Abstract: A method of mitigating quantum readout errors by stochastic matrix inversion includes performing a plurality of quantum measurements on a plurality of qubits having predetermined plurality of states to obtain a plurality of measurement outputs; selecting a model for a matrix linking the predetermined plurality of states to the plurality of measurement outputs, the model having a plurality of model parameters, wherein a number of the plurality of model parameters grows less than exponentially with a number of the plurality of qubits; training the model parameters to minimize a loss function that compares predictions of the model with the matrix; computing an inverse of the model based on the trained model parameters; and providing the computed inverse of the model to a noise prone quantum readout of the plurality of qubits to obtain a substantially noise free quantum readout.

Abstract: A method of mitigating quantum readout errors by stochastic matrix inversion includes performing a plurality of quantum measurements on a plurality of qubits having predetermined plurality of states to obtain a plurality of measurement outputs; selecting a model for a matrix linking the predetermined plurality of states to the plurality of measurement outputs, the model having a plurality of model parameters, wherein a number of the plurality of model parameters grows less than exponentially with a number of the plurality of qubits; training the model parameters to minimize a loss function that compares predictions of the model with the matrix; computing an inverse of the model based on the trained model parameters; and providing the computed inverse of the model to a noise prone quantum readout of the plurality of qubits to obtain a substantially noise free quantum readout.

Abstract: A method of mitigating quantum readout errors by stochastic matrix inversion includes performing a plurality of quantum measurements on a plurality of qubits having predetermined plurality of states to obtain a plurality of measurement outputs; selecting a model for a matrix linking the predetermined plurality of states to the plurality of measurement outputs, the model having a plurality of model parameters, wherein a number of the plurality of model parameters grows less than exponentially with a number of the plurality of qubits; training the model parameters to minimize a loss function that compares predictions of the model with the matrix; computing an inverse of the model based on the trained model parameters; and providing the computed inverse of the model to a noise prone quantum readout of the plurality of qubits to obtain a substantially noise free quantum readout.

Abstract: In a system including a cross-resonance gate having a superconducting control qubit and having a superconducting target qubit coupled through a bus resonator, echo pulses are generated at a first frequency and directed to the control qubit, wherein the first frequency is on resonance with the control qubit. Cross-resonance pulses are generated at a second frequency on resonance with the target qubit and applied to the control qubit, wherein the generating and applying the cross-resonance pulses induce rotations on the target qubit through an interaction that is mediated by the bus resonator. Cancellation pulses are generated at the second frequency and applied to the target qubit. Sets of Hamiltonian tomographies may be measured to determine appropriate amplitudes and phases of the cross-resonance and cancellation pulses.

Abstract: A method of mitigating quantum readout errors by stochastic matrix inversion includes performing a plurality of quantum measurements on a plurality of qubits having predetermined plurality of states to obtain a plurality of measurement outputs; selecting a model for a matrix linking the predetermined plurality of states to the plurality of measurement outputs, the model having a plurality of model parameters, wherein a number of the plurality of model parameters grows less than exponentially with a number of the plurality of qubits; training the model parameters to minimize a loss function that compares predictions of the model with the matrix; computing an inverse of the model based on the trained model parameters; and providing the computed inverse of the model to a noise prone quantum readout of the plurality of qubits to obtain a substantially noise free quantum readout.

Abstract: In a system including a cross-resonance gate having a superconducting control qubit and having a superconducting target qubit coupled through a bus resonator, echo pulses are generated at a first frequency and directed to the control qubit, wherein the first frequency is on resonance with the control qubit. Cross-resonance pulses are generated at a second frequency on resonance with the target qubit and applied to the control qubit, wherein the generating and applying the cross-resonance pulses induce rotations on the target qubit through an interaction that is mediated by the bus resonator. Cancellation pulses are generated at the second frequency and applied to the target qubit. Sets of Hamiltonian tomographies may be measured to determine appropriate amplitudes and phases of the cross-resonance and cancellation pulses.