Amazon Braket supported devices - Amazon Braket

Amazon Braket supported devices

In Amazon Braket, a device represents a QPU or simulator that you can call to run quantum tasks. Amazon Braket provides access to five QPU devices — from IonQ, Oxford Quantum Circuits, QuEra, Rigetti, and Xanadu, three on-demand simulators, and embedded simulators. For all devices, you can find further device properties, such as device topology, calibration data, and native gate sets, on the Devices tab of the Amazon Braket console or by means of the GetDevice API. When constructing a circuit with the simulators, Amazon Braket currently requires that you use contiguous qubits or indices. If you are working with the Amazon Braket SDK, you have access to device properties as shown in the following code example.

from braket.aws import AwsDevice from braket.devices import LocalSimulator device = AwsDevice('arn:aws:braket:::device/quantum-simulator/amazon/sv1') #SV1 # device = LocalSimulator() #Local State Vector Simulator # device = LocalSimulator("default") #Local State Vector Simulator # device = LocalSimulator(backend="default") #Local State Vector Simulator # device = LocalSimulator(backend="braket_sv") #Local State Vector Simulator # device = LocalSimulator(backend="braket_dm") #Local Density Matrix Simulator # device = AwsDevice('arn:aws:braket:::device/quantum-simulator/amazon/tn1') #TN1 # device = AwsDevice('arn:aws:braket:::device/quantum-simulator/amazon/dm1') #DM1 # device = AwsDevice('arn:aws:braket:::device/qpu/ionq/ionQdevice') #IonQ # device = AwsDevice('arn:aws:braket:::device/qpu/rigetti/Aspen-11') #Rigetti Aspen-11 # device = AwsDevice('arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-2') #Rigetti Aspen M-2 # device = AwsDevice('arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy') #OQC Lucy # device = AwsDevice('arn:aws:braket:us-east-1::device/qpu/xanadu/Borealis') #Xanadu Borealis # device = AwsDevice('arn:aws:braket:us-east-1::device/qpu/quera/Aquila') #QuEra Aquila # get device properties device.properties

Supported QPUs

Supported simulators

Choose the best simulator for your task

Note

To view the available AWS Regions for each device, scroll right across the following table.

Amazon Braket devices
Provider Device Name Paradigm Type Device ARN Region

IonQ

ionQdevice

gate-based

QPU

arn:aws:braket:::device/qpu/ionq/ionQdevice

us-east-1

Oxford Quantum Circuits

Lucy

gate-based

QPU

arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy

eu-west-2

QuEra

Aquila

analog Hamiltonian simulation

QPU

arn:aws:braket:us-east-1::device/qpu/quera/Aquila

us-east-1

Rigetti

Aspen-11

gate-based

QPU

arn:aws:braket:::device/qpu/rigetti/Aspen-11

us-west-1

Rigetti

Aspen M-2

gate-based

QPU

arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-2

us-west-1

Xanadu

Borealis

continuous-variable

QPU

arn:aws:braket:us-east-1::device/qpu/xanadu/Borealis

us-east-1

AWS

braket_sv

gate-based

Simulator

N/A (local simulator in Braket SDK)

N/A

AWS

braket_dm

gate-based

Simulator

N/A (local simulator in Braket SDK)

N/A

AWS

SV1

gate-based

Simulator

arn:aws:braket:::device/quantum-simulator/amazon/sv1

All Regions where Amazon Braket is available.

AWS

DM1

gate-based

Simulator

arn:aws:braket:::device/quantum-simulator/amazon/dm1

All Regions where Amazon Braket is available.

AWS

TN1

gate-based

Simulator

arn:aws:braket:::device/quantum-simulator/amazon/tn1

us-west-2, us-east-1, and eu-west-2

To view additional details about the QPUs you can use with Amazon Braket, see Amazon Braket Hardware Providers.

IonQ

IonQ offers a gate-based QPU based on ion trap technology. IonQ’s trapped ion QPUs are built on a chain of trapped 171Yb+ ions that are spatially confined by means of a microfabricated surface electrode trap within a vacuum chamber.

The IonQ device supports the following quantum gates.

'x', 'y', 'z', 'rx', 'ry', 'rz', 'h', 'cnot', 's', 'si', 't', 'ti', 'v', 'vi', 'xx', 'yy', 'zz', 'swap', 'i'

With verbatim compilation, the IonQ device supports the following native gates.

'gpi', 'gpi2', 'ms'

These native gates can only be used with verbatim compilation. To learn more about verbatim compilation, see Verbatim Compilation.

