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qtensorformer
q-tensorformer
tensor-train
tensor-networks
adaptive-computation
kv-cache
kv-cache-quantization
attention-sinks
pid-control
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File size: 1,883 Bytes
eaeea8f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | """
Tests for Quantum Backend Module.
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
import pytest
from src.quantum_backend import QuantumBackend, BackendType, ClassicalSurrogateUnitary
def test_classical_surrogate_unitary():
surrogate = ClassicalSurrogateUnitary(n_qubits=4, n_layers=2, n_outputs=4)
x = torch.randn(2, 6, 4)
out = surrogate(x)
assert out.shape == (2, 6, 4)
# Pauli-Z expectation values bounded in [-1, 1]
assert out.min() >= -1.01
assert out.max() <= 1.01
print("✓ test_classical_surrogate_unitary passed")
def test_quantum_backend_execution():
backend = QuantumBackend(
backend_type=BackendType.CLASSICAL_SURROGATE,
n_qubits=4,
n_layers=2,
d_model=64,
)
x = torch.randn(2, 8, 64)
out, meta = backend(x)
assert out.shape == (2, 8, 64)
assert meta["classification"] in ["MEASURED", "SIMULATED"]
print("✓ test_quantum_backend_execution passed")
def test_quantum_kernel_fidelity():
backend = QuantumBackend(
backend_type=BackendType.CLASSICAL_SURROGATE,
n_qubits=4,
n_layers=2,
d_model=32,
)
B, H, T_q, T_k, D = 1, 2, 4, 4, 8
q = torch.randn(B, H, T_q, D)
k = torch.randn(B, H, T_k, D)
K = backend.compute_kernel_matrix(q, k)
assert K.shape == (B, H, T_q, T_k)
# Self-fidelity K(q, q) should be close to 1.0
K_self = backend.compute_kernel_matrix(q, q)
diag = torch.diagonal(K_self[0, 0])
assert torch.all(diag > 0.8), f"Self-fidelity diagonal should be near 1.0, got {diag}"
print("✓ test_quantum_kernel_fidelity passed")
if __name__ == "__main__":
test_classical_surrogate_unitary()
test_quantum_backend_execution()
test_quantum_kernel_fidelity()
print("All Quantum Backend tests passed!")
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