Accelerated Quantum Monte Carlo with Probabilistic Computers

Abstract

Quantum Monte Carlo (QMC) techniques are widely used in a variety of scientific problems and much work has been dedicated to developing optimized algorithms that can accelerate QMC on standard processors (CPU). With the advent of various special purpose devices and domain specific hardware, it has become increasingly important to establish clear benchmarks of what improvements these technologies offer compared to existing technologies. In this paper, we demonstrate 2 to 3 orders of magnitude acceleration of a standard QMC algorithm using a specially designed digital processor, and a further 2 to 3 orders of magnitude by mapping it to a clockless analog processor. Our demonstration provides a roadmap for 5 to 6 orders of magnitude acceleration for a transverse field Ising model (TFIM) and could possibly be extended to other QMC models as well. The clockless analog hardware can be viewed as the classical counterpart of the quantum annealer and provides performance within a factor of $<10$ of the latter. The convergence time for the clockless analog hardware scales with the number of qubits as $\sim N$, improving the $\sim N^2$ scaling for CPU implementations, but appears worse than that reported for quantum annealers by D-Wave.

References in corpus (32)

• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum computational advantage using photons
• A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
• Adiabatic Quantum Computing
• Computational complexity and fundamental limitations to fermionic quantum Monte Carlo simulations
• Strong quantum computational advantage using a superconducting quantum processor
• Quantum Monte Carlo methods for nuclear physics
• Defining and detecting quantum speedup
• Cluster Algorithm for Vertex Models
• Quantum speedup of Monte Carlo methods
• Observation of topological phenomena in a programmable lattice of 1,800 qubits
• Lattice simulations for few- and many-body systems
• Taming the dynamical sign problem in real-time evolution of quantum many-body problems
• Feynman diagrams versus Fermi-gas Feynman emulator
• Massively Parallel Probabilistic Computing with Sparse Ising Machines
• Unbiasing Fermionic Quantum Monte Carlo with a Quantum Computer
• Demonstration of a scaling advantage for a quantum annealer over simulated annealing
• Diagrammatic Monte Carlo
• Scaling advantage in quantum simulation of geometrically frustrated magnets
• Probabilistic computing with p-bits
• Multi-GPU Accelerated Multi-Spin Monte Carlo Simulations of the 2D Ising Model
• Understanding Quantum Tunneling through Quantum Monte Carlo Simulations
• Hardware-aware $insitu$ Boltzmann machine learning using stochastic magnetic tunnel junctions
• Accurately computing electronic properties of a quantum ring
• Autonomous Probabilistic Coprocessing with Petaflips per Second
• Non-Stoquastic Interactions in Quantum Annealing via the Aharonov-Anandan Phase
• Scalable Emulation of Sign-Problem$-$Free Hamiltonians with Room Temperature p-bits
• The Power of Adiabatic Quantum Computation with No Sign Problem
• Ab initio lattice results for Fermi polarons in two dimensions
• Hardware Design for Autonomous Bayesian Networks
• Parallel Tempering Simulation of the three-dimensional Edwards-Anderson Model with Compact Asynchronous Multispin Coding on GPU

Cited by in corpus (11)

• A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms
• Experimental demonstration of an integrated on-chip p-bit core utilizing stochastic Magnetic Tunnel Junctions and 2D-MoS2 FETs
• All-to-all reconfigurability with sparse and higher-order Ising machines
• Integrated probabilistic computer using voltage-controlled magnetic tunnel junctions as its entropy source
• Double-Free-Layer Stochastic Magnetic Tunnel Junctions with Synthetic Antiferromagnets
• Noise-augmented Chaotic Ising Machines for Combinatorial Optimization and Sampling
• Pushing the Boundary of Quantum Advantage in Hard Combinatorial Optimization with Probabilistic Computers
• Emulating Quantum Interference with Generalized Ising Machines
• Electrically Tunable Picosecond-scale Octupole Fluctuations in Chiral Antiferromagnets
• Many-body computing on Field Programmable Gate Arrays
• Improving deep neural network performance through sampling