Qiskit

Qiskit Terra provides tools to create quantum circuits at or close to the level of quantum machine code.[13] It allows the processes that run on quantum hardware to be explicitly constructed in terms of quantum gates. It also provides tools to allow quantum circuits to be optimized for a particular device, as well as managing batches of jobs and running them on remote-access quantum devices and simulators.

The following shows a simple example of Qiskit Terra. In this, a quantum circuit is created for two qubits, which consists of the quantum gates required to create a Bell state. The quantum circuit then ends with quantum measurements, which extract a bit from each qubit.

from qiskit import QuantumCircuit, Aer, execute

qc = QuantumCircuit(2, 2)

qc.h(0)
qc.cx(0, 1)
qc.measure([0,1], [0,1])

Once the quantum circuit has been created, it can be run on a backend (either quantum hardware or a simulator). In the following example, a local simulator is used.

backend = Aer.get_backend('qasm_simulator')
job_sim = execute(qc, backend)
sim_result = job_sim.result()
print(sim_result.get_counts(qc))

The final print statement here will show the results returned by the backend. This is a Python dictionary that describes the bit strings obtained from multiple runs of the quantum circuit. In the quantum circuit used in this example, the bit strings '00' and '11' should be the only possible results, and should occur with equal probability. The full results will therefore typically have the samples split approximately equally between the two, such as {'00':519, '11':505} .

Experiments done on quantum hardware using Qiskit Terra have been used in many research papers,[14] such as in tests of quantum error correction[15] ,[16] generation of entanglement[17] and simulation of far-from-equilibrium dynamics[18] .

Qiskit Aqua provides tools that can be used without any explicit quantum programming required by the user themselves.[19] It currently supports applications in chemistry, AI, optimization and finance. Users can provide problems and receive results defined using standard tools in these domains, such as PySCF for chemistry. Qiskit Aqua then implements a corresponding quantum algorithm.

In the near-term, development of quantum software will depend largely on simulation of small quantum devices. For Qiskit, this is provided by the Aer component. This provides simulators hosted locally on the user's device, as well as HPC resources available through the cloud.[20] The simulators can also simulate the effects of noise for simple and sophisticated noise models.[21]

Ignis is a component that contains tools for characterizing noise in near-term devices, as well as allowing computations to be performed in the presence of noise. This is includes tools for benchmarking near-term devices, error mitigation and error correction.[22]