activity
20182022
most citedAn LLVM-based C++ Compiler Toolchain for Variational Hybrid Quantum-Classical Algorithms and Quantum Accelerators

13 citations · 21 across the 4 of their papers we have counts for

collaborators

10 papers

quant-ph20222 cited

Introducing the Quantum Research Kernels: Lessons from Classical Parallel Computing

A. Y. Matsuura, Timothy G. Mattson

Quantum computing represents a paradigm shift for computation requiring an entirely new computer architecture. However, there is much that can be learned from traditional classical…

quant-ph20224 cited

A Scalable Microarchitecture for Efficient Instruction-Driven Signal Synthesis and Coherent Qubit Control

Nader Khammassi, Randy W. Morris, Shavindra Premaratne +7

Execution of quantum algorithms requires a quantum computer architecture with a dedicated quantum instruction set that is capable of supporting translation of workloads into actual…

quant-ph202213 cited

An LLVM-based C++ Compiler Toolchain for Variational Hybrid Quantum-Classical Algorithms and Quantum Accelerators

Pradnya Khalate, Xin-Chuan Wu, Shavindra Premaratne +6

Variational algorithms are a representative class of quantum computing workloads that combine quantum and classical computing. This paper presents an LLVM-based C++ compiler toolch…

quant-ph20202 cited

Entanglement Properties of Quantum Superpositions of Smooth, Differentiable Functions

Adam Holmes, A. Y. Matsuura

We present an entanglement analysis of quantum superpositions corresponding to smooth, differentiable, real-valued (SDR) univariate functions. SDR functions are shown to be scalabl…

quant-ph2020

Probing many-body localization on a noisy quantum computer

D. Zhu, S. Johri, N. H. Nguyen +5

A disordered system of interacting particles exhibits localized behavior when the disorder is large compared to the interaction strength. Studying this phenomenon on a quantum comp…

quant-ph2020

Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning

Sahar Daraeizadeh, Shavindra P. Premaratne, A. Y. Matsuura

In this paper, we present a machine learning framework to design high-fidelity multi-qubit gates for quantum processors based on quantum dots in silicon, with qubits encoded in the…