Skip to content

Welcome to Bartiq

Introduction

Bartiq is a Python library for compiling and analyzing fault-tolerant quantum algorithms to understand their computational requirements. It focuses on Quantum Resource Estimation (QRE), tracking key logical-level resources including T-gates, Toffolis, circuit active volume, and qubit count. In bartiq, quantum algorithms are expressed as subroutines with locally defined symbolic resource costs, which are used by its compilation engine to generate global resource costs from these local definitions.

To install bartiq via pip, run 

pip install bartiq
More detailed instructions can be found on the installation page.

Quick start

As an example we consider the following circuit, from Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity ⧉. This circuit prepares an arbitrary state with $L$ unique amplitudes, and is equivalent to classical alias sampling. From Fig. 11 in the paper:

Alias Sampling

In order to quickly get started with bartiq, you can load this as an example routine and use it as follows (click here to download alias_sampling_basic.json):

import json
from bartiq import compile_routine, evaluate
from qref import SchemaV1

with open("alias_sampling_basic.json", "r") as f:
    routine_dict = json.load(f)

uncompiled_routine = SchemaV1(**routine_dict)
compiled_routine = compile_routine(uncompiled_routine).routine
After loading the alias sampling JSON file we cast it to the qref.SchemaV1 type, our data format ⧉ for representing quantum algorithms for the purposes for resource estimation. This provides us with an uncompiled_routine, which we can then compile with bartiq. The compilation engine will propagate the resource costs from low-level subroutines up, to create aggregated global costs for the whole circuit.

To see, for example, the symbolic $T$-gate count for this circuit: 

print(compiled_routine.resources["T_gates"].value)
>>> 4*L + 8*L/multiplicity(2, L) + 4*mu + O(log2(L)) - 8

To obtain numeric resource costs we can assign values to our variables $L$ and $\mu$ and then evaluate the routine

assignments = {"L": 100, "mu": 10}
evaluated_routine = evaluate(compiled_routine, assignments).routine

print(evaluated_routine.resources["T_gates"].value)
>>> O(log2(100)) + 832

As bartiq is primarily symbolic in nature, we do not have to assign values for all of our variables: 

assignments = { "mu": 10}
evaluated_routine = evaluate(compiled_routine, assignments).routine

print(evaluated_routine.resources["T_gates"].value)
>>> 4*L + 8*L/multiplicity(2, L) + O(log2(L)) + 32

Next steps

  • For more comprehensive examples, please see the tutorials.
  • If you are interested in learning more about how the bartiq compilation engine works, please see the compilation page.
  • For common issues, please check troubleshooting section.
  • You can find reference documentation for the public API of bartiq's python package, please go to reference.