Peter Mcmahon is a distinguished figure in the field of applied physics and engineering, renowned for his groundbreaking work in the physics of computation and the exploration of novel computing technologies. Holding a Ph.D. in Electrical Engineering with a Physics minor from Stanford University (2014), Peter McMahon’s academic journey is marked by significant contributions to quantum information processing and unconventional computing methodologies. His research seeks to transcend the limitations of conventional CMOS-based von Neumann processors by engineering physical systems capable of performing computation in innovative and more efficient ways. Currently a faculty member at Cornell University’s School of Applied and Engineering Physics, Peter McMahon continues to lead cutting-edge research at the McMahon Lab, pushing the boundaries of computational physics.
Research Focus: Quantum, Photonic, and Neuromorphic Computing
The McMahon Lab at Cornell University, under Peter McMahon’s direction, is dedicated to investigating the fundamental physics underpinning computation. His research is particularly focused on developing and exploring future computing technologies that offer advantages over traditional computing architectures. This encompasses a significant emphasis on quantum computation, alongside explorations into classical alternatives such as photonic computing and neuromorphic computing. Peter McMahon’s work is driven by the potential of these technologies to revolutionize computation across various applications.
Within quantum computing, Peter McMahon’s research spans multiple physical platforms, including spins in semiconductor devices, superconducting circuits, and quantum-optical systems. Each platform presents unique challenges and opportunities, and his lab rigorously examines the fundamental physical limits of each while striving to advance the experimental state-of-the-art. Beyond hardware development, Peter McMahon is deeply engaged in exploring the practical applications of quantum computers, both in the near-term with noisy, intermediate-scale quantum (NISQ) devices, and in the long run with fault-tolerant quantum machines. His research seeks to understand how quantum computers can deliver tangible benefits in areas like optimization, quantum simulation, and machine learning, addressing critical computational challenges in various scientific and engineering domains.
Quantum simulation, originally envisioned by Feynman, remains a central focus of Peter McMahon’s quantum computing research. He investigates the potential of quantum simulators to unlock new insights into strongly correlated systems, with implications for quantum chemistry and condensed-matter physics. This research aims to mirror the transformative impact of conventional computational physics, fostering new scientific discoveries and engineering innovations. Similarly, his explorations into classical unconventional computing technologies are guided by the potential for real-world impact, particularly in optimization and machine learning, areas where novel computational approaches could offer significant advancements.
Selected Publications Highlighting Peter McMahon’s Contributions
Peter McMahon’s prolific research is reflected in numerous high-impact publications, demonstrating his leadership and innovation in the field. A selection of his notable publications includes:
- Microwave signal processing using an analog quantum reservoir computer: This recent work explores the application of quantum reservoir computing for advanced microwave signal processing, showcasing the potential of quantum systems in analog computation. (Senanian et al., 2023)
- Image sensing with multilayer, nonlinear optical neural networks: Published in Nature Photonics, this paper details the development of optical neural networks for image sensing, highlighting the capabilities of photonic computing in visual information processing. (Wang et al., Nature Photonics 2023)
- Deep physical neural networks trained with backpropagation: Featured in Nature, this publication presents research on deep physical neural networks trained using backpropagation, marking a significant step in the development of physical neural networks for complex computations. (Wright et al., Nature 2022)
- An optical neural network using less than 1 photon per multiplication: This Nature Communications paper demonstrates an energy-efficient optical neural network, achieving computation with extremely low photon counts, pushing the boundaries of energy-efficient computing. (Wang et al., Nature Communications 2022)
