Improving Quantum Computation With Classical Machine Learning
One of the primary challenges for the realization of near-term quantum computers has to do with their most basic constituent: the qubit. Qubits can interact with anything in close proximity that carries energyclose to their own—stray photons (i.e., unwanted electromagnetic fields), phonons (mechanical oscillations of the quantum device), or quantum defects (irregularities in the substrate of the chip formed during manufacturing)—which can unpredictably change the state of the qubits themselves.
Further complicating matters, there are numerous challenges posed by the tools used to control qubits. Manipulating and reading out qubits is performed via classical controls: analog signals in the form of electromagnetic fields coupled to a physical substrate in which the qubit is embedded, e.g., superconducting circuits. Imperfections in these control electronics (giving rise to white noise), interference from external sources of radiation, and fluctuations in digital-to-analog converters, introduce even more stochastic errors that degrade the performance of quantum circuits. These practical issues impact the fidelity of the computation and thus limit the applications of near-term quantum devices.
To improve the computational capacity of quantum computers, and to pave the road towards large-scale quantum computation, it is necessary to first build physical models that accurately describe these experimental problems.
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