Qiskit: Nondeterministic Failures In Preset Pass Manager

by Alex Johnson 57 views

Introduction

In the realm of quantum computing, Qiskit stands out as a powerful open-source framework for quantum software development. However, like any complex system, it occasionally presents challenges. This article delves into a peculiar issue encountered in Qiskit: the nondeterministic failure of the preset pass manager when dealing with seemingly simple quantum circuits. Specifically, we will address the question: Why does the Qiskit preset pass manager sometimes fail seemingly at random when working with a simple circuit?

The Problem: Intermittent Transpilation Errors

A user reported a perplexing problem while using Qiskit version 2.1.1. They found that the transpilation process, which converts a high-level quantum circuit into a form executable on actual quantum hardware, would sometimes succeed and sometimes fail. This inconsistency occurred even when the code and environment remained unchanged. This nondeterministic behavior strongly suggests an underlying instability or bug within the preset pass manager, particularly at optimization level 3. This is quite concerning because consistent and reliable transpilation is crucial for quantum algorithm development and execution.

The error manifests as a TranspilerError, indicating that the transpiler is unable to translate the operations in the circuit to the target basis gates. The error message points out that this could be due to a non-universal target basis or missing equivalence rules in the EquivalenceLibrary. However, the intermittent nature of the error raises questions about the true cause.

Understanding the Environment

To better grasp the context, let's outline the user's setup:

  • Qiskit version: 2.1.1
  • Python version: 3.13.5
  • Operating system: Windows 11
  • Environment: Python virtual environment within a Jupyter Notebook

This setup is fairly standard for Qiskit development, ruling out any immediately obvious environmental factors as the primary cause. The use of a virtual environment ensures that dependencies are isolated, and the Jupyter Notebook environment is commonly used for interactive quantum computing tasks.

Reproducing the Issue: A Simple Circuit

The user provided a minimal code snippet that reliably reproduces the issue. This is invaluable for debugging, as it allows others to independently verify the problem. Here's the code:

from qiskit import QuantumCircuit
from qiskit.transpiler import generate_preset_pass_manager

qc = QuantumCircuit(3)
qc.h(0)
qc.cx(0, 1)
qc.cx(0, 2)
qc.measure_all()

pass_manager = generate_preset_pass_manager(
    optimization_level=3,
    coupling_map=[[0, 1], [1, 0], [2, 1], [1, 2]],
    basis_gates=['h', 'cx'],
    initial_layout=[0, 1, 2]
)

tqc = pass_manager.run(qc)
tqc.draw()

This code defines a simple 3-qubit quantum circuit, applies a Hadamard gate to the first qubit, and then performs controlled-NOT (CX) gates between the first qubit and the other two. The measure_all() function adds measurements to all qubits. The crux of the issue lies in the generate_preset_pass_manager function, which sets up the transpilation process. The optimization_level=3 parameter requests the highest level of optimization, which likely involves more complex transformations. The coupling_map specifies the connectivity of the quantum hardware, the basis_gates define the native gates available, and the initial_layout maps the logical qubits to physical qubits.

Running this script repeatedly demonstrates the nondeterministic behavior. Sometimes, the transpilation succeeds, producing a circuit composed of h and cx gates. Other times, it throws a TranspilerError, indicating a failure to map the circuit to the specified basis gates.

Expected Behavior vs. Reality

The expected behavior is that the circuit should transpile successfully every time. Given the coupling map, basis gates, and initial layout, the output should always be a deterministic circuit consisting only of h and cx gates. The nondeterministic nature of the failure is therefore unexpected and points to a potential issue in the transpilation process itself.

Ideally, if any randomness is introduced during transpilation (for example, in the choice of a particular optimization path), it should be clearly documented. This would prevent confusion and allow users to understand why different transpilation runs might produce slightly different results. However, in this case, the errors suggest a more fundamental problem than just variations in optimization.

Diving into the Error Message

The TranspilerError message provides valuable clues:

TranspilerError: 'Unable to translate the operations in the circuit: [