Fermioniq provides Ava, a proprietary tensor network quantum emulator designed for scalable simulation of quantum algorithms and hardware testing. This platform allows application developers to benchmark algorithms and hardware developers to create customizable digital twins for research guidance. It supports complex operations, noise modeling, and integrates easily with standard quantum programming frameworks like Cirq and Qiskit.
Funding
$293.6K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Quantum algorithm development and hardware research are limited by the computational cost and scalability of simulating quantum circuits, especially as qubit counts increase and complex operations are introduced. Traditional full-state emulation methods face exponential scaling in memory requirements, hindering the design, benchmarking, and testing of quantum algorithms at scale.
Solution
Fermioniq's Ava is a next-generation quantum emulator that addresses these limitations by using proprietary tensor network techniques to compress quantum states, enabling scalable simulation without a proportional increase in cost. This allows application developers to design, benchmark, and test quantum algorithms at scale, and hardware developers to leverage customizable digital twins to guide quantum hardware research. Ava supports the simulation of cross-talk, readout errors, intermediate measurements, and classical control, facilitating the optimization of error correction protocols and online training of variational circuits. The platform offers flexible input, seamless integration with Cirq and Qiskit, and multiple plans for on-demand and dedicated access via a lightweight Python client or the NVIDIA CUDA-Q platform.
Target Audience
The primary users are quantum application developers designing and testing algorithms, and quantum hardware developers using digital twins to guide research.
Features
- Tensor network emulation to compress quantum states, enabling simulation of larger circuits with fewer resources.
- Customizable noise models to simulate cross-talk and readout errors.
- Support for intermediate measurements and classical control for optimizing error correction protocols.
- Online training of variational circuits for VQE, QAOA, and QML algorithms.
- Seamless integration with Cirq and Qiskit.
- Cloud access via a lightweight Python client.
- Integration with the NVIDIA CUDA-Q platform.
- Support for simulating qudits, leakage, and other complex operations.