Unstructured Adiabatic Quantum Optimization: Optimality with Limitations

• URL: http://arxiv.org/abs/2411.05736v1
• Date: Fri, 08 Nov 2024 17:51:18 GMT
• Title: Unstructured Adiabatic Quantum Optimization: Optimality with Limitations
• Authors: Arthur Braida, Shantanav Chakraborty, Alapan Chaudhuri, Joseph Cunningham, Rutvij Menavlikar, Leonardo Novo, Jérémie Roland,
• Abstract summary: We show that adiabatic quantum optimization using an unstructured search approach results in a running time that matches a lower bound for a class of classical local spin Hamiltonians. We show that the position of the avoided crossing is approximately given by a quantity that depends on the degeneracies and inverse gaps of the problem Hamiltonian and is NP-hard to compute even within a low additive precision.
• Score: 0.06022769903412461
• License:
• Abstract: In the circuit model of quantum computing, amplitude amplification techniques can be used to find solutions to NP-hard problems defined on $n$-bits in time $\text{poly}(n) 2^{n/2}$. In this work, we investigate whether such general statements can be made for adiabatic quantum optimization, as provable results regarding its performance are mostly unknown. Although a lower bound of $\Omega(2^{n/2})$ has existed in such a setting for over a decade, a purely adiabatic algorithm with this running time has been absent. We show that adiabatic quantum optimization using an unstructured search approach results in a running time that matches this lower bound (up to a polylogarithmic factor) for a broad class of classical local spin Hamiltonians. For this, it is necessary to bound the spectral gap throughout the adiabatic evolution and compute beforehand the position of the avoided crossing with sufficient precision so as to adapt the adiabatic schedule accordingly. However, we show that the position of the avoided crossing is approximately given by a quantity that depends on the degeneracies and inverse gaps of the problem Hamiltonian and is NP-hard to compute even within a low additive precision. Furthermore, computing it exactly (or nearly exactly) is \#P-hard. Our work indicates a possible limitation of adiabatic quantum optimization algorithms, leaving open the question of whether provable Grover-like speed-ups can be obtained for any optimization problem using this approach.

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