NVIDIA / NVIDIA/cuda-quantum

Ionq results differ from quantinuum results with multiple measurements

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Description

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Describe the bug

The following test produces different results on Ionq emulation and Ionq simulation in CI due to mz instructions and extra mz-related registers we create in QIR.

Questions:

  • Is this code valid?
    • should we emit an error on get_state on a kernel that contains a measurement?
  • If the code is valid, what results do we expect for various platforms?
Steps to reproduce the bug
# ============================================================================ #
# Copyright (c) 2022 - 2025 NVIDIA Corporation & Affiliates.                   #
# All rights reserved.                                                         #
#                                                                              #
# This source code and the accompanying materials are made available under     #
# the terms of the Apache License 2.0 which accompanies this distribution.     #
# ============================================================================ #

def test_state_synthesis():
    
        @cudaq.kernel
        def init(n: int):
            q = cudaq.qvector(n)
            h(q[0]
            x(q[1])
            mz(q)
    
        @cudaq.kernel
        def kernel(s: cudaq.State):
            q = cudaq.qvector(s)
            x(q[1])
            mz(q)
    
        s = cudaq.get_state(init, 2)
        s = cudaq.get_state(kernel, s)
        print(counts)
        counts = cudaq.sample(kernel, s)

cudaq.set_target("ionq", emulate=True)
test_state_synthesis()

cudaq.set_target("ionq", emulate=False)
test_state_synthesis()
Tests with emulation enabled on a local simulator give following results:
{ 01:521 11:479 }
Tests against IonQ simulator in CI give different results:
   __global__ : { 000111:479 111111:521 }
   r00000 : { 0:479 1:521 }
   r00001 : { 1:1000 }
   r00002 : { 0:479 1:521 }
   r00003 : { 1:1000 }
   r00004 : { 0:479 1:521 }
   r00005 : { 1:1000 }
}
 =========================== short test summary info ============================
 FAILED python/tests/backends/test_IonQ.py::test_Ionq_state_synthesis - Assert...
 =================== 1 failed, 12 passed, 1 warning in 6.34s ====================
QIR for ionq (note that we create 6 registers)
; ModuleID = 'LLVMDialectModule'
source_filename = "LLVMDialectModule"
target datalayout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-f80:128-n8:16:32:64-S128"
target triple = "x86_64-unknown-linux-gnu"

%Qubit = type opaque

define void @__nvqpp__mlirgen__kernel() local_unnamed_addr #0 {
"0":
  tail call void @__quantum__qis__h__body(%Qubit* null)
  tail call void @__quantum__qis__x__body(%Qubit* nonnull inttoptr (i64 1 to %Qubit*))
  tail call void @__quantum__qis__x__body(%Qubit* nonnull inttoptr (i64 1 to %Qubit*))
  tail call void @__quantum__qis__x__body(%Qubit* nonnull inttoptr (i64 1 to %Qubit*))
  ret void
}

declare void @__quantum__qis__h__body(%Qubit*) local_unnamed_addr

declare void @__quantum__qis__x__body(%Qubit*) local_unnamed_addr

attributes #0 = { "entry_point" "output_labeling_schema"="schema_id" "output_names"="[[[0,[0,\22r00000\22]],[1,[1,\22r00001\22]],[2,[0,\22r00002\22]],[3,[1,\22r00003\22]],[4,[0,\22r00004\22]],[5,[1,\22r00005\22]]]]" "qir_profiles"="base_profile" "requiredQubits"="2" "requiredResults"="6" }

!llvm.module.flags = !{!0, !1, !2, !3, !4}

!0 = !{i32 2, !"Debug Info Version", i32 3}
!1 = !{i32 1, !"qir_major_version", i32 1}
!2 = !{i32 7, !"qir_minor_version", i32 0}
!3 = !{i32 1, !"dynamic_qubit_management", i1 false}
!4 = !{i32 1, !"dynamic_result_management", i1 false}
WARNING: this kernel invocation produced 0 shots worth of results when executed. Exiting shot loop to avoid infinite loop.
{ }
Expected behavior

Same results in both executions (or an error if this code is invalid)

Is this a regression? If it is, put the last known working version (or commit) here.

Not a regression

Environment
  • CUDA-Q version:
  • Python version:
  • C++ compiler:
  • Operating system:
Suggestions

No response

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with python/tests/backends/test_IonQ.py::test_Ionq_state_synthesis and run it with IonQ emulation and simulation enabled. Compare the get_state, measurement, and sample behavior with the shown QIR output. Done means both executions produce consistent results, or the API reports an explicit error for an invalid measured-state operation.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
quantum-computing, testing-qa
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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