How Quantum Computing’s Largest Challenges Are Being Solved With Accelerated Computing

Quantum computing guarantees to reshape industries — however progress hinges on fixing key issues. Error correction. Simulations of qubit designs. Circuit compilation optimization duties. These are among the many bottlenecks that should be overcome to deliver quantum {hardware} into the period of helpful purposes.

Enter accelerated computing. The parallel processing of accelerated computing gives the facility wanted to make the quantum computing breakthroughs of at present and tomorrow potential.

NVIDIA CUDA-X libraries type the spine of quantum analysis. From quicker decoding of quantum errors to designing bigger techniques of qubits, researchers are utilizing GPU-accelerated instruments to develop classical computation and convey helpful quantum purposes nearer to actuality.

Accelerating Quantum Error Correction Decoders With NVIDIA CUDA-Q QEC and cuDNN

Quantum error correction (QEC) is a key approach for working with unavoidable noise in quantum processors. It’s how researchers distill hundreds of noisy bodily qubits right into a handful of noiseless, logical ones by decoding knowledge in actual time, recognizing and correcting errors as they emerge.

Among the many most promising approaches to QEC are quantum low-density parity-check (qLDPC) codes, which may mitigate errors with low qubit overhead. However decoding them requires computationally costly typical algorithms working at extraordinarily low latency with very excessive throughput.

The College of Edinburgh used the NVIDIA CUDA-Q QEC library to construct a brand new qLDPC decoding technique referred to as AutoDEC — and noticed a 2x increase in velocity and accuracy. It was developed utilizing CUDA-Q’s GPU-accelerated BP-OSD decoding performance, which parallelizes the decoding course of, rising the chances that error correction works.

In a separate collaboration with QuErathe NVIDIA PhysicsNeMo framework and cuDNN library had been used to develop an AI decoder with a transformer structure. AI strategies supply a promising means to scale decoding to the larger-distance codes wanted in future quantum computer systems. These codes enhance error correction — however they arrive with a steep computational value.

AI fashions can frontload the computationally intensive parts of the workloads by coaching forward of time and working extra environment friendly inference at runtime. Utilizing an AI mannequin developed with NVIDIA CUDA-Q, QuEra achieved a 50x increase in decoding velocity — together with improved accuracy.

Optimizing Quantum Circuit Compilation With cuDF

A technique to enhance an algorithm that works even with out QEC is to compile it to the highest-quality qubits on a processor. The method of mapping qubits in an summary quantum circuit to a bodily structure of qubits on a chip is tied to a particularly computationally difficult drawback referred to as graph isomorphism.

In collaboration with Q-CTRL and Oxford Quantum CircuitsNVIDIA developed a GPU-accelerated structure choice technique referred to as ?-Motif, offering as much as a 600x speedup in purposes like quantum compilation, which contain graph isomorphism. To scale this method, NVIDIA and collaborators used cuDF — a GPU-accelerated knowledge science library — to carry out graph operations and assemble potential layouts with predefined patterns (aka “motifs”) primarily based on the bodily quantum chip structure.

These layouts might be constructed effectively and in parallel by merging motifs, enabling GPU acceleration in graph isomorphism issues for the primary time.

Accelerating Excessive-Constancy Quantum System Simulation With cuQuantum

Numerical simulation of quantum techniques is important for understanding the physics of quantum units — and for growing higher qubit designs. QuTiP, a extensively used open-source toolkit, is a workhorse for understanding the noise sources current in quantum {hardware}.

A key use case is the high-fidelity simulation of open quantum techniques, resembling modeling superconducting qubits coupled with different elements throughout the quantum processor, like resonators and filters, to precisely predict gadget habits.

By way of a collaboration with the College of Sherbrooke and Amazon Net Providers (AWS), QuTiP was built-in with the NVIDIA cuQuantum software program growth package by way of a brand new QuTiP plug-in referred to as qutip-cuquantum. AWS offered the GPU-accelerated Amazon Elastic Compute Cloud (Amazon EC2) compute infrastructure for the simulation. For big techniques, researchers noticed as much as a 4,000x efficiency increase when learning a transmon qubit coupled with a resonator.

Study extra in regards to the NVIDIA CUDA-Q platform. Learn this NVIDIA technical weblog for extra particulars on how CUDA-Q powers quantum purposes analysis.

Discover quantum computing periods at NVIDIA GTC Washington, D.C, working Oct. 27-29.