Compute is our most valuable resource, yet we haven't fundamentally changed how we compute since classical computers. Every tech breakthrough in history has relied on binary computation. While this consistency enabled rapid growth, energy expenditure is soaring and will provably become problematic for important tasks (incl. ML). As humankind advances, we'll dedicate an ever-increasing share of our energy to compute. We urgently need new computational methods that offer fundamental efficiencies for current and future problems.
Quantum computing presents a promising solution to this challenge. However, quantum bits (qubits) are much more prone to noise, both in magnitude and complexity, than their classical counterparts. Two broad classes of solutions to this problem have emerged: Quantum Error Mitigation (QEM), which seeks to extract useful information from noisy quantum computers, and Quantum Error Correction (QEC), which aims to use extra qubits to correct errors as they occur. QEM alone is likely not powerful enough to achieve quantum advantage, and while QEC will likely enable fault-tolerant quantum computing in the long term, practical QEC is decades away. However, there is likely a rich set of techniques in the intersection of QEM and QEC that could enable quantum advantage on practical tasks within the next few years. This is what I work on - i.e., blending QEM and QEC techniques to overcome the limitations of both paradigms and achieve near-term quantum advantage.
My goal is to build a software suite to enable the device-tailored application of these techniques to real quantum systems, enabling both quantum hardware and quantum applications developers to accelerate the practicality of their work.