Vulnerability of quantum classification to adversarial perturbations

High-dimensional quantum systems are vital for quantum technologies and are essential in demonstrating practical quantum advantage in quantum computing, simulation, and sensing. Since dimensionality grows exponentially with the number of qubits, the potential power of noisy intermediate-scale quantum devices over classical resources also stems from entangled states in high dimensions. An important family of quantum protocols that can take advantage of high-dimensional Hilbert space is classification tasks. These include quantum machine learning algorithms, witnesses in quantum information processing, and certain decision problems. However, due to counterintuitive geometrical properties emergent in high dimensions, classification problems are vulnerable to adversarial attacks. We demonstrate that the amount of perturbation needed for an adversary to induce a misclassification scales inversely with dimensionality. This is shown to be a fundamental feature independent of the details of the classification protocol. Furthermore, this leads to a tradeoff between the security of the classification algorithm against adversarial attacks and quantum advantages that we expect for high-dimensional problems. In fact, protection against these adversarial attacks requires extra resources that can even scale polynomially with the Hilbert space dimension of the system, which can erase any significant quantum advantage that we might expect from a quantum protocol. This has wide-ranging implications in the use of both near-term and future quantum technologies for classification.