As we sail into the once-uncharted waters of artificial intelligence (AI) and quantum computing, it’s remarkable to witness the sheer magnitude of technological advancements never thought possible a few years ago. With the proliferation of neural networks and quantum computers, we are steadily unraveling the potential locked in an amalgamation of these powerful technologies – an alliance that promises to redefine the very contour of AI.

At the heart of our exploration is understanding the concept of neural networks and quantum computers. Neural networks, essentially systems inspired by the human brain, are the pivotal drivers of advanced artificial intelligence. Conversely, quantum computers are pioneering the frontier of computing capabilities, offering solutions to complex problems with their radically different mode of operation derived from the principles of quantum mechanics.

But what happens when these two enormous fields intertwine? It is anticipated that blending neural networks with quantum computing would reveal a new dimension in artificial intelligence systems and pave the way to quantum machine learning – a new field that promises high-speed solutions to massive data problems that make today’s more significant AI challenges seem trivial.

A key reason behind merging these fields is the intrinsic limit of classical computers in handling complex computations required by advanced AI. Neural networks need to process considerable amounts of data, and hence require a computing capacity that can handle this workload efficiently. Quantum computers, with their exceptional computing prowess, fit the bill perfectly.

Unlike classical computers, quantum computers use qubits instead of bits. This allows them to process a multitude of values simultaneously, immensely improving computing speed and capacity. Leveraging this exceptional computational power of quantum computing can supercharge neural networks, allowing them to learn and adapt significantly faster and process massive datasets efficiently. Moreover, quantum-enhanced machine learning algorithms can inherently capture the complexity and noisy nature of real-world data, propelling AI towards more robust and accurate systems.

Similarly, by adopting the structure and learning methods of neural networks, quantum computing could severely improve. For instance, an AI model can help optimize the configuration of qubits, a challenge faced in quantum computing. This symbiotic fusion can lead to enhanced computational capabilities thereby catalyzing a significant surge in the trajectory of technological advancements.

While exciting, it’s also essential to acknowledge that the integration of quantum mechanics and AI is still unexplored territory, with a host of challenges and unknowns. Quantum hardware continues to grapple with problems like error rates and device stability. Meanwhile, designing practical quantum algorithms that can outperform classical ones is still elusive.

In spite of these challenges, the fusion of quantum computing and neural networks carries the potential to revolutionize AI as we understand it today. Pioneering companies and research institutions are investing substantial resources to harness the power of quantum computing for AI, yet we are only scratching the surface of this new frontier.

The fusion between neural networks and quantum computing is not merely an evolutionary step in technology. It is a paradigm shift that will define the future trajectory of artificial intelligence. As we continue to cross the barriers of technological innovation, driven by the relentless pursuit of knowledge, we embark on the thrilling journey of uncovering the true potential of this fusion in reshaping the AI landscape. No, it’s not science fiction anymore. Rather, it’s a reality that is breaking the barriers of possibilities and leading us into the future.