Rigetti

Rigetti quantum processors are universal, gate-model machines based on all-tunable superconducting qubits. The Rigetti Aspen-11 system is based on scalable 40-qubit node technology. The Rigetti Aspen-M-2 system leverages their proprietary multi-chip technology and is assembled from two 40-qubit processors.

The Rigetti devices support the following quantum gates.

'cz', 'xy', 'ccnot', 'cnot', 'cphaseshift', 'cphaseshift00', 'cphaseshift01', 'cphaseshift10', 'cswap', 'h', 'i', 'iswap', 'phaseshift', 'pswap', 'rx', 'ry', 'rz', 's', 'si', 'swap', 't', 'ti', 'x', 'y', 'z'

With verbatim compilation, the Rigetti devices support the following native gates.

'rx', 'rz', 'cz', 'cphaseshift', 'xy'

Rigetti superconducting quantum processors can run the 'rx' gate with only the angles of ±π/2 or ±π.

Pulse-level control is available on the Rigetti devices, which support a set of predefined frames that are of the following types:

'rf', 'rf_f12', 'ro_rx', 'ro_rx', 'cz', 'cphase', 'xy'

See Roles of frames and ports for more information about these frames.

Oxford Quantum Circuits (OQC)

OQC quantum processors are universal, gate-model machines, built using scalable Coaxmon technology. The OQC Lucy system is an 8-qubit device with the topology of a ring in which each qubit is connected to its two nearest neighbors.

The Lucy device supports the following quantum gates.

'ccnot', 'cnot', 'cphaseshift', 'cswap', 'cy', 'cz', 'h', 'i', 'phaseshift', 'rx', 'ry', 'rz', 's', 'si', 'swap', 't', 'ti', 'v', 'vi', 'x', 'y', 'z', 'ecr'

With verbatim compilation, the OQC device supports the following native gates.

'i', 'rz', 'v', 'x', 'ecr'

Pulse-level control is available on the OQC devices. The OQC devices support a set of predefined frames that are of the following types:

'drive', 'second_state', 'measure', 'acquire', 'cross_resonance', 'cross_resonance_cancellation'

OQC devices support dynamic declaration of frames provided that you supply a valid port identifier. See Roles of frames and ports for more information about these frames and ports.

Note

When using pulse control with OQC, the length of your programs cannot exceed a maximum of 90 microseconds. It is important to remember that the duration’s upper bound of single-qubit and two-qubit gates is approximately 50 nanoseconds and 1 microsecond, respectively. These numbers may vary depending on the qubits used, the device’s current calibration and the circuit compilation.

QuEra

QuEra offers neutral-atom based devices that can run analog Hamiltonian simulation (AHS) tasks. These special-purpose devices faithfully reproduce the time-dependent quantum dynamics of hundreds of simultaneously interacting qubits.

One can program these devices in the paradigm of analog Hamiltonian simulation by prescribing the layout of the qubit register and the temporal and spatial dependence of the manipulating fields. Amazon Braket provides utilities to construct such programs via the AHS module of the python SDK, braket.ahs.

For more information, see the Analog Hamiltonian Simulation example notebooks or the Submit an analog program using QuEra’s Aquila page.

Xanadu

Xanadu builds photonic quantum computers that use continuous variables for quantum computing known as qumodes instead of traditional two-level systems or qubits. In the case of the Borealis QPU, each qumode represents the quantized electromagnetic field of a laser pulse traveling through the device. Gates between two qumodes are implemented by interfering two temporally separated qumodes via programmable beam splitters and delay lines. Instead of the usual one- and two-qubit gates, such as Hadamard or CNOT, continuous variable quantum computing uses gates such as rotation, displacement, beam-splitting, and squeezing.

Xanadu’s photonic quantum computer Borealis is not a universal machine, capable of arbitrary quantum computation, but instead implements a specific protocol known as Gaussian boson sampling (GBS). GBS is a model of photonic quantum computation first introduced by Hamilton et al. that consists of multi-mode linear optical operations followed by photon-counting measurements. The Borealis device implements GBS with 216 temporally-spaced qumodes. Amazon Braket offers access to Borealis via the open-source Strawberry Fields library for photonic quantum computation.

For more information, see the Borealis example notebook

Local state vector simulator (braket_sv)

The local state vector simulator (braket_sv) is part of the Amazon Braket SDK that runs locally in your environment. It is well-suited for rapid prototyping on small circuits (up to 25 qubits) depending on the hardware specifications of your Braket notebook instance or your local environment.

