IBM TEM: Hybrid Quantum-Classical Noise Mitigation Explained
A hybrid quantum-classical algorithm, Algorithmiq’s Tensor-network Error Mitigation (IBM TEM) technique is intended to handle noise mitigation solely at the classical post-processing step
As a benefit, IBM TEM uses informationally full measurements to access a large collection of mitigated expectation values of observables and has optimal sampling overhead on quantum technology
There is no technique that can obtain a lower measurement overhead than IBM TEM, which is ideal in terms of theoretical limits
IBM TEM effectively estimates numerous observables using the same measurement data from the quantum computer because of informationally-complete measurements
Digital quantum simulations are more accurate and reliable using IBM TEM, making quantum algorithms more reliable
With little sampling overhead, you can get error-mitigated expectation values for a number of observables on a quantum circuit using the IBM TEM function
The noise model captures delicate aspects, such as qubit cross-talk, and accurately describes the noise on the device
By building a tensor network that depicts the inverse of the global noise channel influencing the state of the quantum processor, IBM TEM seeks to increase accuracy