Key features
Accelerated emulations

Access to exclusive photonic emulators

Quandela and Scaleway join forces to deliver a unique and appealing offer that leverages high-performance GPU capabilities, ensuring lightning-fast computes that outpace conventional CPUs hundredfold (see benchmark).

Increased qubit count

Up to error-free 31 qubits

Quantum emulation consumption increases exponentially with the qubit count, making it challenging to run more than a few qubits on a local desktop. Our optimized platforms address this issue by allowing stable and larger-scale emulations to design wider algorithms without grappling with material errors.

Many platforms

0-setup quantum computing

We offer to researchers and developers fast and convenient quantum computing emulation platforms. A Scaleway quantum platform is an emulated quantum processing unit (QPU) by a powerful Scaleway instance. Each platform billed per minute depending on the emulator and the underlying instance cost.

Perceval built-in provider

Perceval built-in provider

Perceval is a quantum photonic development kit written in Python to design and run circuits on your local machine or on a remote computer. Scaleway is embedded into Perceval as a built-in provider to run quantum circuits on remote instances. To design your photonic circuits, you can rely on a set of Jupyter Notebook tutorials.

Dedicated Sessions

Dedicated sessions

Start a platform’s session then run your quantum jobs without dealing with queuing delays of mutualized hardware. Once a session is out, you keep the trace of your jobs. If you manage a training workshop or want to collaborate with others, you can share sessions among participants with a deduplication system.


Globalized service

Focus on your quantum business and algorithm design, we handle the run. Scaleway’s data centers are settled on 3 european regions: fr-par, pl-waw and nl-ams powered by sustainable energies.

Why does quantum emulation matter?

Quantum emulators are significantly valuable in the current and upcoming periods, serving as primary means to design quantum algorithms, free from constraints associated with quantum hardware.

In the current landscape, quantum computers are prone to produce errors during operations as illustrated in Figure 1. This Noisy Intermediate Scale Quantum (NISQ) era will persist until the emergence of Fault-Tolerant Quantum Computing (FTQC). In the meantime, emulation stands as the only way to simulate error-free qubits.

Emulation into the quantum ecosystem Emulation into the quantum ecosystem

Emulators are time saving tools that offer to researchers and programmers an accessible platform to explore and develop quantum algorithms, eliminating the reliance and limitation of physical quantum computers.

By narrowing the gap between quantum computing and industrial applications, emulators play an enabler role in training new generations of programmers and preparing the ground for the widespread adoption of quantum technology.

A new step for photonic emulation

Our benchmarking method for quantum emulators is to run growing circuits until we reach hardware limitations. We create square circuits, e.g. 24x24 or 30x30, denoting the number of qubits and the number of quantum gates (depth) respectively. These gates are randomly selected from Pauli X, Y, Z and Hadamar.

These squared-circuits, designed on Perceval SDK, were executed on popular local setups and on our Quantum as a Service across different platforms.

Benchmark for 1000 shots on square-circuits Benchmark for 1000 shots on square-circuits

As depicted in Figure 2, our GPU-accelerated platforms exhibit a substantial computation speedup for equivalent circuit size, taking less than a second compared to 241 seconds for Apple M2 or 695 seconds for an Intel i7.

Moreover, in local setups we encounter limitations by running squared-circuits with more than 11 qubits due to excessive memory requirement. In contrast our H100 GPU-accelerated platform, enables us to extend up to 31 qubits in 2h.

Type of platformSimulation providerSimulation instanceIntegrationsMax qubit countSpeed performances (for 20 photons circuits)Price
sim:sampling:p100QuandelaNvidia Tesla P100Perceval, API29 photons,
80 modes*
sim:sampling:h100QuandelaNvidia H100Perceval, API31 photons,
192 modes*

* 2 modes, 1 photon can be used to encode 1 logical qubit
** for 10K photon sampling
*** Limited offer, metered by minute

Use cases
Machine learning

Quantum machine learning (QML) algorithms like quantum neural networks and variational quantum algorithms (VQA) provide advantages in handling large datasets and optimizing complex models. This emerging field can combine algorithm and data from classical and/or quantum fields leading to exciting hybrid approaches.

AI pre-processing and data embedding

Even if we are still in the Noisy Intermediate Simulated Quantum (NISQ) era, we can leverage quantum circuits for the midterms to enhance dimensional reduction and data encoding. This new kind of pre-processing can greatly increase your dataset entropy.


Simulating the behavior of molecules and materials at the quantum level is computationally intensive and often intractable for classical computers. Quantum algorithms can potentially model molecular interaction with high precision. Leading to advancements in drug discovery, science of materials and climate modeling.


Quantum algorithms accelerate at solving optimization problems, which involve finding the best solution from a large set of possible solutions. They explore multiple possibilities simultaneously, allowing for more efficient solution to complex challenges.

Cheat Sheet

Install Perceval SDK


$ pip3 install --upgrade pip
$ pip3 install perceval_quandela==0.10.4

    Create a platform session

    import perceval.providers.scaleway as scw

    session = scw.Session(


    Get and configure your remote processor

    import perceval as pcvl

    proc = session.build_remote_processor()
    proc.set_circuit(pcvl.Circuit(m=2, name="circuit") // pcvl.BS.H())

    Finally, run the circuit and get your job result

    sampler = pcvl.algorithm.Sampler(proc)
    job = sampler.samples(10)

    Close your session once your work is done



    Quantum computing is a field of computing that relies on quantum mechanics to perform specific kinds of computations much more efficiently than classical “binary” computers.

    A quantum digit, or qubit / qbit, is the elementary storage unit in quantum computing. Qubits, like classical bits, are based on physical phenomena to store data.

    Quandela produces photonic quantum computing, so photons are used to store and manipulate data. These photons are manipulated through tiny light fibers called modes and operations are performed with beam splitters and phase shifters. Handling a photon directly instead of an abstract qubit allows it to take a significant advantage on computation.

    Logical qubit is an abstract information storage built to be fault tolerant and time resilient to quantum operations. It is built on a set of hundreds or thousands of physical qubits. Physical qubits tend to be closer to the quantum material and more unstable.

    No, all quantum algorithm jobs sent to our platform ran on an emulated quantum processing unit.

    Of course, be patient! We plan to release new platforms based on new underlying hardware, or new underlying emulation layer

    Noisy Intermediate Simulated Quantum era corresponds to our current period where quantum computers are too noisy and error prone to make relevant computations. That is why quantum emulators are emerging to allow developers to design and experiment quantum algorithms.

    Not yet. But you can already use Qiskit and convert it to a Perceval compatible circuit.

    Yes, the QaaS service will be available as an API into the Scaleway Python SDK.

    Yes, have a look onto the Perceval github page.

    No, this is a proprietary asset.