The simulator supports all gates in the Amazon Braket SDK, but QPU devices support a smaller subset. You can find the supported gates of a device in the device properties.

Note

The local simulator supports advanced OpenQASM features which may not be supported on QPU devices or other simulators. For more information on supported features, see the examples provided in the OpenQASM Local Simulator notebook.

For more information about how to work with simulators, see the Amazon Braket examples.

Local density matrix simulator (braket_dm)

The local density matrix simulator (braket_dm) is part of the Amazon Braket SDK that runs locally in your environment. It is well-suited for rapid prototyping on small circuits with noise (up to 12 qubits) depending on the hardware specifications of your Braket notebook instance or your local environment.

You can build common noisy circuits from the ground up using gate noise operations such as bit-flip and depolarizing error. You can also apply noise operations to specific qubits and gates of existing circuits that are intended to run both with and without noise.

The braket_dm local simulator can provide the following results, given the specified number of shots:

  • Reduced density matrix: Shots = 0

Note

The local simulator supports advanced OpenQASM features, which may not be supported on QPU devices or other simulators. For more information about supported features, see the examples provided in the OpenQASM Local Simulator notebook.

To learn more about the local density matrix simulator, see the Braket introductory noise simulator example.

State vector simulator (SV1)

SV1 is a fully managed, high-performance, universal state vector simulator. It can simulate circuits of up to 34 qubits. You can expect a 34-qubit, dense, and square circuit (circuit depth = 34) to take approximately 1–2 hours to complete, depending on the type of gates used and other factors. Circuits with all-to-all gates are well suited for SV1. It returns results in forms such as a full state vector or an array of amplitudes.

SV1 has a maximum runtime of 6 hours. It has a default of 35 concurrent tasks, and a maximum of 100 (50 in us-west-1 and eu-west-2) concurrent tasks.

SV1 results

SV1 can provide the following results, given the specified number of shots:

  • Sample: Shots > 0

  • Expectation: Shots >= 0

  • Variance: Shots >= 0

  • Probability: Shots > 0

  • Amplitude: Shots = 0

For more about results, see Result types.

SV1 is always available, it runs your circuits on demand, and it can run multiple circuits in parallel. The runtime scales linearly with the number of operations and exponentially with the number of qubits. The number of shots has a small impact on the runtime. To learn more, visit Compare simulators.

Simulators support all gates in the Braket SDK, but QPU devices support a smaller subset. You can find the supported gates of a device in the device properties.

Density matrix simulator (DM1)

DM1 is a fully managed, high-performance, density matrix simulator. It can simulate circuits of up to 17 qubits.

DM1 has a maximum runtime of 6 hours, a default of 35 concurrent tasks, and a maximum of 50 concurrent tasks.

DM1 results

DM1 can provide the following results, given the specified number of shots:

  • Sample: Shots > 0

  • Expectation: Shots >= 0

  • Variance: Shots >= 0

  • Probability: Shots > 0

  • Reduced density matrix: Shots = 0, up to max 8 qubits

For more information about results, see Result types.

DM1 is always available, it runs your circuits on demand, and it can run multiple circuits in parallel. The runtime scales linearly with the number of operations and exponentially with the number of qubits. The number of shots has a small impact on the runtime. To learn more, see Compare simulators.

Noise gates and limitations

AmplitudeDamping Probability has to be within [0,1] BitFlip Probability has to be within [0,0.5] Depolarizing Probability has to be within [0,0.75] GeneralizedAmplitudeDamping Probability has to be within [0,1] PauliChannel The sum of the probabilities has to be within [0,1] Kraus At most 2 qubits At most 4 (16) Kraus matrices for 1 (2) qubit PhaseDamping Probability has to be within [0,1] PhaseFlip Probability has to be within [0,0.5] TwoQubitDephasing Probability has to be within [0,0.75] TwoQubitDepolarizing Probability has to be within [0,0.9375]

Tensor network simulator (TN1)

TN1 is a fully managed, high-performance, tensor network simulator. TN1 can simulate certain circuit types with up to 50 qubits and a circuit depth of 1,000 or smaller. TN1 is particularly powerful for sparse circuits, circuits with local gates, and other circuits with special structure, such as quantum Fourier transform (QFT) circuits. TN1 operates in two phases. First, the rehearsal phase attempts to identify an efficient computational path for your circuit, so TN1 can estimate the runtime of the next stage, which is called the contraction phase. If the estimated contraction time exceeds the TN1 simulation runtime limit, TN1 does not attempt contraction.

