Fixing `for_(from_array(...))` Error In QUANALYZE

Dec 18, 2025 by Alex Johnson 50 views

Debugging `for_(from_array(...))` Errors in QUANALYZE: A Deep Dive

Quantum computing is an exciting field, and with powerful tools like QUANALYZE and its associated libraries, you can build sophisticated quantum experiments. However, as with any complex software, sometimes you might run into unexpected errors. One such issue, which can be particularly perplexing, is an internal error that arises when using the for_ loop with the from_array function from py-qua-tools without the crucial asterisk.

This article will guide you through understanding why this error occurs, how to fix it, and what steps you can take to prevent similar issues in the future. We'll break down the problem, provide clear examples, and ensure you can get back to your quantum programming with confidence.

Understanding the `from_array` Function and the Missing Asterisk

The from_array function, found within the qualang_tools.loops library, is designed to help you iterate over arrays in your QUANALYZE programs. It's a convenient way to feed a sequence of values into a loop, allowing for parametric sweeps and other forms of systematic experimentation. When you want to use the values generated by from_array within a for_ loop, you need to unpack them. In Python, the asterisk (*) is used as the unpacking operator. So, if from_array returns a sequence (like a tuple or list), *from_array(...) tells Python to treat each element of that sequence as a separate argument to the for_ loop.

The core of the problem lies in this unpacking. When you write for_(from_array(...)) without the asterisk, you're not passing the elements of the array to the loop; instead, you're passing the from_array object itself as a single, unrecognized argument. The QUANALYZE compiler or runtime then encounters this unexpected input, leading to an internal error because it doesn't know how to process a for_ loop that's expecting individual values but is given a complex object instead.

This might seem like a small detail, but in programming, syntax and operators carry significant meaning. The absence of the asterisk fundamentally changes how Python interprets your code, leading to the cascade of errors you see in the traceback. The traceback, while intimidating, points to a GRPCError with a Status.INTERNAL, which is often a symptom of the underlying system not understanding the input it received.

Let's look at a common scenario where this might happen. Imagine you're trying to sweep a parameter, say a, across a range of values using np.arange. Your intention is to have the for_ loop iterate through each value generated by np.arange and assign it to a. The correct way to achieve this, using from_array, is:

with program() as hello_qua:
    a = declare(fixed)
    with infinite_loop_():
        # Correct usage with asterisk for unpacking
        with for_(*from_array(a, np.arange(0, 1.1, 0.05))):
            play("pi" * amp(a), "qubit")
        wait(25, "qubit")

Here, np.arange(0, 1.1, 0.05) generates an array of floating-point numbers. from_array(a, ...) pairs the declared variable a with this array. Crucially, the * before from_array unpacks the result of from_array so that the for_ loop receives the variable a and the sequence of values it should take on, one by one. This is the intended way for the for_ loop to consume the output of from_array.

Now, consider the incorrect version that leads to the error:

with program() as hello_qua:
    a = declare(fixed)
    with infinite_loop_():
        # Incorrect usage without asterisk
        with for_(from_array(a, np.arange(0, 1.1, 0.05))):
            play("pi" * amp(a), "qubit")
        wait(25, "qubit")

In this incorrect snippet, from_array(a, np.arange(0, 1.1, 0.05)) is passed as a single argument to for_. The for_ loop expects to be told which variable to iterate over and what sequence of values that variable should take. When it receives the from_array object directly, it doesn't know how to interpret this. It's like trying to give someone a recipe book and expecting them to cook a meal without telling them which recipe to use or what ingredients are for which step. The system gets confused, leading to the Status.INTERNAL error you observed.

The Traceback: What It Tells You

The traceback you provided is quite detailed and offers clues. It shows that the error originates deep within the gRPC communication layer (grpclib.exceptions.GRPCError) between your Python script and the Quantum Orchestration Platform (QOP). The sequence of calls leads to qm.api.base_api._handle_connection_error, which eventually raises a QMConnectionError. The message, "Encountered connection error from QOP: details: An internal error occurred., status: Status.INTERNAL", is the manifestation of the QOP receiving malformed input and failing internally.

