Quantum computing in a time span of the past few years, has experienced immense growth not only in the field of construction of quantum hardware but in the development of quantum algorithms as well. The development of algorithms with the availability of Noisy Intermediate Scale Quantum (NISQ) computers to understand the power of these machines is today of increasing importance.
However, a common problem that arises when on a NISQ processor a quantum algorithm is designed is how to take full advantage of these quantum devices that are limited. Adding on, some quantum processors have other nuances and complex geometric constraints as well. And ignoring these will either result in quantum computation that is faulty, or a modified and sub-optimal computation.
The Google AI Quantum team at the First International Workshop on Quantum Software and Quantum Machine Learning(QSML), made the introduction of An Open Source Framework For NISQ Computers: The Public Alpha Of Cirq.
What is Criq?
The open source framework for NISQ computers called Cirq is focused on near-term questions and helping researchers understand whether NISQ quantum computers have the potential of solving computational problems that are of practical importance. The framework is licensed under Apache 2, and in any commercial or open source package is free to be modified or embedded.
Once Cirq is installed, it enables researchers to write for specific quantum processors the quantum algorithms. The running of these algorithms is supported by Cirq locally on a simulator and is designed to easily integrate with future quantum hardware or larger simulators via the cloud.
Also announcing OpenFermion-Cirq:
The team also announces the release of, an example of a Cirq based application enabling near-term algorithms: OpenFermion-Cirq. OpenFermion is a platform for developing for chemistry problems the quantum algorithms, and OpenFermion-Cirq is an open source library which to Cirq compiles quantum simulation algorithms.
The new library uses the latest advances in building low depth quantum algorithms for quantum chemistry problems to enable users to go from the details of a chemical problem to highly optimized quantum circuits customized to run on particular hardware.
Quantum computing if it is going to realize its full potential will require strong cross-industry and academic collaborations. In building Cirq, for NISQ computers they mainly worked with early testers in order to gain feedback as well as insight into algorithm design. Given are some examples of Cirq work that were resulted from these early adopters:
Zapata Computing: Simulation Of A Quantum Autoencoder
QC Ware: QAOA Implementation And Integration Into QC Ware’s AQUA Platform
Quantum Benchmark: Integration Of True-Q Software Tools For Assessing And Extending Hardware Capabilities
Heisenberg Quantum Simulations: Simulating The Anderson Model
Cambridge Quantum Computing: Integration Of Proprietary Quantum Compiler T|Ket>
NASA: Architecture-Aware Compiler Based On Temporal-Planning For QAOA And Simulator Of Quantum Computers.
This day, the Google AI Quantum team is making the use of Cirq in order to create circuits that run on the Bristlecone processor of Google. In the nearing future, the team further plans to make this processor available in the cloud as well, and Cirq will then be the interface in which users for this processor write programs.
But In the meantime, it is greatly hoped that Cirq will improve the productivity of Noisy Intermediate Scale Quantum (NISQ) algorithm developers as well as researchers everywhere.
More information: Cirq
Github: Click Here