Exploiting dynamic quantum circuits in a quantum algorithm with superconducting qubits

TL;DR

This paper demonstrates the use of dynamic quantum circuits on superconducting qubits to perform adaptive quantum phase estimation, showing potential advantages over non-adaptive methods in noisy, low-latency quantum hardware.

Contribution

It introduces and experimentally explores dynamic quantum circuits in superconducting systems, enabling real-time adaptive quantum algorithms beyond traditional static circuits.

Findings

Dynamic circuits can outperform static ones in noisy environments.

Real-time processing within circuits is feasible on superconducting hardware.

Adaptive quantum phase estimation shows tangible advantages over non-adaptive methods.

Abstract

The execution of quantum circuits on real systems has largely been limited to those which are simply time-ordered sequences of unitary operations followed by a projective measurement. As hardware platforms for quantum computing continue to mature in size and capability, it is imperative to enable quantum circuits beyond their conventional construction. Here we break into the realm of dynamic quantum circuits on a superconducting-based quantum system. Dynamic quantum circuits involve not only the evolution of the quantum state throughout the computation, but also periodic measurements of a subset of qubits mid-circuit and concurrent processing of the resulting classical information within timescales shorter than the execution times of the circuits. Using noisy quantum hardware, we explore one of the most fundamental quantum algorithms, quantum phase estimation, in its adaptive version, which exploits dynamic circuits, and compare the results to a non-adaptive implementation of the same algorithm. We demonstrate that the version of real-time quantum computing with dynamic circuits can offer a substantial and tangible advantage when noise and latency are sufficiently low in the system, opening the door to a new realm of available algorithms on real quantum systems.