The Tool Desk
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What you need to get started
- A computer with enough memory and compute for the circuit and simulation method you choose.
- A supported Python environment and a simulator package, such as Qiskit Aer or Microsoft QDK.
- A defined circuit and output goal: for example, sampled measurements, a statevector, or a density matrix.
There is no single hardware specification for every quantum simulation. Resource use varies with circuit structure, the representation being simulated, and the method used. A local simulator is software modeling a circuit; it is not a substitute for the behavior of a physical quantum processor.
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Choose a local simulator
Qiskit Aer
Qiskit Aer simulates quantum circuits locally and provides multiple simulation methods. The Qiskit Aer 0.17.1 getting-started guide covers setup, and its AerSimulator reference describes simulator options. GPU support depends on the method and installation: the referenced documentation lists statevector, density-matrix, unitary, and tensor-network methods, with tensor-network described as GPU-only. Check the method support and installation instructions for the exact version you plan to use; Aer defaults to CPU simulation.
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Microsoft QDK
Microsoft’s QDK Python package offers sparse, Clifford, GPU, and CPU simulators. Its installation guide lists Python 3.10 or later and explains how to install and run the simulators: How to install and run the QDK quantum simulators. Microsoft describes these tools as a way to test how programs run on quantum hardware, but a simulator does not reproduce every behavior of a physical processor. See the QDK simulator overview for the available simulator types.
NVIDIA CUDA-Q
CUDA-Q can run on CPU-only systems; a GPU is required for its GPU-based simulators. Supported operating systems, CPU architectures, and Python versions vary by release, so consult NVIDIA’s local installation guide before choosing a setup.
How much memory and compute do you need?
IBM’s quantum debugging documentation says exact simulation requirements depend on several factors. It gives approximately 27 qubits on a system with 4 GB of RAM as an illustrative example—not a universal capacity guarantee or a benchmark for every circuit or simulation method. See IBM’s introduction to debugging tools.
Rank #2
Use that figure only as a rough point of reference. Memory requirements and run time can change with circuit structure, the chosen representation, and what results you need. More memory may allow larger simulations or faster results, but memory alone does not make all circuits equally tractable.
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Start with CPU simulation unless you have a workload that is too slow or too large for your current setup. A GPU is not required for basic local work, and GPU acceleration is not available for every simulator method. Before buying or configuring one, verify all of the following:
- The simulator and specific method support GPU execution.
- The package installation enables that support and matches the version you intend to use.
- Your operating system, GPU hardware, drivers, and any required CUDA components are compatible.
- The workload is one that can benefit from the supported GPU method.
The available documentation supports a compatible CUDA-capable GPU as an optional route, but does not establish a best model, price, or speedup. Do not buy a GPU solely because a project is described as a quantum physics simulation.
Choose the method around the physics problem
Before settling on a backend, identify how your problem is represented as a circuit and what output you need. Those choices determine which methods are relevant and what resources to estimate.
- Circuit structure: Clifford circuits may be a good fit for stabilizer simulation. Other circuit structures may require a different method.
- Output: Decide whether you need a statevector, density matrix, sampled measurements, or another representation.
- Noise: If you need to model hardware noise, confirm that the simulator supports the noise model and understand how it represents the device.
- Scale: Estimate memory and compute needs for the specific circuit and method rather than extrapolating from one qubit-count example.
- Workflow: Check that the tool accepts your program format and supports your operating system, Python version, package version, and any accelerator dependencies.
Local simulation or a real quantum processor?
Local simulators are useful for computational modeling and testing programs without access to a quantum processor. If the goal requires behavior from actual quantum hardware, a local simulation is not equivalent: access to a physical processor is a separate requirement. Likewise, more compute—such as a GPU, multiple GPUs, or distributed resources—can help only when the simulator, method, and workload support it.
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