Intel® Quantum SDK
Learn and write code today that runs on quantum hardware tomorrow.
Overview
The Intel® Quantum SDK is a complete quantum computing stack in simulation. It includes:
- An intuitive user interface
- A compiler toolchain
- A quantum runtime environment optimized for running hybrid quantum-classical algorithms
- A choice of qubit simulation back ends
Three Ways to Try the Intel Quantum SDK
Depending on your system memory, you can simulate a different number of Qubits. For details, see Total Qubits in the Simulator.
Hosted on the Intel® Tiber™ Developer Cloud
The Intel® Tiber™ Developer Cloud provides access to the Intel Quantum SDK for developers to explore hybrid quantum-classical algorithms in the context of using their application on high-performance hardware.
These options are available after you select the following Get Access button:
- To begin learning what quantum computing offers, sign up for access to the free compute sandbox.
- To access the Intel Quantum SDK through a trial account on the Intel Tiber Developer Cloud, in the Developer Cloud Console, go to the left tabs, select Learning, and then scroll to the Quantum Computing section.
For more information, see:
- Get Started
- The examples at:
ls /opt/intel/quantum_sdk/quantum-examples/
ls /opt/intel/quantum_sdk/python-quantum-examples/
Use the qBraid Cloud-based Quantum Computing Platform
- qBraid integrates many quantum computing kits into their all-in-one platform.
- The Intel Quantum SDK is preinstalled into qBraid Lab for all users.
In a Containerized Environment
- The Docker* image for Intel Quantum SDK provides the full feature set, allowing developers to experience quantum programming powered by their local machine.
- Get the containerized Intel Quantum SDK from Docker Hub*:
docker pull intellabs/intel_quantum_sdk:latest
Features
Code in Familiar Patterns or Powerful Expressions
The Intel Quantum SDK includes an LLVM*-based compiler extension providing intuitive C++ language extensions to program quantum algorithms. Developers can get started building quantum kernels simply by issuing quantum instruction calls in an imperative style.
As a component of the SDK, the Functional Language Extension for Quantum (FLEQ) takes a step beyond the imperative style. It provides powerful tools to enable the flexible and modular development of complex quantum logic.
Efficient Running of Hybrid Quantum-Classical Workflows
Thanks to the quantum runtime included in the Intel Quantum SDK, you can take advantage of the speed of C++ for processing classical data, and then use that data as dynamic inputs to the quantum algorithm. This creates the feedback loops needed for hybrid quantum-classical workflows that enable optimization algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) and variational quantum eigensolver (VQE).
Multiple Choices of Qubit Back Ends
A suite of qubit simulators brings quantum computing onto the CPU, and together they serve as the tools needed to validate algorithms that will run on quantum hardware that is probabilistic in nature.
- The Intel® Quantum Simulator is a state-vector simulation that allows you to implement your quantum algorithm or hybrid quantum-classical algorithm on a high-performance, qubit-agnostic back end. A flexible, adjustable noise model allows developers to test and validate quantum algorithms for the noisy hardware qubits of the current Noisy Intermediate-Scale Quantum (NISQ) era. The total number of qubits available in the simulation will depend on the memory of the host computer:
Total Qubits in the Simulator |
Approximate Free RAM Required |
24 |
430 MB |
26 |
1.2 GB |
28 |
4.3 GB |
30 |
16 GB |
32 |
67 GB |
- The tensor network back end simulates an even larger number of qubits for certain types of quantum circuits.
- The Clifford simulator back end provides extremely efficient computations for quantum circuits limited to a subset of the quantum gates.
- The Quantum Dot simulator back end incorporates the physics of the quantum dot qubit technology into the simulation of qubits. This back end simulates quantum hardware from Intel that is under development.
- If you need to customize a back end to your own needs, build your own qubit simulator with the user-defined back end in the SDK.
Choose the implementation that best supports the current step of your algorithm and application development path.
Release Notes & Documentation
Version 1.1.1
Release Notes
- Replaced repeated invocations of compiler optimizations with a single binary that results in:
- Reduced compilation time for large programs.
- Additional warnings for potential invalid blocks.
- Reduced file system access.
- A new driver that does not require any changes from the user in normal use.
- Changed behavior for generating intermediate representation (IR) files:
- The -k flag no longer generates IR files at each stage of compilation. It only outputs the final classical and quantum IR.
