A team of researchers from the University of Texas at Dallas and an industry collaborator have developed a groundbreaking approach to bolster the security of quantum computers against adversarial attacks. Their innovative solution, Quantum Noise Injection for Adversarial Defense (QNAD), aims to mitigate the impact of attacks that target AI’s decision-making abilities.
Quantum computers are on the cutting edge of technology, capable of solving complex problems exponentially faster than traditional computers, which makes them valuable for enhancing AI applications in devices such as autonomous vehicles. However, these quantum computers are susceptible to adversarial attacks that can potentially have serious consequences, much like traditional computers.
Dr. Kanad Basu, an assistant professor at the University of Texas at Dallas, explained that adversarial attacks aimed at disrupting AI’s decision-making process could be likened to obstructing a stop sign, resulting in potentially dangerous outcomes for autonomous vehicles. The QNAD approach leverages quantum noise and crosstalk to counteract these attacks, ultimately enhancing the security of quantum computer applications.
Quantum computing harnesses the principles of quantum mechanics to perform computations, with qubits serving as the fundamental unit of information. Unlike traditional bits, qubits can exist in a state of 0 and 1 simultaneously, leading to significant speed advantages. However, quantum computers are vulnerable to noise and crosstalk, which can introduce errors into their operations.
The researchers’ novel approach exploits the intrinsic noise and crosstalk present in quantum computers to mitigate the impact of adversarial attacks on AI models. By introducing crosstalk into the quantum neural network (QNN), a form of machine learning, the researchers demonstrated a remarkable 268% increase in accuracy during attacks when utilizing the QNAD framework.
The study authors emphasized that the QNAD approach should complement existing security measures for quantum computers, akin to how seat belts enhance safety in vehicles. The framework acts as a protective layer, reducing the impact of adversarial attacks on the QNN model.
Through their collaborative efforts, the research team has made strides towards enhancing the security of quantum computer applications, presenting their findings at the IEEE International Symposium on Hardware Oriented Security and Trust. With quantum computing poised to revolutionize AI applications, the development of robust defense mechanisms like QNAD is crucial to safeguard against potential vulnerabilities and ensure the continued advancement of this groundbreaking technology.
