We show a significant reduction of errors for an architecture of quantum annealers where bosonic modes mediate the interaction between qubits. These systems have a large redundancy in the subspace of solutions, supported by arbitrarily large bosonic occupations. We explain how this redundancy leads to a mitigation of errors when the bosonic modes operate in the ultrastrong coupling regime. Numerical simulations also predict a large increase of qubit coherence for a specific annealing problem with mediated interactions. We provide evidence that noise reduction occurs in more general types of quantum computers with similar architectures.
We show a significant reduction of errors for an architecture of quantum annealers where bosonic modes mediate the interaction between qubits. These systems have a large redundancy in the subspace of solutions, supported by arbitrarily large bosonic occupations. We explain how this redundancy leads to a mitigation of errors when the bosonic modes operate in the ultrastrong coupling regime. Numerical simulations also predict a large increase of qubit coherence for a specific annealing problem with mediated interactions. We provide evidence that noise reduction occurs in more general types of quantum computers with similar architectures.
We study a quantum annealer where bosons mediate the Ising-type interactions between qubits. We compare the efficiency of ground state preparation for direct and mediated couplings, for which Ising and spin-boson Hamiltonian are employed respectively. This comparison is done numerically for a small frustrated antiferromagnet, with a careful choice of the optimal adiabatic passage that reveals the features of the boson-mediated interactions. Those features can be explained by taking into account what we called excited solutions: states with the same spin correlations as the ground-state but with a larger bosonic occupancy. For similar frequencies of the bosons and qubits, the performance of quantum annealing depends on how excited solutions interchange population with local spin errors. We report an enhancement of quantum annealing thanks to this interchange under certain circumstances.
Ising machines, quantum annealing (QA) machines, are promising for solving combinatorial optimization problems at high speed. Problems are formulated as Ising models where optimal solutions correspond to ground states, but stochastic noise often causes deviations, necessitating error reduction strategies. The quantum annealing correction (QAC) model introduces penalty spins to favor consistent solutions across replicas and has shown effectiveness in QA, but its behavior under simulated annealing (SA) remains insufficiently understood. This study systematically evaluates the stacked model, a recently proposed error reduction method, against QAC-based and simple parallelization approaches in SA. Our results demonstrate that, even for large-scale systems, the stacked model consistently outperforms alternatives as the number of replicas increases, maintaining relatively high success probability through spontaneous replica alignment via structured coupling. These findings highlight the stacked model as a scalable and error reduction framework for Ising-based optimization.
Abstract We present a general error-correcting scheme for quantum annealing that allows for the encoding of a logical qubit into an arbitrarily large number of physical qubits. Given any Ising model optimization problem, the encoding replaces each logical qubit by a complete graph of degree C, representing the distance of the error-correcting code. A subsequent minor-embedding step then implements the encoding on the underlying hardware graph of the quantum annealer. We demonstrate experimentally that the performance of a D-Wave Two quantum annealing device improves as C grows. We show that the performance improvement can be interpreted as arising from an effective increase in the energy scale of the problem Hamiltonian or, equivalently, an effective reduction in the temperature at which the device operates. The number C thus allows us to control the amount of protection against thermal and control errors, and, in particular, to trade qubits for a lower effective temperature that scales as C−η, with η⩽2. This effective temperature reduction is an important step towards scalable quantum annealing.
We consider quantum computer architectures where interactions are mediated between hot qubits that are not in their mechanical ground state. Such situations occur, e.g., when not cooling ideally, or when moving ions or atoms around. We introduce quantum gates between logically encoded systems that consist of multiple physical ones and show how the encoding can be used to make these gates resilient against such imperfections. We demonstrate that, in this way, one can improve gate fidelities by enlarging the logical system, and counteract the effect of unknown positions or position fluctuations of involved particles. We consider both a classical treatment of positions, in terms of probability distributions, and a quantum treatment using mechanical eigenmodes. We analyze different settings including a cool logical system mediating interactions between two hot systems, as well as two logical systems consisting of hot physical systems whose positions fluctuate collectively or individually. In all cases, assuming ideal local control to logical systems, we demonstrate a significant improvement in gate fidelities, which provides a platform-independent tool to mitigate thermal noise in the context of trapped-particle-based architectures. Published by the American Physical Society 2024
Abstract Quantum annealers like those manufactured by D-Wave Systems are designed to find high quality solutions to optimization problems that are typically hard for classical computers. They utilize quantum effects like tunneling to evolve toward low-energy states representing solutions to optimization problems. However, their analog nature and limited control functionalities present challenges to correcting or mitigating hardware errors. As quantum computing advances towards applications, effective error suppression is an important research goal. We propose a new approach called replication based mitigation (RBM) based on parallel quantum annealing (QA). In RBM, physical qubits representing the same logical qubit are dispersed across different copies of the problem embedded in the hardware. This mitigates hardware biases, is compatible with limited qubit connectivity in current annealers, and is well-suited for currently available noisy intermediate-scale quantum annealers. Our experimental analysis shows that RBM provides solution quality on par with previous methods while being more flexible and compatible with a wider range of hardware connectivity patterns. In comparisons against standard QA without error mitigation on larger problem instances that could not be handled by previous methods, RBM consistently gets better energies and ground state probabilities across parameterized problem sets.
Nested quantum annealing correction (NQAC) is an error-correcting scheme for quantum annealing that allows for the encoding of a logical qubit into an arbitrarily large number of physical qubits. The encoding replaces each logical qubit by a complete graph of degree C. The nesting level C represents the distance of the error-correcting code and controls the amount of protection against thermal and control errors. Theoretical mean-field analyses and empirical data obtained with a D-Wave Two quantum annealer (supporting up to 512 qubits) showed that NQAC has the potential to achieve a scalable effective-temperature reduction, ${T}_{\mathrm{eff}}\ensuremath{\sim}{C}^{\ensuremath{-}\ensuremath{\eta}}$, with $0<\ensuremath{\eta}\ensuremath{\le}2$. We confirm that this scaling is preserved when NQAC is tested on a D-Wave 2000Q device (supporting up to 2048 qubits). In addition, we show that NQAC can also be used in sampling problems to lower the effective-temperature of a quantum annealer. Such effective-temperature reduction is relevant for machine-learning applications. Since we demonstrate that NQAC achieves error correction via a reduction of the effective-temperature of the quantum annealing device, our results address the problem of the ``temperature scaling law for quantum annealers,'' which requires the temperature of quantum annealers to be reduced as problems of larger sizes are attempted to be solved.