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William D. Oliver

dblp:180/1677 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0001-8041-0824ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2 · 2 since 2021Software engineering, systems software and programming languages · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
Emerging computing paradigms · 45% Electronic design automation · 41% Hardware accelerators and domain-specific architectures · 14%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Emerging computing paradigms
quantum computer architecture
1.522025
QPlacer: Frequency-Aware Component Placement for Superconducting Quantum Computers · ISCA 2025
Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures · ISCA 2023
Electronic design automation › physical design › placement
component placement
0.912025
QPlacer: Frequency-Aware Component Placement for Superconducting Quantum Computers · ISCA 2025
Electronic design automation
physical design
0.912025
QPlacer: Frequency-Aware Component Placement for Superconducting Quantum Computers · ISCA 2025
Hardware accelerators and domain-specific architectures
machine learning accelerator
0.712023
Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures · ISCA 2023
Emerging computing paradigms › quantum computing and quantum information › quantum measurement
superconducting qubit readout
0.712023
Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures · ISCA 2023
Electronic design automation › signal integrity
crosstalk mitigation
0.312025
QPlacer: Frequency-Aware Component Placement for Superconducting Quantum Computers · ISCA 2025

Methods — techniques the papers use, named apart from their topics

frequency assignment · 0.9electrostatic-based placement · 0.9machine learning · 0.7
YearPublicationVenuePosition
2025 QPlacer: Frequency-Aware Component Placement for Superconducting Quantum Computers
abstract
Quantum Computers face a critical limitation in qubit numbers, hindering their progression towards large-scale and fault-tolerant quantum computing.A significant challenge impeding scaling is crosstalk, characterized by unwanted interactions among neighboring components on quantum chips, including qubits, resonators, and substrates.We motivate a general approach to systematically resolving multifaceted crosstalks in a limited substrate area.We propose QPlacer, a frequency-aware electrostatic-based placement framework tailored for superconducting quantum computers, to alleviate crosstalk by isolating these components in spatial and frequency domains alongside compact substrate design.QPlacer commences with a frequency assigner that ensures frequency domain isolation for qubits and resonators.It then incorporates a padding strategy and resonator partitioning for layout flexibility.Central to our approach is the conceptualization of quantum components as charged particles, enabling strategic spatial isolation through a 'frequency repulsive force' concept.Our results demonstrate that QPlacer carefully crafts the physical component layout in mitigating various crosstalk impacts while maintaining a compact substrate size.On various device topologies and NISQ benchmarks, QPlacer improves fidelity by an average of 37.5× and reduces spatial violations (susceptible to crosstalk) by an average of 12.76×, compared to classical placement engines.Regarding area
Junyao Zhang 0003, Hanrui Wang 0002, Jiaqi Gu 0002, Reouven Assouly, William D. Oliver, Song Han 0003, Kenneth R. Brown, Hai Li 0001, Yiran Chen 0001
ISCA6
2023 Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures
abstract
Reading a qubit is a fundamental operation in quantum computing. It translates quantum information into classical information enabling subsequent classification to assign the qubit states '0' or '1'. Unfortunately, qubit readout is one of the most error-prone and slowest operations on a superconducting quantum processor. On state-of-the-art superconducting quantum processors, readout errors can range from 1--10%. These errors occur for various reasons - crosstalk, spontaneous state transitions, and excitation caused by the readout pulse. The error-prone nature of readout has resulted in significant research to design better discriminators to achieve higher qubit-readout accuracies. High readout accuracy is essential for enabling high fidelity for near-term noisy quantum computers and error-corrected quantum computers of the future.
Satvik Maurya, Chaithanya Naik Mude, William D. Oliver, Benjamin Lienhard, Swamit S. Tannu
ISCA3