Stochastic quantum adiabatic algorithm with fractional Brownian motion

• URL: http://arxiv.org/abs/2504.19801v1
• Date: Mon, 28 Apr 2025 13:51:16 GMT
• Title: Stochastic quantum adiabatic algorithm with fractional Brownian motion
• Authors: Osanda Chinthila, Pani W. Fernando, Anuradha Mahasinghe, Kaushika De Silva, Sarath Kumara,
• Abstract summary: We investigate the counterintuitive hypothesis that incorporating noise, specifically noise driven by fractional Brownian motion, in a non-Markovian setup can enhance the performance of adiabatic quantum computing.<n>Although simulations are limited to problems involving a modest number of qubits, evidence suggests that the proposed approach scales favorably with the system size.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adiabatic Quantum Computing relies on the quantum adiabatic theorem, which states that a quantum system evolves along its ground state with time if the governing Hamiltonian varies infinitely slowly. However, practical limitations force computations to be performed within limited times, exposing the system to transitions into excited states, and thereby reducing the success probability. Here we investigate the counterintuitive hypothesis that incorporating stochastic noise, specifically noise driven by fractional Brownian motion, in a non-Markovian setup can enhance the performance of adiabatic quantum computing by improving its success probability at limited evolution times. The study begins by developing the mathematical framework to introduce stochastic noise multiplicatively into the Schr\"{o}dinger equation, resulting in a stochastic Schr\"{o}dinger equation. To preserve It\^{o} integrability within the non-Markovian framework, a semimartingale approximation for fractional Brownian motion is employed. We perform numerical simulations to compare the performance of the quantum adiabatic algorithm with and without noise driven by fractional Brownian motion using the NP-complete Exact Cover-3 problem, transformed into the Ising model. Our results exhibit an improvement in success probability in the presence of noise driven by fractional Brownian motion with Hurst parameter $0<H<\frac{1}{2}$ and an increase in speedup as $H$ approaches 0. Although simulations are limited to problems involving a modest number of qubits, evidence suggests that the proposed approach scales favorably with the system size.

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