Meet Aquila,
QuEra's 256-qubit
quantum processor

Available now on Amazon Braket

Aquila is QuEra’s first-generation machine. Its core is based on programmable arrays of neutral Rubidium atoms, trapped in vacuum by tightly focused laser beams.

  • Large and powerful
    With up to 256 qubits and tens of coherent Rabi oscillations, Aquila is uniquely suitable for simulating quantum dynamics at scales that are impractical for classical resources.

  • Robust to noise
    Operating in the analog quantum processing mode, Aquila performs continuous temporal control over its qubits. This solves one of the key issues for today’s gate-based computers: the compounding of gate errors. Entanglement is generated and manipulated via direct design of Aquila’s atomic natural Hamiltonian.

  • Flexible programmability
    With customer-defined qubit layout and connectivity, Aquila enables unique strategies for algorithm development. Aquila is ready for the easy deployment of applications in quantum simulation, optimization, and machine learning.

Getting Started

General access to Aquila can be obtained through the Amazon Braket service.

Programming instructions can be generated via the AWS Braket SDK, based on Python.

Need help? Check out some examples here, or visit the Amazon Braket forum.


Amazon Braket availability windows*:

QuEra’s direct customers and partners enjoy top-tier support as well as premium access to new functionality.

Reach out to us to inquire about our partnership models.

Technical Specifications

Aquila’s analog mode operation covers a wide family of Hamiltonians within the format

\(\frac{H}{\hbar} = \sum\limits_{i} \frac{\Omega(t)}{2} \left( e^{i \phi(t) } | 0_i \rangle  \langle 1_i | + h.c. \right) - \Delta(t)\sum\limits_{i} \hat{n}_i + \sum\limits_{i < j} V_{ij} \hat{n}_i \hat{n}_j\)
\(n_i = |1_i \rangle \langle 1_i |\)
\(V_{ij} = \frac{C_6}{\left|r_i - r_j \right|^6}\)

General parameter ranges can be seen on the table to the right and more information can be found in the getting started example.

Table of user controllable parameters

Best Practices

Programming analog quantum computers is a powerful way to develop applications for large quantum systems efficiently. To best leverage the strengths of this operation mode, make sure to:

Leverage parallel processing
Aquila’s 256 qubits and large field of view imply ample capacity to decompose problems into copies of small clusters or long chains for 1D problems. Make sure to leverage this capacity to parallelize your calculations and increase your throughput.
Leverage the geometry
Reprogramming qubit positions and controlling their connectivity means that a wide range of problems can be mapped to Aquila’s native Hamiltonian. Don’t forget to leverage this to create different lattices, encode gauge constraints, and explore optimization in a wide range of graph mapping strategies, as done here.
Think analog, not digital
Smooth time evolutions substitute digital gates here. Remember that several protocols benefit from avoiding the errors of compounding gates, such as quantum evolution by Trotterization.
Use the Rydberg blockade, but don’t forget interaction tails
The Rydberg blockade controls several applications for Aquila, including some ordered phases and the computation of Maximum Independent Sets. Despite these strong-interaction constraints, longer distance interaction tails matter, enabling longer-range frustration, stabilizing spin liquids and more.
Push the boundaries
Don’t always rely on classical simulation for benchmark. Aquila’s capacity for quantum dynamical evolution is at the limit of classical.

Using Aquila

Click here for a full end-to-end demonstration of Aquila.