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arXiv · 2003.03460

Enhancing a Near-Term Quantum Accelerator's Instruction Set Architecture for Materials Science Applications

Xiang Zou·Shavindra P. Premaratne·M. Adriaan Rol·Sonika Johri·Viacheslav Ostroukh·David J. Michalak·Roman Caudillo·James S. Clarke·Leonardo Dicarlo·A. Y. Matsuura
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Abstract

Quantum computers with tens to hundreds of noisy qubits are being developed today. To be useful for real-world applications, we believe that these near-term systems cannot simply be scaled-down non-error-corrected versions of future fault-tolerant large-scale quantum computers. These near-term systems require specific architecture and design attributes to realize their full potential. To efficiently execute an algorithm, the quantum coprocessor must be designed to scale with respect to qubit number and to maximize useful computation within the qubits' decoherence bounds. In this work, we employ an application-system-qubit co-design methodology to architect a near-term quantum coprocessor. To support algorithms from the real-world application area of simulating the quantum dynamics of a material system, we design a (parameterized) arbitrary single-qubit rotation instruction and a two-qubit entangling controlled-Z instruction. We introduce dynamic gate set and paging mechanisms to implement the instructions. To evaluate the functionality and performance of these two instructions, we implement a two-qubit version of an algorithm to study a disorder-induced metal-insulator transition and run 60 random instances of it, each of which realizes one disorder configuration and contains 40 two-qubit instructions (or gates) and 104 single-qubit instructions. We observe the expected quantum dynamics of the time-evolution of this system.

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Xiang Zou, Shavindra P. Premaratne, M. Adriaan Rol, Sonika Johri, Viacheslav Ostroukh, David J. Michalak, Roman Caudillo, James S. Clarke, Leonardo Dicarlo, A. Y. Matsuura. 2020-03-06. Enhancing a Near-Term Quantum Accelerator's Instruction Set Architecture for Materials Science Applications. https://doi.org/10.1109/tqe.2020.2965810

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