This type of error, an "internal error," is often a catch-all for unexpected situations on the server-side. In this case, the unexpected situation is the improperly formatted loop construct. Because the input isn't valid for what the QOP expects, it can't proceed with compilation or execution, resulting in this generic internal failure.

The Solution: Adding the Asterisk

As highlighted in the explanation above, the solution is remarkably simple once you understand the cause: add the asterisk (*).

By changing for_(from_array(a, np.arange(0, 1.1, 0.05))) to for_(*from_array(a, np.arange(0, 1.1, 0.05))), you ensure that the from_array function's output is correctly unpacked and passed to the for_ loop. The for_ loop then receives the variable a and the sequence of values it should iterate through, allowing the QUANALYZE compiler to understand and process your program correctly.

Here’s the corrected code snippet:

from qualang_tools.loops import from_array
import numpy as np
from qm.program_qms import ProgramPage
from qm.qua import program, infinite_loop_, for_, play, wait, declare

# Assuming 'pi' and 'qubit' are defined elsewhere or are standard.
# For a complete runnable example, you might need more setup.

with program() as hello_qua:
    a = declare(fixed) # Declare 'a' as a fixed-point number
    with infinite_loop_(): # Start an infinite loop
        # *** The crucial fix is the asterisk (*) here ***
        with for_(*from_array(a, np.arange(0, 1.1, 0.05))):
            # Inside the loop, 'a' will take on each value from the array
            play("pi" * amp(a), "qubit")
        wait(25, "qubit") # Wait for 25 time units on the 'qubit' element

# To execute this, you would typically:
# from qm.QuantumMachine import QuantumMachine
# from qm.qua import QuantumMachineManager
# 
# # Assume 'config' is your QM configuration dictionary
# qm = QuantumMachine(config)
# job = qm.execute(hello_qua)
# # ... further job management ...

This small change is vital. It tells Python to expand the iterable returned by from_array into individual arguments for the for_ loop. In this context, it ensures that the loop knows it should iterate over the values provided for the variable a.

Why This Error Is Tricky and How to Avoid It

This type of error can be tricky because the traceback doesn't immediately point to a syntax error in your Python code in the way you might expect. Instead, it manifests as a QMConnectionError with an INTERNAL status. This makes it seem like there's a problem with the connection to the QOP or a deeper issue within the QUANALYZE system itself, rather than a simple mistake in how you're calling a function.

Key takeaways for avoiding similar issues:

Understand Python's Unpacking Operator (*): Always be mindful of when and how to use the asterisk (*) for unpacking iterables, especially when passing results from functions like from_array to other functions or loops that expect multiple arguments or elements.
Consult the Documentation: When using library functions, especially those related to iteration or data structures, refer to the official documentation. The py-qua-tools documentation for from_array would explicitly show the usage with the unpacking operator.
Read Tracebacks Carefully: Even when an error seems vague (like INTERNAL), trace it back. The sequence of function calls leading to the error can often reveal the specific part of your code or the underlying library interaction that failed.
Check Function Signatures and Return Types: Understand what a function returns and what arguments the function you're passing its output to expects. from_array returns an iterable structure that needs to be unpacked for for_.
Look for Examples: Code examples provided by libraries are invaluable. The example code for hello_qua you referenced likely demonstrates the correct usage of from_array with the asterisk.

Tip for Debugging: If you encounter a similar INTERNAL error, consider if you're incorrectly passing an object where specific values are expected. Try to simplify the call, print intermediate results (if possible before compilation), or search for examples of the function's usage.

While the QUANALYZE platform and its tools are powerful, they rely on precise input. Small syntactical details, like the presence or absence of an asterisk, can have significant consequences. By understanding the role of the unpacking operator and how functions like from_array interact with loops, you can efficiently resolve these issues and continue to build your quantum experiments.

For more information on QUANALYZE and its programming aspects, you can refer to the official documentation: