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Fix cudaq::slice_vector synthesis bug and add test #1981

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15 changes: 6 additions & 9 deletions lib/Optimizer/Transforms/QuakeSynthesizer.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -166,15 +166,14 @@ synthesizeVectorArgument(OpBuilder &builder, ModuleOp module, unsigned &counter,

auto replaceLoads = [&](cudaq::cc::ComputePtrOp elePtrOp,
Value newVal) -> LogicalResult {
bool allLoadUsers = true;
for (auto *u : elePtrOp->getUsers()) {
if (auto loadOp = dyn_cast<cudaq::cc::LoadOp>(u)) {
if (auto loadOp = dyn_cast<cudaq::cc::LoadOp>(u))
loadOp.replaceAllUsesWith(newVal);
continue;
}
return elePtrOp.emitError(
"Unknown gep/load configuration for synthesis.");
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else
allLoadUsers = false;
}
return success();
return success(allLoadUsers);
};

// Iterate over the users of this stdvec argument.
Expand Down Expand Up @@ -221,9 +220,7 @@ synthesizeVectorArgument(OpBuilder &builder, ModuleOp module, unsigned &counter,
Value memArr = getArrayInMemory();
builder.setInsertionPoint(elePtrOp);
Value newComputedPtr = builder.create<cudaq::cc::ComputePtrOp>(
argLoc, ptrEleTy, memArr,
SmallVector<cudaq::cc::ComputePtrArg>{
0, elePtrOp.getDynamicIndices()[0]});
argLoc, ptrEleTy, memArr, elePtrOp.getDynamicIndices()[0]);
elePtrOp.replaceAllUsesWith(newComputedPtr);
}
continue;
Expand Down
130 changes: 130 additions & 0 deletions targettests/Remote-Sim/observe_h2.cpp
Original file line number Diff line number Diff line change
@@ -0,0 +1,130 @@
/*******************************************************************************
* Copyright (c) 2022 - 2024 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. *
******************************************************************************/

// REQUIRES: remote-sim

// clang-format off
// RUN: nvq++ %cpp_std --target remote-mqpu --remote-mqpu-auto-launch 1 %s -o %t && %t
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// RUN: nvq++ %cpp_std --enable-mlir --target remote-mqpu --remote-mqpu-auto-launch 1 %s -o %t && %t
// clang-format on

// Note: this is similar to the vqe_h2.cpp example file, but it uses
// cudaq::observe instead of cudaq::vqe in order to exercise quake-synth on
// vector parameters with complicated usage patterns (like cudaq::slice_vector).

#include <cudaq.h>
#include <cudaq/algorithm.h>
#include <cudaq/builder.h>
#include <cudaq/gradients.h>
#include <cudaq/optimizers.h>

// Here we build up a CUDA-Q kernel with N layers and each
// layer containing an arrangement of random SO(4) rotations. The algorithm
// leverages the CUDA-Q VQE support to compute the ground state of the
// Hydrogen atom.

// The SO4 random entangler written as a CUDA-Q kernel free function
// since this is a pure-device quantum kernel
__qpu__ void so4(cudaq::qubit &q, cudaq::qubit &r,
const std::vector<double> &thetas) {
ry(thetas[0], q);
ry(thetas[1], r);

h(r);
cx(q, r);
h(r);

ry(thetas[2], q);
ry(thetas[3], r);

h(r);
cx(q, r);
h(r);

ry(thetas[4], q);
ry(thetas[5], r);

h(r);
cx(q, r);
h(r);
}

// The SO4 fabric CUDA-Q kernel. Keeps track of simple
// arithmetic class members controlling the number of qubits and
// entangling layers.
struct so4_fabric {
void operator()(std::vector<double> params, int n_qubits,
int n_layers) __qpu__ {
cudaq::qvector q(n_qubits);

x(q[0]);
x(q[2]);

const int block_size = 2;
int counter = 0;
for (int i = 0; i < n_layers; i++) {
// first layer of so4 blocks (even)
for (int k = 0; k < n_qubits; k += 2) {
auto subq = q.slice(k, block_size);
auto so4_params = cudaq::slice_vector(params, counter, 6);
so4(subq[0], subq[1], so4_params);
counter += 6;
}

// second layer of so4 blocks (odd)
for (int k = 1; k + block_size < n_qubits; k += 2) {
auto subq = q.slice(k, block_size);
auto so4_params = cudaq::slice_vector(params, counter, 6);
so4(subq[0], subq[1], so4_params);
counter += 6;
}
}
}
};

int main() {
// Read in the spin op from file
std::vector<double> h2_data{0, 0, 0, 0, -0.10647701149499994, 0.0,
1, 1, 1, 1, 0.0454063328691, 0.0,
1, 1, 3, 3, 0.0454063328691, 0.0,
3, 3, 1, 1, 0.0454063328691, 0.0,
3, 3, 3, 3, 0.0454063328691, 0.0,
2, 0, 0, 0, 0.170280101353, 0.0,
2, 2, 0, 0, 0.120200490713, 0.0,
2, 0, 2, 0, 0.168335986252, 0.0,
2, 0, 0, 2, 0.165606823582, 0.0,
0, 2, 0, 0, -0.22004130022499996, 0.0,
0, 2, 2, 0, 0.165606823582, 0.0,
0, 2, 0, 2, 0.174072892497, 0.0,
0, 0, 2, 0, 0.17028010135300004, 0.0,
0, 0, 2, 2, 0.120200490713, 0.0,
0, 0, 0, 2, -0.22004130022499999, 0.0,
15};
cudaq::spin_op H(h2_data, /*nQubits*/ 4);

// For 8 qubits, 36 parameters per layer
int n_layers = 2, n_qubits = H.num_qubits(), block_size = 2, p_counter = 0;
int n_blocks_per_layer = 2 * (n_qubits / block_size) - 1;
int n_params = n_layers * 6 * n_blocks_per_layer;
printf("%d qubit Hamiltonian -> %d parameters\n", n_qubits, n_params);

// Define the initial parameters and ansatz. For this test, make a linear ramp
// from -1 to 1.
std::vector<double> init_params(n_params);
for (int i = 0; i < n_params; i++)
init_params[i] = -1 + i * (2.0 / n_params);

so4_fabric ansatz;

auto energy =
cudaq::observe(ansatz, H, init_params, n_qubits, n_layers).expectation();
printf("energy %f\n", energy);

bool isGood = std::abs(energy - -0.320848) < 1e-3;
return isGood ? 0 : 1;
}
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