Superconducting

Google Quantum AI

First to claim quantum supremacy in 2019. Willow (2024) demonstrated below-threshold quantum error correction, a foundational milestone for fault-tolerant quantum computing.

About Google Quantum AI

From quantum supremacy to below-threshold error correction

Google's quantum computing effort began with the Xmon qubit architecture and the Bristlecone 72-qubit processor, before culminating in the 2019 announcement that Sycamore had performed a specific sampling task in 200 seconds that Google estimated would take a classical supercomputer 10,000 years. The result, published in Nature, is the most-cited quantum supremacy demonstration to date, though classical simulation algorithms have since closed some of that gap.

In December 2024, Google published results from Willow, a 105-qubit processor, demonstrating that surface code logical error rates decrease as the code distance grows past a threshold. This is the first experimental confirmation of below-threshold error correction, meaning adding more physical qubits to the error correcting code actively improves, rather than just changes, the logical qubit fidelity. It is a landmark result for the long-term viability of fault-tolerant quantum computing.

The primary SDK for Google's hardware is Cirq, an open-source Python framework for writing, simulating, and running quantum circuits. Cirq is designed around the specific gate sets and connectivity of Google's hardware, with native support for the fSim gate family that appears on Sycamore-architecture processors. Alongside Cirq, OpenFermion handles quantum chemistry, TensorFlow Quantum handles quantum machine learning, and ReCirq provides research-grade circuit tools used in Google's published experiments.

Hardware Specifications

System specs at a glance

Specification Value
Current systems Willow (105q, 2024), Sycamore (53q, 2019), Bristlecone (72q, research)
Qubit technology Superconducting transmon qubits (Xmon design)
Two-qubit gate fidelity ~99.7% on Willow
Readout fidelity 99%+
Connectivity 2D grid (nearest-neighbor)
Error correction Below-threshold surface code demonstrated (Willow, 2024)
Benchmarking method Cross-entropy benchmarking (XEB)
Cloud access Google Cloud research program (application required)
Primary SDK Cirq (open source, Python)
Supported SDKs Cirq, OpenFermion, TensorFlow Quantum, ReCirq

Use Cases

Where Google Quantum AI hardware excels

Getting Started

Start building with Cirq

  1. Install Cirq

    pip install cirq

    Cirq installs with all simulators included. No API key is needed to run circuits against the local simulator. For quantum chemistry, also install openfermion and openfermion-cirq. For QML, install tensorflow-quantum.

  2. Build and simulate a Bell state

    import cirq

# Define two qubits on a grid
q0, q1 = cirq.LineQubit.range(2)

# Build a Bell state circuit
circuit = cirq.Circuit([
    cirq.H(q0),
    cirq.CNOT(q0, q1),
    cirq.measure(q0, q1, key='result'),
])

print(circuit)

# Simulate locally
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=1000)
print(result.histogram(key='result'))

    Cirq circuits use explicit qubit objects (GridQubit, LineQubit, NamedQubit). GridQubit matches Google's 2D hardware connectivity; LineQubit is convenient for linear experiments.

  3. Use Google's native fSim gate

    import cirq
import numpy as np

q0, q1 = cirq.GridQubit(0, 0), cirq.GridQubit(0, 1)

# fSim is Google's native 2-qubit gate (theta, phi parameterized)
fsim = cirq.FSimGate(theta=np.pi/4, phi=np.pi/6)

circuit = cirq.Circuit([
    fsim(q0, q1),
    cirq.measure(q0, q1, key='m'),
])

print(circuit)

    The fSim gate is native to Sycamore-family hardware. Writing circuits directly with fSim avoids decomposition overhead that occurs when using CNOT on Google hardware. See the Cirq reference for gate decomposition details.

  4. Add noise modeling for realistic simulation

    import cirq

q0, q1 = cirq.LineQubit.range(2)

# Build a noise model approximating Sycamore-class hardware
noise = cirq.ConstantQubitNoiseModel(cirq.depolarize(p=0.005))

circuit = cirq.Circuit([cirq.H(q0), cirq.CNOT(q0, q1), cirq.measure(q0, q1, key='m')])

noisy_simulator = cirq.DensityMatrixSimulator(noise=noise)
result = noisy_simulator.run(circuit, repetitions=500)
print(result.histogram(key='m'))

    Depolarizing noise with p=0.005 approximates Sycamore two-qubit gate error rates. Use noise modeling during algorithm development before applying for hardware access.

  5. Apply for hardware access

    Google Quantum AI hardware access is not available via a public queue. Researchers can apply through the Google Quantum AI research program. Applications are reviewed based on scientific merit, research goals, and alignment with Google's quantum computing roadmap. Students and academic groups with strong proposals are encouraged to apply.

Access

How to access Google Quantum AI hardware

Google does not offer a public pay-per-use hardware queue. Access to real QPUs is through the research collaboration program. However, free simulation via Cirq is available to anyone.

Learning Resources

Tutorials and reference docs