- New flags interrupt or start compilation flow at a specified step:
- -z (lowercase "z") denotes the starting stage for compilation.
- -Z (uppercase "Z") denotes the ending stage for compilation.
Version 1.1
Release Notes
- Addition of quantum expression (QExpr) and FLEQ:
- Version 1.1 introduces QExpr, a new data type for expressing quantum instructions, and APIs to express algorithms using a functional programming paradigm.
- FLEQ provides a powerful, succinct tool for defining quantum algorithms and APIs.
- Addition of more qubit simulation back ends:
- Tensor network back end: A new target back end uses tensor network methods to simulate quantum circuits with the ability to simulate large numbers of qubits for certain types of circuits.
- Clifford circuit back end: A new target back end that provides extremely fast results for simulating workloads with only Clifford quantum gates.
- A user-defined back end: A new interface provides a route to implementing your own qubit simulations as a back end for quantum workloads.
- Performance improvements to the existing back ends for Intel Quantum Simulator and the Quantum Dot Simulator.
- A new API provides the capability to add your own noise models for the qubits simulated through the Intel Quantum Simulator.
- When writing a quantum_kernel, developers can declare variables of type qbit in the local scope.
- Enhancement of qubit scheduling and placement, where users can now select between predefined algorithms for:
- Mapping software qubits (virtual) to hardware qubits (physical)
- Determining the order of quantum gate and instruction completion
- Added the ability to run user-selected passes and external passes:
- A new set of compiler features allows you to run optimization passes from LLVM, the Intel® Quantum Compiler, or external libraries. These can be added in between any or all of the unrolling, validation, synthesis, scheduling, or lowering steps of quantum compilation.
- Users can now run their own passes developed using the open sourced front end for the Intel Quantum Compiler.
- Documentation
- Get Started (85 KB)
- Tutorials (336 KB)
- Developer Guide and Reference (642 KB)
- Functional Language Extension Developer Guide and Reference (289 KB)
Version 1.0
Release Notes
- Addition of the Quantum Dot Simulator back end supports a new target back end for the full stack in simulation. This is a physics-based simulation of quantum dot qubits as well as simulation of control electronics.
- Addition of placement and scheduler passes provides automatic mapping and scheduling of quantum algorithms onto qubits. These passes treat the problem of mapping qubit-type variables onto the qubits as a physical resource and take into account the connectivity between qubits.
- Added support for Open Quantum Assembly Language (OpenQASM) 2.0 provides a source-to-source translator module that enables you to directly use programs that you have written to output OpenQASM 2.0.
- Added an interface to Python* to support interacting with the Python parts of the quantum computing ecosystem through a choice of writing and compiling OpenQASM 2.0 or by compiling your own shared object.
- Changes to the API naming convention follows LLVM-style to provide predictability in the future. Several of the existing methods' names in the API of the Intel Quantum SDK were standardized so that they are compliant. This onetime breaking of the user API helps increase future user friendliness.
Join the Discussion
Ask questions and get answers from Intel engineers and from peers working with the Intel Quantum SDK.
Join the Intel® DevHub: Visit the Channels and Roles tab and the intel-quantum-sdk channel.
Events
Past
- IEEE International Conference on Rebooting Computing 2023
- IEEE / ACM International Conference on Computer-Aided Design 2023
- The 32nd International Symposium on High-Performance Parallel and Distributed Computing (HPDC) 2023
- IEEE Quantum Week 2023
- IEEE Quantum Week 2022
- Supercomputing (SC2022)
- Design Automation Conference (DAC 2022)
- Intel® Innovation 2023
- Intel® Innovation 2022
- Intel Quantum SDK Challenge (January, Munich)
Research and Training
Build a Quantum Ecosystem
Intel is increasing accessibility for quantum developers by using the industry standard LLVM compiler—a more user-friendly interface familiar to classical computing developers. Users included:
- The Deggendorf Institute of Technology in Munich, Germany, is using the SDK to explore a fluid dynamics problem important for aerodynamics and hydrodynamics.
- Leidos* is exploring applications like computational chemistry and materials modeling as well as distributed computing with data privacy and security.
Intel is funding curriculum to help build an ecosystem of developers to begin exploring programming applications for quantum computing. Universities are developing and sharing quantum course curricula to proliferate the use of the Intel Quantum SDK.
Core Concepts
Find out more about the core concepts of quantum computing and review C++ and LLVM standards.