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CMP
Credit: Jack Hobhouse

Peter Leek

Research Fellow

Sub department

  • Condensed Matter Physics

Research groups

  • Superconducting quantum devices
peter.leek@physics.ox.ac.uk
Telephone: 01865 (2)72364,01865 (2)82066
Clarendon Laboratory, room 018,104
  • About
  • Publications

Characterization of Nanostructural Imperfections in Superconducting Quantum Circuits

(2025)

Authors:

Mohammed Alghadeer, Simone D Fasciati, Shuxiang Cao, Michele Piscitelli, Susannah C Speller, Peter J Leek, Mustafa Bakr
More details from the publisher

Multiplexed Readout of Superconducting Qubits Using a 3D Re-entrant Cavity Filter

(2024)

Authors:

Mustafa Bakr, Simone D Fasciati, Shuxiang Cao, Giulio Campanaro, James Wills, Mohammed Alghadeer, Michele Piscitelli, Boris Shteynas, Vivek Chidambaram, Peter J Leek
More details from the publisher

Agents for self-driving laboratories applied to quantum computing

(2024)

Authors:

Shuxiang Cao, Zijian Zhang, Mohammed Alghadeer, Simone D Fasciati, Michele Piscitelli, Mustafa Bakr, Peter Leek, Alán Aspuru-Guzik
Details from ArXiV

Complementing the transmon by integrating a geometric shunt inductor

(2024)

Authors:

Simone D Fasciati, Boris Shteynas, Giulio Campanaro, Mustafa Bakr, Shuxiang Cao, Vivek Chidambaram, James Wills, Peter J Leek
More details from the publisher
Details from ArXiV

Encoding optimization for quantum machine learning demonstrated on a superconducting transmon qutrit

Quantum Science and Technology IOP Publishing 9:4 (2024) 045037

Authors:

Shuxiang Cao, Weixi Zhang, Jules Tilly, Abhishek Agarwal, Mustafa Bakr, Giulio Campanaro, Simone Diego Fasciati, James Wills, Boris Shteynas, Vivek Chidambaram, Peter J Leek, Ivan Rungger

Abstract:

A qutrit represents a three-level quantum system, so that one qutrit can encode more information than a qubit, which corresponds to a two-level quantum system. This work investigates the potential of qutrit circuits in machine learning classification applications. We propose and evaluate different data-encoding schemes for qutrits, and find that the classification accuracy varies significantly depending on the used encoding. We therefore propose a training method for encoding optimization that allows to consistently achieve high classification accuracy, and show that it can also improve the performance within a data re-uploading approach. Our theoretical analysis and numerical simulations indicate that the qutrit classifier can achieve high classification accuracy using fewer components than a comparable qubit system. We showcase the qutrit classification using the encoding optimization method on a superconducting transmon qutrit, demonstrating the practicality of the proposed method on noisy hardware. Our work demonstrates high-precision ternary classification using fewer circuit elements, establishing qutrit quantum circuits as a viable and efficient tool for quantum machine learning applications.
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Details from ORA
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