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Quantum Science and Technology

QAOA pseudo-Boltzmann sampling beyond the rapid-mixing regime on superconducting quantum computers

Feliks Kivelä, Pablo Díez-Valle, Juan José García‐Ripoll, Gheorghe Sorin Paraoanu

Published Sep 17, 2026• 0 citations
Publisher

Abstract

Abstract The quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm developed for solving combinatorial optimization problems. It has also been shown to generate pseudo-Boltzmann distributions, with potential applications in various fields of computer science and computational physics. In this work, we build upon previous theoretical results and demonstrate pseudo-Boltzmann sampling with single-layer QAOA on real superconducting quantum computers. We show that the pseudo-Boltzmann distribution and the ability to adjust its effective temperature survive the accumulated errors of a real, noisy superconducting quantum processor. On an IBM processor, we are able to reach temperatures below a classical minimum-temperature boundary that guarantees the rapid mixing of single-site Glauber dynamics. We also run controlled simulations of QAOA with various noise models to characterize how increasing error rates raises the effective temperature of the distribution and limits the lowest accessible temperature.

DOI
10.1088/2058-9565/aea92a

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Abstract The quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm developed for solving combinatorial optimization problems. It has also been shown to generate pseudo-Boltzmann distributions, with potential applications in various fields of computer science and computational physics. In this work, we build upon previous theoretical results and demonstrate pseudo-Boltzmann sampling with single-layer QAOA on real superconducting quantum computers. We show that the pseudo-Boltzmann distribution and the ability to adjust its effective temperature survive the accumulated errors of a real, noisy superconducting quantum processor. On an IBM processor, we are able to reach temperatures below a classical minimum-temperature boundary that guarantees the rapid mixing of single-site Glauber dynamics. We also run controlled simulations of QAOA with various noise models to characterize how increasing error rates raises the effective temperature of the distribution and limits the lowest accessible temperature.
The quantum approximate optimization algorithm (QAOA) was originally proposed to find approximate solutions to combinatorial optimization problems on quantum computers. However, the algorithm has also attracted interest for sampling purposes since it was theoretically demonstrated under reasonable complexity assumptions that one layer of the algorithm already engineers a probability distribution beyond what can be simulated by classical computers. In this regard, a recent study has also shown that, in universal Ising models, this global probability distribution resembles pure but thermal-like distributions at a temperature that depends on the internal correlations of the spin model. In this work, through an interferometric interpretation of the algorithm, we extend the theoretical derivation of the amplitudes of the eigenstates and the Boltzmann distributions generated by a single-layer QAOA. We also review the implications of this behavior from practical and fundamental perspectives.
The quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid technique to solve combinatorial optimization problems in near-term gate-based noisy quantum devices. In QAOA, the objective is a function of the quantum state, which itself is a function of the gate parameters of a multi-level parameterized quantum circuit (PQC). A classical optimizer varies the continuous gate parameters to generate distributions (quantum state) with significant support to the optimal solution. Even at the lowest circuit depth, QAOA offers non-trivial provable performance guarantee which is expected to increase with the circuit depth. However, the existing analysis fails to consider non-idealities in the qubit quality i.e., short lifetime and imperfect gate operations in realistic quantum hardware. In this article, we investigate the impact of various noise sources on the performance of QAOA both in simulation and on a real quantum computer from IBM. Our analyses indicate that the optimal number of stages (p-value) for any QAOA instance is limited by the noise characteristics (gate error, coherence time, etc.) of the target hardware as opposed to the current perception that higher-depth QAOA will provide monotonically better performance for a given problem compared to the low-depth implementations.
κκκ-Parametrized Boltzmann Distribution Sampling on Quantum Approximate Optimization Algorithm
Generative models use training data to produce new samples that resemble the original, with applications in classification and recommendation systems. Restricted Boltzmann Machines (RBMs) are at the core of generative models but suffer from the computational intractability of training, which relies on sampling an unknown Boltzmann Distribution (BD). The Quantum Approximate Optimization Algorithm (QAOA) can assist RBM training, potentially providing benefits over strictly classical strategies. One particular problem is determining the quantum circuit depth for an arbitrary problem size that yields the expected sampling results. In this study, we evaluate QAOA-assisted sampling of BDs using Qiskit's Aer Simulator by analysing the inverse effective temperature of the distribution, κκκ, for tractable problem sizes. The results provide numerical evidence, based on Qiskit Aer simulations, that parameterized annealing-scheduled QAOA (PAQ-QAOA) can approximate RBM Boltzmann sampling for the tractable instances considered, while hardware performance and scaling remain open questions.
In this letter, we provide analytical and numerical evidence that the single-layer Quantum Approximate Optimization Algorithm (QAOA) on universal Ising spin models produces thermal-like states with Gaussian perturbations. We find that these pseudo-Boltzmann states can not be efficiently simulated on classical computers according to state-of-art techniques, and we relate this distribution to the optimization potential of QAOA. Moreover, we observe that the temperature depends on a hidden universal correlation between the energy of a state and the covariance of other energy levels and the Hamming distances of the state to those energies.
The Quantum Approximate Optimization Algorithm (QAOA) is designed to run on a gate model quantum computer and has shallow depth. It takes as input a combinatorial optimization problem and outputs a string that satisfies a high fraction of the maximum number of clauses that can be satisfied. For certain problems the lowest depth version of the QAOA has provable performance guarantees although there exist classical algorithms that have better guarantees. Here we argue that beyond its possible computational value the QAOA can exhibit a form of Quantum Supremacy in that, based on reasonable complexity theoretic assumptions, the output distribution of even the lowest depth version cannot be efficiently simulated on any classical device. We contrast this with the case of sampling from the output of a quantum computer running the Quantum Adiabatic Algorithm (QADI) with the restriction that the Hamiltonian that governs the evolution is gapped and stoquastic. Here we show that there is an oracle that would allow sampling from the QADI but even with this oracle, if one could efficiently classically sample from the output of the QAOA, the Polynomial Hierarchy would collapse. This suggests that the QAOA is an excellent candidate to run on near term quantum computers not only because it may be of use for optimization but also because of its potential as a route to establishing quantum supremacy.
This study numerically investigates the thermal sampling properties of QAOA, the Quantum Alternating Operator Ansatz which was generalized from the original Quantum Approximate Optimization Algorithm. Specifically, the ability of QAOA to sample from the Gibbs distribution, equivalently the Boltzmann distribution, defined by a classical Ising model, specifically a fully connected disordered spin glass (Sherrington-Kirkpatrick) model. We focus on two different QAOA mixers; the standard transverse field X mixer, and the Grover mixer. At a QAOA depth of one we examine, for a single full QAOA parameter search space period, the energy landscape, the Shannon entropy landscape of the QAOA probability distribution, and the tradeoff between Boltzmann distribution sampling temperature and error rate (how close to the true Boltzmann distribution is the QAOA distribution). We find that at very high temperatures one-round Grover mixer QAOA can sample from the Boltzmann distribution more accurately than the standard X mixer QAOA at one round. Both X mixer and Grover mixer depth one QAOA can serve as approximate Boltzmann distribution samplers, and how good this approximation is depends heavily on the QAOA angle choice.