TN1 has a runtime limit of 6 hours. It is limited to a maximum of 10 (5 in eu-west-2) concurrent tasks.

TN1 results

The contraction phase consists of a series of matrix multiplications. The series of multiplications continues until a result is reached or until it is determined that a result cannot be reached.

Note: Shots must be > 0 for the TN1 simulator.

Result types include:

  • Sample

  • Expectation

  • Variance

For more about results, see Result types.

TN1 is always available, it runs your circuits on demand, and it can run multiple circuits in parallel. To learn more, see Compare simulators.

Simulators support all gates in the Braket SDK, but QPU devices support a smaller subset. You can find the supported gates of a device in the device properties.

Visit the Amazon Braket GitHub repository for a TN1 example notebook to help you get started with TN1.

Best practices for working with the TN1 simulator

  • Avoid all-to-all circuits.

  • Test a new circuit or class of circuits with a small number of shots, to learn the circuit’s "hardness" for TN1.

  • Split large shot simulations over multiple tasks.

PennyLane’s lightning simulators

In addition to the Braket simulators, we also support PennyLane’s lightning simulators. With PennyLane’s lightning simulators, you can leverage advanced gradient computation methods, such as adjoint differentiation, to evaluate gradients faster. The lightning.qubit simulator is available as a device via Braket NBIs and as an embedded simulator, whereas the lightning.gpu simulator needs to be run as an embedded simulator with a GPU instance. See the Embedded simulators in Braket Jobs notebook for an example of using lightning.gpu. For more information about embedded simulators, see the Run a job with Amazon Braket Hybrid Jobs.

Compare simulators

This section helps you select the Amazon Braket simulator that’s best suited for your task, by describing some concepts, limitations, and use cases.

Choosing between local simulators and managed simulators (SV1, TN1, DM1)

The performance of local simulators depends on the hardware that hosts the local environment, such as a Braket notebook instance, used to run your simulator. Managed simulators run in the AWS cloud and are designed to scale beyond typical local environments. Managed simulators are optimized for larger circuits, but add some latency overhead per task or batch of tasks. This can imply a trade-off if many tasks are involved. Given these general performance characteristics, the following guidance can help you choose how to run simulations, including ones with noise.

For simulations:

  • When employing fewer than 18 qubits, use a local simulator.

  • When employing 18–24 qubits, choose a simulator based on the workload.

  • When employing more than 24 qubits, use a managed simulator.

For noise simulations:

  • When employing fewer than 9 qubits, use a local simulator.

  • When employing 9–12 qubits, choose a simulator based on the workload.

  • When employing more than 12 qubits, use the DM1 simulator.

What is a state vector simulator?

The Amazon Braket state vector simulator (SV1) is a universal state vector simulator. It stores the full wave function of the quantum state and sequentially applies gate operations to the state. It stores all possibilities, even the extremely unlikely ones. The SV1 simulator’s run time for a task increases linearly with the number of gates in the circuit.

What is a density matrix simulator?

The Amazon Braket density matrix simulator (DM1) simulates quantum circuits with noise. It stores the full density matrix of the system and sequentially applies the gates and noise operations of the circuit. The final density matrix contains complete information about the quantum state after the circuit runs. The runtime generally scales linearly with the number of operations and exponentially with the number of qubits.

What is a tensor network simulator?

The Amazon Braket tensor network simulator (TN1) encodes quantum circuits into a structured graph.

  • The nodes of the graph consist of quantum gates, or qubits.

  • The edges of the graph represent connections between gates.

As a result of this structure, TN1 can find simulated solutions for relatively large and complex quantum circuits.

The TN1 simulator requires two phases

Typically, TN1 operates in a two-phase approach to simulating quantum computation.

  • The rehearsal phase: In this phase, TN1 comes up with a way to traverse the graph in an efficient manner, which involves visiting every node so that you can obtain the measurement you desire. As a customer, you do not see this phase because TN1 performs both phases together for you. It completes the first phase and determines whether to perform the second phase on its own based on practical constraints. You have no input into that decision after the simulation has begun.

  • The contraction phase: This phase is analogous to the execution phase of a computation in a classical computer. The phase consists of a series of matrix multiplications. The order of these multiplications has a great effect on the difficulty of the computation. Therefore, the rehearsal phase is accomplished first in order to find the most effective computation paths across the graph. After it finds the contraction path during the rehearsal phase, TN1 contracts together the gates of your circuit to produce the results of the simulation.

