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A Survey of Quantum Resource Estimation Tools

Brian Goldsmith

brian.goldsmith@gmail.com

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Arnav Sharma

arnav.sharma@ll.mit.edu

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Outline

  • Intro to Quantum Resource Estimation
  • Overview of current tools
    • pyLIQTR
    • Qualtran
    • BenchQ
    • Azure Quantum Resource Estimator
    • Other tools
  • Comparing the tools
  • Acknowledgements
  • Q&A

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Quantum Resource Estimation

What is it?

  • Provides estimates on what it would take to accurately run an algorithm

Why it is important?

  • Gives an idea of how close/far current hardware is
  • Illustrates trade offs (time/space)
  • Allows for comparisons between implementations

How good are the estimates?

  • Uhm.... next question please.

Which tool should I use for my scenario?

  • It depends

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Quantum Resource Estimation

What is it?

  • Provides estimates on what it would take to accurately run an algorithm

Why it is important?

  • Gives an idea of how close/far current hardware is
  • Illustrates trade offs (time/space)
  • Allows for comparisons between implementations

How good are the estimates?

  • Uhm.... next question please.

Which tool should I use for my scenario?

  • It depends

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Quantum Resource Estimation

What is it?

  • Provides estimates on what it would take to accurately run an algorithm

Why it is important?

  • Gives an idea of how close/far current hardware is
  • Illustrates trade offs (time/space)
  • Allows for comparisons between implementations

How good are the estimates?

  • Uhm.... next question please.

Which tool should I use for my scenario?

  • It depends

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Quantum Resource Estimation

What is it?

  • Provides estimates on what it would take to accurately run an algorithm

Why it is important?

  • Gives an idea of how close/far current hardware is
  • Illustrates trade offs (time/space)
  • Allows for comparisons between implementations

How good are the estimates?

  • Uhm.... next question please.

Which tool should I use for my scenario?

  • It depends

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Quantum Resource Estimation

What is it?

  • Provides estimates on what it would take to accurately run an algorithm

Why it is important?

  • Gives an idea of how close/far current hardware is
  • Illustrates trade offs (time/space)
  • Allows for comparisons between implementations

How good are the estimates?

  • Uhm.... next question please.

Which tool should I use for my scenario?

  • It depends

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Current Resource Estimation Tools

MIT Lincoln Lab’s pyLIQTR

Google’s Qualtran

Zapata AI’s BenchQ

Microsoft’s Azure Quantum Resource Estimator

OpenFermion

PsiQuantum’s Bartiq

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pyLIQTR

(LIncoln Laboratory Quantum algorithm Test and Research) is a python library for building quantum circuits derived from quantum algorithms and generating Clifford+T resource estimates. Builds on Cirq/Cirq-FT (ports over CNOT/Toffoli decompositions). Supports:

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  • Problem Instances (Hamiltonians)
  • Block Encodings
  • Quantum Signal Processing / Quantum Singular Value Transform
  • Gate/Circuit Decomps
  • Resource Analysis

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Qualtran

Bloqs: pre-built and user defined

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Pre-built operations break down into (arb) Clifford + T

Decompositions ported from Cirq

*Recently added EC support with Azure Cost and CCZ Models

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BenchQ

Resource estimation tool developed by Zapata AI as part of the DARPA Quantum Benchmarking project which utilizes graph states.

A Substrate Scheduler for Compiling Arbitrary Fault-Tolerant Graph States�http://arxiv.org/abs/2306.03758

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BenchQ

Precise Graph Estimator - Full graph of Clifford+T circuit

Fast Graph Estimator - Full graph of non-Clifford+T circuit

Precise Extrapolation (Stitched) Graph Estimator - Partial graph of Clifford+T circuit

Fast Extrapolation (Stitched) Graph Estimator - Partial graph of non-Clifford+T circuit

Footprint Estimator

Azure Estimator

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BenchQ

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Assessing requirements to scale to practice quantum advantage https://arxiv.org/abs/2211.07629

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Azure Quantum Resource Estimator

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Assessing requirements to scale to practice quantum advantage https://arxiv.org/abs/2211.07629

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Azure Quantum Resource Estimator

Flexibility

  • Physical Qubit Model
  • Quantum Error Correction Scheme
  • Distillation Units
  • Error Budget
  • Constraints

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Azure Quantum Resource Estimator

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Inputs

Physical Qubit/Runtime

Highlighted Feature

T Gates per Rotation Gate

pyLIQTR

Cirq circuits

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QSP / QSVT

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Qualtran

Bloqs (Cirq circuits), Azure Cost Model, CCZ Cost Model

✓

Bloqs

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BenchQ

QuantumProgram (Qiskit, Cirq, Orquestra circuits), Error Budget

✓

Graph Estimator

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Azure QRE

Q#, Qiskit, QIR, Logical Estimates

✓

Use of error correction and physical hardware details

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Bartiq and QREF

Bartiq is a library that allows users to analyze quantum algorithms and calculate symbolic expressions for quantum resource estimates (QRE).

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Acknowledgements and Q&A

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Quantum Open Source Foundation

Mariia Mykhailova - Microsoft

Athena Caesura and Peter Johnson - Zapata AI

Kevin Obenland - Lincoln Laboratory