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Strengths and Weaknesses of Quantum Computing

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arxiv quant-ph/9701001 v1 pith:JAHSY5UP submitted 1997-01-01 quant-ph

classification quant-ph
keywords quantumtimeclassoraclerelativesolvedcannotchosen
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Recently a great deal of attention has focused on quantum computation following a sequence of results suggesting that quantum computers are more powerful than classical probabilistic computers. Following Shor's result that factoring and the extraction of discrete logarithms are both solvable in quantum polynomial time, it is natural to ask whether all of NP can be efficiently solved in quantum polynomial time. In this paper, we address this question by proving that relative to an oracle chosen uniformly at random, with probability 1, the class NP cannot be solved on a quantum Turing machine in time $o(2^{n/2})$. We also show that relative to a permutation oracle chosen uniformly at random, with probability 1, the class $NP \cap coNP$ cannot be solved on a quantum Turing machine in time $o(2^{n/3})$. The former bound is tight since recent work of Grover shows how to accept the class NP relative to any oracle on a quantum computer in time $O(2^{n/2})$.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Non-Hermitian Quantum Adiabatic Algorithm

    quant-ph 2026-07 conditional novelty 7.0 of 10

    A history-decoupled Hamiltonian mapping makes non-Hermitian adiabatic quantum optimization pseudospectrally stable, achieving polynomial-time (per configuration) evolution on the CK maximum-independent-set benchmarks.

  2. Nested Grover's Algorithm for Tree Search

    quant-ph 2025-09 reject novelty 4.0 of 10

    A nested Grover algorithm for tree search claims cost O(m*2^(m/4)) using partial candidate solutions, but the speedup relies on an unexamined assumption that the candidate set contains the solution.

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