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Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study

Quantum computing is one of the most enticing emerging computational paradigms. It has the potential to revolutionize diverse areas within the future of computation. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, current processors are still too small and non-robust to be directly useful for any real-world applications today. Nevertheless, we are entering an era of unprecedented capabilities for the exploration of quantum algorithms and protocols beyond what is possible today. There is also the opportunity to map out large-scale architectures and estimate resources for early fault-tolerant quantum computing, tailored to specific applications, through codesign of algorithms, quantum error correction, and hardware. In this talk, I’ll discuss NASA’s work in assessing and advancing the potential of quantum computing, illustrating advances in algorithms, both near- and longer-term, in designing novel quantum error correction methods, in resource estimation, and in co-design. I’ll also highlight physics-inspired classical algorithms that can be used at the application scale today. The talk will conclude with a discussion of open research directions.

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Semiconductor quantum systems

Quantum technologies need photonics for scaling. This is true even for “non-photonic” quantum systems based on superconductors, or trapped atoms and ions in vacuum. For example, new types of spatial light modulators and switches are needed to trap and control atoms and ions, microwave to optical quantum transducers are needed for networking superconducting processors, chip-scale laser systems are required for controlling atoms or spin qubits in solids, and very high efficiency integrated photonics is needed for quantum networks, sensors, and chip-based semiconductor quantum systems. Unfortunately, these photonics functionalities and performances are not available even in today’s best integrated photonic systems. We show how inverse design (which combines AI hardware with new ty pes of physics solvers) can lead to much better photonics designs, and how new photonic materials combined with new nanofabrication and heterogenous integration can lead to desired performances. Specific examples include development of miniaturized titanium:sapphire lasers on chip, strontium titanate transducers, quantum network nodes in diamond, and a quantum simulator and computer with silicon carbide color centers.

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