Productionizing Quantum Mass Production
William J. Huggins, Tanuj Khattar, Nathan Wiebe

TL;DR
This paper introduces quantum mass production techniques combined with QROM to significantly reduce the cost of data loading in quantum computing, enabling more efficient algorithms for quantum chemistry and data reuse.
Contribution
It presents a novel approach to quantum data loading that polynomially reduces gate complexity and demonstrates practical applications in quantum algorithms.
Findings
Polynomial reduction in data loading gates
Order-of-magnitude cost reduction in realistic models
Improved quantum eigenvalue estimation algorithms
Abstract
For many practical applications of quantum computing, the most costly steps involve coherently accessing classical data. We help address this challenge by applying mass production techniques, which can reduce the cost of applying an operation multiple times in parallel. We combine these techniques with modern approaches for classical data loading based on "quantum read-only memory" (QROM). We find that we can polynomially reduce the total number of gates required for data loading, but we find no advantage in cost models that only count the number of non-Clifford gates. Furthermore, for realistic cost models and problem sizes, we find that it is possible to reduce the cost of parallel data loading by an order of magnitude or more. We present several applications of quantum mass production, including a scheme that uses parallel phase estimation to asymptotically reduce the gate complexity…
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Taxonomy
TopicsLaser-induced spectroscopy and plasma
