Pith. sign in

Implementing a Ternary Decomposition of the Toffoli Gate on Fixed-FrequencyTransmon Qutrits

1 Pith paper cite this work. Polarity classification is still indexing.

1 Pith paper citing it
abstract

Quantum computation is conventionally performed using quantum operations acting on two-level quantum bits, or qubits. Qubits in modern quantum computers suffer from inevitable detrimental interactions with the environment that cause errors during computation, with multi-qubit operations often being a primary limitation. Most quantum devices naturally have multiple accessible energy levels beyond the lowest two traditionally used to define a qubit. Qudits offer a larger state space to store and process quantum information, reducing complexity of quantum circuits and improving efficiency of quantum algorithms. Here, we experimentally demonstrate a ternary decomposition of a multi-qubit operation on cloud-enabled fixed-frequency superconducting transmons. Specifically, we realize an order-preserving Toffoli gate consisting of four two-transmon operations, whereas the optimal order-preserving binary decomposition uses eight \texttt{CNOT}s on a linear transmon topology. Both decompositions are benchmarked via truth table fidelity where the ternary approach outperforms on most sets of transmons on \texttt{ibmq\_jakarta}, and is further benchmarked via quantum process tomography on one set of transmons to achieve an average gate fidelity of 78.00\% $\pm$ 1.93\%.

fields

quant-ph 1

years

2025 1

verdicts

UNVERDICTED 1

representative citing papers

A short review on qudit quantum machine learning

quant-ph · 2025-05-08 · unverdicted · novelty 1.0

This survey summarizes the potential of qudit-based quantum machine learning for expressivity and resource efficiency, along with current hardware demonstrations and remaining challenges.

citing papers explorer

Showing 1 of 1 citing paper.

  • A short review on qudit quantum machine learning quant-ph · 2025-05-08 · unverdicted · none · ref 24 · internal anchor

    This survey summarizes the potential of qudit-based quantum machine learning for expressivity and resource efficiency, along with current hardware demonstrations and remaining challenges.