Programmable Quantum Annealers as Noisy Gibbs Samplers

Abstract

Drawing independent samples from high-dimensional probability distributions represents the major computational bottleneck for modern algorithms, including powerful machine learning frameworks such as deep learning. The quest for discovering larger families of distributions for which sampling can be efficiently realized has inspired an exploration beyond established computing methods and turning to novel physical devices that leverage the principles of quantum computation. Quantum annealing embodies a promising computational paradigm that is intimately related to the complexity of energy landscapes in Gibbs distributions, which relate the probabilities of system states to the energies of these states. Here, we study the sampling properties of physical realizations of quantum annealers which are implemented through programmable lattices of superconducting flux qubits. Comprehensive statistical analysis of the data produced by these quantum machines shows that quantum annealers behave as samplers that generate independent configurations from low-temperature noisy Gibbs distributions. We show that the structure of the output distribution probes the intrinsic physical properties of the quantum device such as effective temperature of individual qubits and magnitude of local qubit noise, which result in a non-linear response function and spurious interactions that are absent in the hardware implementation. We anticipate that our methodology will find widespread use in characterization of future generations of quantum annealers and other emerging analog computing devices.

arXiv comment6 pages, 4 figures, with 36 pages of Supplementary Information

References in corpus (18)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum Machine Learning
• Decoherence, einselection, and the quantum origins of the classical
• A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
• Adiabatic Quantum Computing
• Quantum Annealing and Analog Quantum Computation
• Quantum Boltzmann Machine
• Observation of topological phenomena in a programmable lattice of 1,800 qubits
• Quantum Metropolis Sampling
• Inverse statistical problems: from the inverse Ising problem to data science
• Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
• Searching for quantum speedup in quasistatic quantum annealers
• Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
• Adiabaticity in open quantum systems
• Demonstration of nonstoquastic Hamiltonian in coupled superconducting flux qubits
• Global warming: Temperature estimation in annealers
• Thermalization, freeze-out and noise: deciphering experimental quantum annealers
• Analog Errors in Ising Machines

Cited by in corpus (15)

• High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware
• Using quantum annealing to design lattice proteins
• Testing a quantum annealer as a quantum thermal sampler
• Signatures of Open and Noisy Quantum Systems in Single-Qubit Quantum Annealing
• Boltzmann sampling with quantum annealers via fast Stein correction
• A protocol to characterize errors in quantum simulation of many-body physics
• Cost of Emulating a Small Quantum Annealing Problem in the Circuit-Model
• Classical Criticality via Quantum Annealing
• Computing Canonical Averages with Quantum and Classical Optimizers: Thermodynamic Reweighting for QUBO Models of Physical Systems
• Biased Degenerate Ground-State Sampling of Small Ising Models with Converged QAOA
• Quantum Annealing Algorithms for Estimating Ising Partition Functions
• Neural-network-assisted Monte Carlo sampling trained by Quantum Approximate Optimization Algorithm
• Quantum approximate optimization of finite-state bosonic systems
• Multi-tasking through quantum annealing
• Boltzmann Sampling of Frustrated J1 - J2 Ising Models with Programmable Quantum Annealers