Tag Archives: Quantum

qsim integrates with NVIDIA cuQuantum SDK to accelerate quantum circuit simulations on NVIDIA GPUs

To make quantum computers useful, we need algorithms which cleverly use the unique properties of quantum hardware to solve problems intractable on classical computers. High performance quantum circuit simulation helps researchers and developers around the world build and test novel quantum algorithms. We recently launched major new features in qsim, Google Quantum AI’s open source quantum circuit simulator, that make it more performant, intuitive, and realistic.

Today, we are excited to announce an integration between qsim and the NVIDIA cuQuantum SDK. This integration will enable qsim users to make the most of GPUs when developing quantum algorithms and applications. NVIDIA CEO Jensen Huang announced the integration between Google Quantum AI's open source software stack and NVIDIA's cuQuantum SDK in the NVIDIA GTC conference keynote this morning.

To use the cuQuantum SDK with qsim, users can follow the usual workflow for simulating quantum circuits on a virtual machine, enabling cuQuantum in their simulation command.

Moving image depicting steps to create a quantum circuit setup Google Compute Engine and simulate circuit with qsim(cuQuantum SDK enabled) on GCP

Get started with quantum algorithm development using qsim+cuQuantum and the rest of the Google Quantum AI open source stack here.



By Sergei Isakov, Catherine Vollgraff Heidweiller (Google Quantum AI)


Upgrading qsim, Google Quantum AI’s Open Source Quantum Simulator

Quantum computing represents a fundamental shift in computation and gives us the opportunity to make important classically intractable problems solvable. To realize the full potential of quantum computing, we first need to build an error-corrected quantum computer. While we are actively working on our hardware roadmap, today’s quantum hardware is expected to remain a scarce resource. This makes software for simulating quantum circuits and emulating quantum hardware a critical enabler for quantum algorithm development.

To help researchers and developers around the world develop quantum algorithms right now, we are making qsim, our open source quantum circuit simulator, more performant and intuitive, and more “hardware-like”. Our recently published white paper provides a description of the theory and software optimizations that underpin qsim.

Launched in 2020, qsim allows quantum algorithm researchers to simulate quantum circuits developed with our algorithm libraries such as TensorFlow Quantum and OpenFermion, and our quantum programming framework Cirq.

With the upgraded version of qsim, users can back their simulations with high performance processors such as GPUs and ultramem CPU’s via Google Cloud, and distribute simulations over multiple compute nodes.
Moving image of steps to create, setup, and simulate circuit with qsim on GCP

The enriched noisy simulation featureset provides researchers with a “hardware-like” simulation experience for developing applications for the Noisy Intermediate-Scale Quantum Computers (also referred to as NISQ hardware) that exists today. Trajectory simulation speeds up noisy simulations with an efficient stochastic procedure which replaces noiseless gates with quantum channels. A new software routine1 for approximating Google Quantum AI NISQ processor specific hardware noise in simulations, can be used by any developer to test and iterate on algorithm prototypes containing up to 32 qubits. Simulation with processor-specific approximate NISQ noise is expected to advance NISQ applications research, because it allows researchers to account for the dominant hardware error mechanisms in real NISQ devices when prototyping algorithms—using error measurements performed on Google quantum processors.

Get started with quantum algorithm development using the Google Quantum AI open source stack here.



By Sergei Isakov, Dvir Kafri, Orion Martin and Catherine Vollgraff Heidweiller – Google Quantum AI


Notes

  1. Public code release for this feature is coming up  ↩


Upgrading qsim, Google Quantum AI’s Open Source Quantum Simulator

Quantum computing represents a fundamental shift in computation and gives us the opportunity to make important classically intractable problems solvable. To realize the full potential of quantum computing, we first need to build an error-corrected quantum computer. While we are actively working on our hardware roadmap, today’s quantum hardware is expected to remain a scarce resource. This makes software for simulating quantum circuits and emulating quantum hardware a critical enabler for quantum algorithm development.

To help researchers and developers around the world develop quantum algorithms right now, we are making qsim, our open source quantum circuit simulator, more performant and intuitive, and more “hardware-like”. Our recently published white paper provides a description of the theory and software optimizations that underpin qsim.

Launched in 2020, qsim allows quantum algorithm researchers to simulate quantum circuits developed with our algorithm libraries such as TensorFlow Quantum and OpenFermion, and our quantum programming framework Cirq.

With the upgraded version of qsim, users can back their simulations with high performance processors such as GPUs and ultramem CPU’s via Google Cloud, and distribute simulations over multiple compute nodes.
Moving image of steps to create, setup, and simulate circuit with qsim on GCP

The enriched noisy simulation featureset provides researchers with a “hardware-like” simulation experience for developing applications for the Noisy Intermediate-Scale Quantum Computers (also referred to as NISQ hardware) that exists today. Trajectory simulation speeds up noisy simulations with an efficient stochastic procedure which replaces noiseless gates with quantum channels. A new software routine1 for approximating Google Quantum AI NISQ processor specific hardware noise in simulations, can be used by any developer to test and iterate on algorithm prototypes containing up to 32 qubits. Simulation with processor-specific approximate NISQ noise is expected to advance NISQ applications research, because it allows researchers to account for the dominant hardware error mechanisms in real NISQ devices when prototyping algorithms—using error measurements performed on Google quantum processors.

Get started with quantum algorithm development using the Google Quantum AI open source stack here.



By Sergei Isakov, Dvir Kafri, Orion Martin and Catherine Vollgraff Heidweiller – Google Quantum AI


Notes

  1. Public code release for this feature is coming up  ↩