With recent innovations yielding fully integrated prototypes and more powerful commercial processors, companies are gearing up to deliver large-scale photonic quantum machines.
Commercial use of quantum computing technology is in our future, and the CEO of one quantum technology company is positioning his company to deliver a first-mover advantage for enterprises. However, the focus is on satisfying customers today, so he is—without pause—preparing customers to leverage the most advanced quantum technology today and into the future. Christopher Savoie, a serial entrepreneur and a founder of Zapata Computing, recently discussed his views on innovation processes, forming companies around emergent technologies, and how he is building a business within the nascent quantum computing industry.
Growing interest in quantum machine learning has resulted into very innovative algorithms and vigorous studies that demonstrate their power. These studies, although very useful, are often designed for fault-tolerant quantum computers that are far from reality of today's noise-prone quantum computers. While companies such as IBM have ushered in a new era of quantum computing by allowing public access to their quantum computers, quantum noise as well as decoherence are daunting obstacles that not only degrade the performance of quantum algorithms, but also make them infeasible for running on current-era quantum processors. We address the feasibility of a quantum machine learning algorithm on IBM quantum processors to shed light on their efficacy and weaknesses to design noise-aware algorithms that work around these limitations. We compare and discuss the results by implementing a quantum convolutional filter on a real quantum processor as well as a simulator.
NP-complete problems are widely and deeply involved in various real-life scenarios while still intractable to solve efficiently on conventional computers. It is of great practical significance to construct versatile computing architectures that solve NP-complete problems with computational advantage. Here, we present a reconfigurable photonic processor to efficiently solve a benchmark NP-complete problem, the subset sum problem (SSP). We show that in the case of successive primes, the photonic processor has genuinely surpassed commercial electronic processors launched recently by taking advantages of the high propagation speed and vast parallelism of photons and state-of-the-art integrated photonic technology. Moreover, we are able to program the photonic processor to tackle different problem instances relying on the tunable integrated modules, variable split junctions, which can be used to build a fully reconfigurable architecture potentially allowing 2N configurations at most. Our experiments confirm the potential of the photonic processor as a versatile and efficient computing platform, suggesting a possible practical route to solving computationally hard problems at large scale.