Domestic Manufacturing Capabilities for Critical DoD Applications Emerging Needs in Quantum-Enabled Systems: Proceedings of a Workshop (2019) / Chapter Skim
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3 Quantum Computing Systems
3 Quantum Computing Systems
Pages 13-23
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From page 13... ...
A SYSTEM OVERVIEW OF QUANTUM COMPUTING Pat Gumann, IBM Research Gumann offered insights about quantum technology, the current state of quantum computing (in particular, his work on IBM's superconducting quantum computing platform) , and the continued need for fundamental research into various fields of science and engineering -- microwave electronics, low-temperature physics, quantum-limited amplifiers, and quantum mechanics.
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era. Today's quantum computers can perform short depths limited by coherence and heuristic applications with possible (but not provable)
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Generality Specialized Universal Universal FIGURE 3.2 Quantum computing paradigms. NOTE: QAOA, Quantum Approximate Optimization Algorithm; QC, quantum computing.
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IBM's superconducting quantum computers combine single-junction fixed frequency transmons (which have reduced noise sensitivity) and superconducting readout resonators to measure states of the qubit.
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FIGURE 3.3 Quantum volume. SOURCE: Pat Gumann, IBM Research, presentation to the workshop.
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SPIN QUBITS DEVICE INTEGRATION Ravi Pillarisetty, Intel Pillarisetty discussed the challenges within quantum computing and Intel's efforts to overcome them by applying its transistor expertise to semiconductor based spin qubits. Quantum computing has the potential to provide exponential computational speedup, with broad applications across the economy, including logistics, image processing, pharmacology, and cryptography.
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At T = 1.5 K, Intel substrate plus thermal oxide produces clean data on the academic flow, but shows noise on the 300 mm integrated flow, which Pillarisetty said is not surprising given that the transistor metrics indicate a nonoptimized barrier gate interface. Intel sees a clear developmental path from transistors to quantum dots to spin qubits.
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Quantum Materials Versus Quantum Devices Quantum materials focus on assisting nature to assemble complex structures. In this context, quantum coherence is preserved through structural perfection and symmetry, collectively creating emergent phenomena that could be harnessed for engineering applications.
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However, there are many other, more flexible qubit designs to explore, including tunable, topological circuits; non-S wave materials; and novel tunnel barriers, which could have better noise properties or compatibility with fault tolerance. While there are applications for current achievements, to get to the 100- or 1,000-qubit control level, additional fundamental research is necessary.
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Quantum signal processing hardware requires high-coherence compatible cryo genic high-noise reduction filters, diplexers, circulators, and amplifiers; scalable digital and analog microwave technologies; and signal multiplexing, modulation hardware, and quantum–classical signal converters. Last, a flexible hardware stack that allows for hierarchical operations, fast feedback, and active quantum control are needed.
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In response to a question about ML, Siddiqi said that ML brings utility, functionality, and speed to quantum, and so it is a legitimate pursuit. ML is also able to provide insights into noise processes, but it is a classical computing concept and therefore not truly quantum yet, which means that, blindly applied, it could also slow down the system.
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