalk. 2-qubit gate interactions of a quantum circuit in the presence of crosstalk are analyzed. Such an analysis may determine the direction that the crosstalk effects spread in the resulting noise model. The quantum circuit may then be decomposed into individual, unique layers. A subset of such layers (“abbreviated layers”) may be generated, where such abbreviated layers include an idle layer (layer where all qubits are idle) and one or more dense layers (layer where gates are densely packed without neighboring idle qubits) to achieve minimal graph coloring for hardware connectivity. Noise models may then be learned from such abbreviated layers. A noise model from the parameters (noise model parameters) of the learned noise models are stitched together to form a noise model for one or more of the individual, unique layers of the decomposed quantum circuit.

Abstract: A method, system, and computer program product for learning noise models to perform quantum error mitigation. Each target layer of a quantum circuit is divided into a set of sub-layers. Each of the sub-layers for each target layer of the quantum circuit is grouped into a reduced set of learning layers, which enables each sub-layer's noise model to be learned from fewer layers (learning layers). A learning layer refers to a layer that is used in combination with other learning layers to form the minimally complete layer set for learning all the layer components used in the quantum circuit. The noise models for each of the sub-layers are then learned on the reduced set of learning layers. Such learned noise models are combined to form a complete set of noise models for the target layers of the quantum circuit and used to perform quantum error mitigation on the quantum circuit.

Abstract: A system to characterize noise of a quantum gate can comprise a memory that stores, and a processor that executes, computer executable components that perform operations comprising generating a quantum circuit comprising a series of one or more instances of a quantum gate, wherein each instance is bounded by a pair of Pauli gates comprising two of the same bounding Pauli gate, selecting, separately for each instance, and employing a partial randomness, the bounding Pauli gate to employ, selecting an initial Pauli gate as Pi from a state-based set of Pauli gates where Pi and Pj?(A?B)Pi both commute with a rotation axis A?B of the quantum gate, wherein A, B?{X, Y, Z}, and, characterizing the noise based on one or more parameters of a curve, to which an expectation value, resulting from (i) a measurement of execution of the quantum circuit and (ii) readout twirling of the initial Pauli gate, is fitted.