Modern neuroscience research faces critical computational bottlenecks. Neural recording technology advances allow for increasingly large, multidimensional datasets which contain non-stationary signals and complex nonlinear interactions across multiple spatiotemporal scales. At the same time, classical computing approaches are reaching their physical limitations. The convergence of these two factors creates an urgent need for alternative computational approaches. Here, we argue that quantum computing offers transformative solutions in three different ways. First, quantum algorithms and their implementation on quantum computing hardware can reveal novel neural features, including subtle dynamics and emergent network properties, that remain computationally inaccessible to classical methods. Indeed, recent findings in other scientific fields have already uncovered properties that classical approaches have failed to capture. Second, quantum systems provide exponential scaling advantages for analyzing high-dimensional neural data. This includes demonstrated significant speedups for network state evaluation and exponentially improved efficiency in detecting correlations across sparse, noisy datasets which are typical of neuroscience research. Third, quantum formalism offers alternative mathematical frameworks for understanding neural information processing. These frameworks better account for context- dependence, probabilistic dynamics, and multi-pathway causation than classical deterministic models. While current pre-fault tolerant devices face limitations in scalability and decoherence, developing hybrid quantum-classical approaches already show practical advantages in other areas of research. Therefore, beyond computational speedups, quantum approaches may fundamentally transform how we understand activity in the brain, neural information processing, and the resulting cognitive phenomena.