Explicit Measurements QPU Compatability

[LukeAndreesen]

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Describe the feature

GPU sampling ignores explicit measurement order unless explicit_measurements=True

Description

In my CUDA-Q program, I need the output bitstring ordering to follow the order of the measurement statements.

We are running a noisy simulation (included noise_model) in sample() parameters of a large circuit for many shots, using the nvidia backend target. Below is a simplified example of the issue:

import cudaq

@cudaq.kernel
def circuit():
    q = cudaq.qvector(4)

    x(q[2])   # Apply X gate to the 3rd qubit (index 2)

    mz(q[2])
    mz(q[0])
    mz(q[1])
    mz(q[3])

if __name__ == "__main__":
    cudaq.set_target('nvidia')
    result = cudaq.sample(circuit)
    result_explicit = cudaq.sample(circuit, explicit_measurements=True)

For this circuit:

• cudaq.sample(circuit) returns 0010
• cudaq.sample(circuit, explicit_measurements=True) returns 1000

The 1000 result is the behavior I want, since the measured 1 comes from q[2], which is measured first and should therefore appear in the first bit position of the returned bitstring.

Issue

Without explicit_measurements=True, CUDA-Q appears to ignore the explicit order of the mz(...) statements when constructing output bitstrings.

Using explicit_measurements=True fixes the ordering, but in my case it appears to no longer use the GPU effectively. Performance drops dramatically and GPU utilization falls significantly compared to the default sampling path.

Request

It would be very helpful if CUDA-Q could support explicit measurement ordering while still using the fast GPU sampling path.

If this is not currently supported, it would also be helpful to clarify whether explicit_measurements=True disables GPU-accelerated sampling, and whether there is another recommended way to preserve measurement order while retaining GPU performance.

[aminx-hue]

Thank you for bringing this to our attention. We will consider this for a coming release.