In the vast landscape of technological advancements, we have witnessed a remarkable evolution from computers that once occupied entire rooms in the 1960s to today, where more power resides in our back pockets with smartphones than the computers that guided astronauts to the moon. Now, as we stand at the threshold of the quantum computing era, we find ourselves facing similar challenges. Quantum computers, colossal in size and complexity, hold the promise of unlocking computational capabilities beyond the reach of classical systems.
Unlike classical computers, where information is stored in bits that can only represent a zero or one, quantum computers harness the power of qubits, or quantum bits which can not only take the value of zero or one, but any value in between! It is through quantum phenomena such as superposition and entanglement that quantum computers enable operations far surpassing the capabilities of current computers.
Various types of qubits exist, each with unique advantages and challenges. These include superconducting qubits, trapped ion qubits, topological qubits, and more. However, while these alternatives offer exciting possibilities, they often present significant manufacturing and scalability hurdles, making them expensive and challenging to utilize effectively.
And here enters silicon qubits, a promising candidate for the future of quantum computing. Leveraging the well-established industrial processes honed over decades, silicon qubits provide an avenue for large-scale manufacturing and scalability. This convergence of quantum and silicon technologies lays the foundation for the development of powerful quantum computers.
Recent advancements in machine learning have showcased its tremendous potential in revolutionizing the field of quantum computing measurements. Throughout my research I hope to use techniques, such as Bayesian inference and reinforcement learning, to offer innovative solutions and tackle the challenges associated with noisy readouts and the complexity of controlling qubits. By leveraging vast amounts of data and powerful computational algorithms, I hope to use algorithms that can analyse and interpret the intricate patterns and behaviours exhibited by quantum systems. This will not only enable more accurate measurements of qubits but also facilitates the development of efficient algorithms for error correction, optimization, and quantum control.
By integration machine learning and quantum computing, we have the possibility to unlock the full potential of this transformative technology, propelling us closer to realizing practical quantum applications.
NanoDTC PhD Student, c2022