Superconducting quantum computing

Superconducting quantum computing is a branch of quantum computing and solid-state physics that implements superconducting electronic circuits as qubits in a quantum processor. These devices are typically microwave-frequency electronic circuits containing Josephson junctions, which are fabricated on solid state chips. Superconducting circuits are one of many possible physical implementations of qubits, the quantum computer's equivalent of a traditional bit in a classic computer. Qubits refer to a two-state quantum mechanical system, and have two logic states, the ground state and the excited state, often denoted | g ⟩ and | e ⟩ {\displaystyle |g\rangle {\text{ and }}|e\rangle } (for ground and excited), or | 0 ⟩ and | 1 ⟩ {\displaystyle |0\rangle {\text{ and }}|1\rangle } . Superconducting quantum computing implementations are categorized as "solid state" quantum computers, where qubits are intrinsically integrated in a solid-state device. Solid state quantum computers also borrow fabrication techniques developed for solid state classical computation. Superconducting architecture is the dominant method in the industry for developing quantum processing units, or QPUs. Research in superconducting quantum computing is conducted by companies such as Google, IBM, IMEC, BBN Technologies, Rigetti, and Intel. Alternatives to superconducting qubits include trapped ions, and neutral atoms. Ongoing research in superconducting quantum computing includes device-level improvement, developing error correction methods, and demonstrating quantum advantage by comparing a quantum processor's performance to a classical computer.

History Quantum computers were first proposed by Richard Feynman, who in 1982 proposed using such a computer to simulate and understand the properties of other quantum systems. In the 1990s, two quantum algorithms were published, which further stirred interest in realizing quantum computers. Peter Shor proposed Shor's algorithm, a quantum algorithm for finding the prime factors of an integer, which could in theory break RSA encryption. Similarly, Lov Grover proposed the Grover search algorithm, which provides an alternative to binary search that can be done with quadratic speedup.

At the time, superconducting quantum circuits were already being used to construct highly sensitive SQUID devices, and had also been used to demonstrate macroscopic quantum phenomena, such as quantized energy levels. It became apparent that these superconducting qubits could be used to achieve quantum computation. This was especially true because such "solid state" approaches to quantum computing were seen as far more viable than other approaches at the time, including NMR (nuclear magnetic resonance) quantum computing, due in part to the fact that existing fabrication techniques would apply. In 1999, a paper was published by Yasunobu Nakamura, demonstrating the first superconducting qubit. It is a form of Cooper pair box, now known as the "charge qubit". Although the design had been proposed in 1997 by the Saclay team (including Devoret), this paper was the first to show coherent control and readout, in the form of Rabi oscillations between the ground and excited states of the qubit. However, even after this first result, it was unclear if superconducting qubits would be viable, and some argued that the system was not truly capable of containing quantum information. Part of the problem was that this initial design maintained coherence for less than a nanosecond, not long enough to do any calculations.

In the following years, several other superconducting qubits were invented, including the phase qubit, flux qubit, quatronium, the transmon qubit, and the fluxonium. Successive advances in qubit design, materials science, and fabrication techniques improved coherence by roughly five orders of magnitude over two decades. The transmon design, introduced in 2007, reduced sensitivity to charge noise and has since become the dominant architecture in superconducting quantum computing processors deployed by IBM, Google, and others. Transmon coherence times were brought into the range of 10 - 100 μ s {\displaystyle 10{\text{ - }}100\ \mu {\text{s}}} , though further gains on that architecture plateaued for nearly a decade. In 2021, a fluxonium qubit achieved a T 2 {\displaystyle T_{2}} coherence time exceeding 1 ms, an improvement by an order of magnitude. In 2025, developments in readout and design allowed superconducting transmon qubits to reach millisecond coherence times as well. Google in 2016, implemented 16 qubits to convey a demonstration of the Fermi-Hubbard Model. In another experiment, Google used 17 qubits to optimize the Sherrington-Kirkpatrick model. In 2019, Google produced the Sycamore quantum computer which performed a task in 200 seconds that Google claimed would have taken 10,000 years on a classical computer. The task was random circuit sampling, a common benchmark for claims of "quantum supremacy" or quantum advantage. As of 2025, superconducting quantum processors have exceeded 1,000 qubits, the largest being IBM Condor, a 1,121-qubit quantum processor. In 2025, Google announced one of the first independently verifiable quantum advantages on hardware using the Willow processor.

Background

Quantum computing

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