- A quantum annealer with fully programmable all-to-all coupling via Floquet engineering: Published in npj Quantum Information, this study presents a novel quantum annealer architecture with enhanced programmability, contributing to the advancement of quantum annealing technology. (Onodera, Ng, & McMahon, npj Quantum Information 2020)
- The Capacity of Quantum Neural Networks: This work, available on arXiv, delves into the theoretical capacity of quantum neural networks, providing foundational insights into the capabilities of quantum machine learning models. (Wright & McMahon, arXiv 2019)
- Experimental investigation of performance differences between Coherent Ising Machines and a quantum annealer: Published in Science Advances, this research experimentally compares the performance of Coherent Ising Machines with quantum annealers, offering valuable insights into the strengths and weaknesses of different quantum computing approaches. (Hamerly et al., Science Advances 2019)
- A fully programmable 100-spin coherent Ising machine with all-to-all connections: Featured in Science, this seminal paper details the creation of a large-scale coherent Ising machine, a significant achievement in the development of specialized hardware for solving optimization problems. (McMahon et al., Science 2016)
Awards and Recognition for Peter McMahon’s Innovative Research
Peter McMahon’s contributions have been widely recognized through prestigious awards and honors, highlighting his impact and leadership in the scientific community. His accolades include:
- IUPAP Early Career Scientist Prize for Applied Aspects on Laser Physics and Photonics (2022)
- Office of Naval Research Young Investigator Program Award (2022)
- Sloan Research Award (2022)
- Packard Fellowship in Science and Engineering (2021-2026)
- CIFAR Azrieli Global Scholar (Quantum Information Science) (2020-2022)
- Google Quantum Research Award (2019-2020)
These awards underscore Peter McMahon’s position as a leading figure in his field and reflect the significant impact of his research on the advancement of quantum and unconventional computing.
Education and Academic Background
Peter McMahon’s robust academic foundation underpins his pioneering research. He holds degrees from esteemed institutions, including:
- B.Sc. (Eng) Electrical and Computer Engineering; M.Sc. (Eng) Electrical Engineering; M.Sc. Computer Science, University of Cape Town (2003-2008)
- M.S. Electrical Engineering, Stanford University (2008-2010)
- Ph.D. Electrical Engineering (Physics minor), Stanford University (2010-2014)
- Postdoctoral Fellow, Applied Physics, Stanford University (2014-2019)
His interdisciplinary education, spanning electrical engineering, computer science, and physics, provides a unique and powerful perspective for addressing the complex challenges in quantum and unconventional computing.
Peter McMahon in the News: Recognizing Research Excellence
Peter McMahon’s innovative work and achievements frequently garner media attention, highlighting the broader impact of his research and contributions to the field.
Optica Names Peter McMahon the 2025 Adolph Lomb Medal Recipient
Peter McMahon has been honored with Optica’s prestigious Adolph Lomb Medal for 2025. This award recognizes his exceptional contributions to the field of optics and photonics, particularly his innovative work in optical neural networks and quantum computing. Read more about Optica Names Peter McMahon the 2025 Adolph Lomb Medal Recipient
Eleanor Richard ’25 named 2024 Optica Women Scholar: Recognizing Student Excellence in McMahon’s Research Environment
Eleanor Richard, a student advised within Professor David Muller’s lab and a member of Professor Ankit Disa’s lab, has been named a 2024 Optica Women Scholar. Professor Disa highlighted Richard’s diligence, creativity, and resourcefulness, attributes fostered within the intellectually stimulating environment of departments like that of Peter McMahon. Read more about Eleanor Richard ’25 named 2024 Optica Women Scholar
Optical Neural Networks for Image Processing Developed by Cornell Researchers
Optical neural networks hold promise for image processing: Cornell Research Led by Figures Like Peter McMahon
Cornell researchers have made significant advancements in optical neural networks, developing technology that can pre-process visual information directly at the sensor level. This innovative approach, stemming from research environments involving leading figures like Peter McMahon, has the potential to revolutionize image processing efficiency. Read more about Optical neural networks hold promise for image processing
Peter McMahon’s ongoing research and numerous accolades solidify his position as a leading innovator in the rapidly evolving landscape of quantum and unconventional computing, promising continued significant contributions to the field.