TN1 graphs are analogous to a map

Metaphorically, you can compare the underlying TN1 graph to the streets of a city. In a city with a planned grid, it is easy to find a route to your destination using a map. In a city with unplanned streets, duplicate street names, and so forth, it can be difficult to find a route to your destination by looking at a map.

If TN1 did not perform the rehearsal phase, it would be like walking around the streets of the city to find your destination, instead of looking at a map first. It can really pay off in terms of walking time to spend more time looking at the map. Similarly, the rehearsal phase provides valuable information.

You might say that the TN1 has a certain “awareness” of the structure of the underlying circuit that it traverses. It gains this awareness during the rehearsal phase.

Types of problems best suited for each of these types of simulators

SV1 is well-suited for any class of problems that rely primarily on having a certain number of qubits and gates. Generally, the time required grows linearly with the number of gates, while it does not depend on the number of shots. SV1 is generally faster than TN1 for circuits under 28 qubits.

The SV1 simulator can be slower for higher qubit numbers because it actually simulates all possibilities, even the extremely unlikely ones. It has no way to determine which outcomes are likely. Thus, for a 30-qubit evaluation, SV1 must calculate 2^30 configurations. The limit of 34 qubits for the Amazon Braket SV1 simulator is a practical constraint due to memory and storage limitations. You can think of it like this: Each time you add a qubit to the SV1 simulator, the problem becomes twice as hard.

For many classes of problems, the TN1 simulator can evaluate much larger circuits in realistic time than the SV1 simulator because TN1 takes advantage of the structure of the graph. It essentially tracks the evolution of solutions from its starting place and it retains only the configurations that contribute to an efficient traversal. Put another way, it saves the configurations to create an ordering of matrix multiplication that results in a simpler evaluation process.

For TN1, the number of qubits and gates matters, but the structure of the graph matters a lot more. For example, TN1 is very good at evaluating circuits (graphs) in which the gates are short-range (that is, each qubit is connected by gates only to its nearest neighbour qubits), and circuits (graphs) in which the connections (or gates) have similar range. A typical range for TN1 is having each qubit talk only to other qubits that are 5 qubits away. If most of the structure can be decomposed into simpler relationships such as these, which can be represented in more, smaller, or more uniform matrices, TN1 performs the evaluation easily.

Limitations of the TN1 simulator

The TN1 simulator can be slower than the SV1 simulator depending on the graph’s structural complexity. For certain graphs, TN1 terminates the simulation after the rehearsal stage, and shows a status of FAILED, for either of these two reasons:

  • Cannot find a path — If the graph is too complex, it is too difficult to find a good traversal path and the simulator gives up on the computation. TN1 cannot perform the contraction. You may see an error message similar to this one: No viable contraction path found.

  • Contraction stage is too difficult — In some graphs, TN1 can find a traversal path, but it is very long and extremely time-consuming to evaluate. In this case, the contraction is so expensive that the cost would be prohibitive and instead, TN1 exits after the rehearsal phase. You may see an error message similar to this one: Predicted runtime based on best contraction path found exceeds TN1 limit.

Note

You are billed for the rehearsal stage of TN1 even if contraction is not performed and you see a FAILED status.

The predicted runtime also depends on the shot count. In worst-case scenarios, TN1 contraction time depends linearly on the shot count. The circuit may be contractable with fewer shots. For example, you might submit a task with 100 shots, which TN1 decides is uncontractable, but if you resubmit with only 10, the contraction proceeds. In this situation, to attain 100 samples, you could submit 10 tasks of 10 shots for the same circuit and combine the results in the end.

As a best practice, we recommend that you always test your circuit or circuit class with a few shots (for example, 10) to find out how hard your circuit is for TN1, before you proceed with a higher number of shots.

Note

The series of multiplications that forms the contraction phase begins with small, NxN matrices. For example, a 2-qubit gate requires a 4x4 matrix. The intermediate matrices required during a contraction that is adjudged to be too difficult are gigantic. Such a computation would require days to complete. That’s why Amazon Braket does not attempt extremely complex contractions.

Concurrency

All Braket simulators give you the ability to run multiple circuits concurrently. Concurrency limits vary by simulator and region. For more information on concurrency limits, see the Quotas page.

Example notebooks

Amazon Braket provides a variety of example notebooks showing the types of circuits that can either work well for, or challenge, the TN1 and SV1 simulators, such as the quantum Fourier transformation (QFT).