Re: use cases for quantum computing: - I spent 4 years at D-Wave and now 2 years at QCI working on apps/tools for quantum computers, mostly focused on constrai

Re: use cases for quantum computing: - I spent 4 years at D-Wave and now 2 years at QCI working on apps/tools for quantum computers, mostly focused on constrained optimization. I know a fair amount about how to use D-Wave's annealing QPU and a smidgen about how to use gate-model QPUs but am not a quantum physicist. - Yes, the QPUs available via AWS' Braket are real QPUs that run real quantum problems, and cost real money. :) IBM, Honeywell, and others have QPUs available directly from the vendor. - To my knowledge there are no real-world problems that today deliver better performance quantumly than classically. (In my view, Google's quantum-supremacy demo of October 2019 was a major milestone but not a real-world problem.) - The use cases that have a chance to be useful before 2030 need to exploit noisy intermediate-scale quantum (NISQ) computers, which for gate-model QPUs means foregoing most of the digital nature of the gate model and using it like an analog computer. Or D-Wave's annealing-based QPU, which is inherently analog. - To my way of thinking, ~all near-term use cases will actually be hybrid classical/quantum, in part because today's QPUs are smaller than ~all real-world problems, and hence the attention must be on the overall algorithm and not just the QPU. - In my view, the 2 leading use cases are simulation of quantum effects and constrained optimization (and I count AI/ML as a close cousin of constrained optimization), which both can derive value from near-term QPUs. For simulation of quantum effects I'd point to the work of Andrew King et al. from D-Wave [1]. For constrained optimization I'd point to the work of Hadfield et al. [2] and Harrigan et al. [3] as well as the host of problems that have been targeted at D-Wave systems over the last few years [4]. Traveling salesperson is a good canonical problem; the constraints are to visit every city and wind up where you started and the objective function is (say) fewest miles traveled. - Here at Quantum Computing Inc., we are focused on a) constrained optimization as the most likely generally useful near-term capability, b) exposing APIs that enable subject-matter experts (in something other than QC) to use QPUs to solve real-world problems, and c) delivering strong classical performance as a building block for earliest quantum advantage, e.g. our paper on graph partitioning [5]. <shamelessCommerce> Our Qatalyst quantum-acceleration platform delivers these capabilities today, along with access to QPUs; see documentation at [6]. </shamelessCommerce> Hope that helps. Caveat: I work for a quantum-software company. [1] https://www.nature.com/articles/s41467-021-20901-5 https://www.nature.com/articles/s41467-021-20901-5 [2] https://arxiv.org/pdf/1706.02998.pdf https://arxiv.org/pdf/1706.02998.pdf [3] https://arxiv.org/pdf/2004.04197.pdf https://arxiv.org/pdf/2004.04197.pdf [4] https://www.dwavesys.com/leap https://www.dwavesys.com/leap [5] https://arxiv.org/pdf/2006.15067v1.pdf https://arxiv.org/pdf/2006.15067v1.pdf [6] https://docs.qci-prod.com/ https://docs.qci-prod.com/