Review of quantum neural networks on gate-based quantum computers for molecular property prediction and generation in drug discovery.
Synthesis of Quantum Logic Circuits
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abstract
We discuss efficient quantum logic circuits which perform two tasks: (i) implementing generic quantum computations and (ii) initializing quantum registers. In contrast to conventional computing, the latter task is nontrivial because the state-space of an n-qubit register is not finite and contains exponential superpositions of classical bit strings. Our proposed circuits are asymptotically optimal for respective tasks and improve published results by at least a factor of two. The circuits for generic quantum computation constructed by our algorithms are the most efficient known today in terms of the number of expensive gates (quantum controlled-NOTs). They are based on an analogue of the Shannon decomposition of Boolean functions and a new circuit block, quantum multiplexor, that generalizes several known constructions. A theoretical lower bound implies that our circuits cannot be improved by more than a factor of two. We additionally show how to accommodate the severe architectural limitation of using only nearest-neighbor gates that is representative of current implementation technologies. This increases the number of gates by almost an order of magnitude, but preserves the asymptotic optimality of gate counts.
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quant-ph 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
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Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
Review of quantum neural networks on gate-based quantum computers for molecular property prediction and generation in drug discovery.