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Sai Sanjay Narayanan

Publications and source records attributed to Sai Sanjay Narayanan.

2 recordsLinked to original sources

Limited Parallelization in Gate Operations Leads to Higher Space Overhead and Lower Noise Threshold

In a modern error corrected quantum memory or circuit, parallelization of gate operations is severely restricted due to issues like cross-talk. Hence, there are enough idle qubits not undergoing gate operations either during the computation phase or during the error correction phase, which suffer further decoherence while waiting. Thus, in reality, the space overhead and the noise threshold would depend on the level of gate parallelization. In this paper, we obtain an analytical lower bound on the required space overhead in terms of the level of parallelization for an error correction framework that has more error correction capability than the existing ones. We consider two types of errors: i.i.d. erasure and depolarization. In comparison to the known lower bounds which assume full gate parallelization, our bound is provably strictly larger despite allowing more capability to the error correction framework. This shows the steep price to be paid for lack of gate parallelization. An implication of the bound is that the noise or decoherence threshold, i.e., the noise beyond which no fault-tolerant memory or circuit can be realized, vanishes if the number of parallel gate operations does not scale linearly with the number of physical qubits.

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Optimum Beamforming and Grating Lobe Mitigation for Intelligent Reflecting Surfaces

Ensuring adequate wireless coverage in upcoming communication technologies such as 6G is expected to be challenging. This is because user demands of higher datarate require an increase in carrier frequencies, which in turn reduce the diffraction effects (and hence coverage) in complex multipath environments. Intelligent reflecting surfaces have been proposed as a way of restoring coverage by adaptively reflecting incoming electromagnetic waves in desired directions. This is accomplished by judiciously adding extra phases at different points on the surface. In practice, these extra phases are only available in discrete quantities due to hardware constraints. Computing these extra phases is computationally challenging when they can only be picked from a discrete distribution, and existing approaches for solving this problem were either heuristic or based on evolutionary algorithms. We solve this problem by proposing fast algorithms with provably optimal solutions. Our algorithms have linear complexity, and are presented with rigorous proofs for their optimality. We show that the proposed algorithms exhibit better performance. We analyze situations when unwanted grating lobes arise in the radiation pattern, and discuss mitigation strategies, such as the use of triangular lattices and prephasing techniques, to eliminate them. We also demonstrate how our algorithms can leverage these techniques to deliver optimum beamforming solutions.

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