Quantum Benchmarking

Understanding Algorithmic Qubits (#AQ)

A benchmark that measures what matters most: a system’s ability to successfully run your target quantum workloads

Talk To An Expert

Quantum Computers Are Complex, Predicting Their Value Doesn't Have To Be

#AQ is an application based benchmark, which aggregates performance across 6 widely known quantum algorithms that are relevant to the most promising near term quantum use cases: Optimization, Quantum Simulation and Quantum Machine Learning.

  • orange rectangleOptimization

Problems involving complex routing, sequencing and more

  • checkmarkAmplitude Estimation
  • checkmarkMonte Carlo Simulation
  • blue rectangleQuantum Simulation

Understand the nature of the very small

  • checkmarkHamiltonian Simulation
  • checkmarkVariational Quantum Eigensolver
  • grey rectangleQuantum Machine Learning

Draw inferences from patterns in data, at scale

  • checkmarkQuantum Fourier Transform
  • checkmarkPhase Estimation

These Near Term Quantum Use Cases are widely Applicable to Multiple Industry Verticals*

Algorithmic Qubits Diagram
orange circleOptimization
blue circleQuantum Simulation
grey circleQuantum Machine Learning
5 - highest relevance, 1 - lowest relevance
*Based on algorithmic derivatives most commonly used for IonQ industry use cases

Putting #AQ Into Practice

IONQ System
#AQ 25
IonQ Aria

A Single Metric, A Wealth Of Information

A computer's #AQ can reveal how the system will perform against the workloads that are the most valuable to you. #AQ is a summary and analysis of multiple quantum algorithms. Here is what IonQ Aria's #AQ means, from a practical lens.

  • checkmark6 instances of the most valuable quantum algorithms were run on IonQ Aria
  • checkmark#AQ Algorithms of up to ~600 entangling gates were run successfully
  • checkmark#AQ Algorithms were successfully run on up to 25 qubits
  • checkmarkAlgorithm results were deemed successful if they acheived over 37% Worst Case results fidelity

Predicting Performance Against Your Intended Workloads

All of the information behind the #AQ benchmark can be summarized in a single chart, that provides insight into how a system performs for a particular class of algorithms. By identifying the algorithmic classes you intend to use the system for, you can make a direct prediction about the performance of an algorithm with a specific gate width and gate depth.

#AQ Benchmark on IonQ Aria (Merged) Sep 26, 2022

Translating #AQ to Real World Impact