Dr Mustafa Bakr

Abstract:

One major challenge that arises from quantum computing is to implement fast, high-accuracy quantum state readout. For superconducting circuits, this problem reduces to a time series classification problem on readout signals. We propose that using path signature methods to extract features can enhance existing techniques for quantum state discrimination. We demonstrate the superior performance of our proposed approach over conventional methods in distinguishing three different quantum states on real experimental data from a superconducting transmon qubit.

Abstract:

Using quantum systems with more than two levels, or qudits, can scale the computational space of quantum processors more efficiently than using qubits, which may offer an easier physical implementation for larger Hilbert spaces. However, individual qudits may exhibit larger noise, and algorithms designed for qubits require to be recompiled to qudit algorithms for execution. In this work, we implemented a two-qubit emulator using a 4-level superconducting transmon qudit for variational quantum algorithm applications and analyzed its noise model. The major source of error for the variational algorithm was readout misclassification error and amplitude damping. To improve the accuracy of the results, we applied error-mitigation techniques to reduce the effects of the misclassification and qudit decay event. The final predicted energy value is within the range of chemical accuracy.