(quantum computing) Examples of code for a "Bell state" : IBM quantum composer vs. OpenQASM vs. Qiskit

 

• https://quantum-computing.ibm.com/composer/files/new "Quantum Composer" IDE to build Q-progs : "Composer has a customizable set of tools that allow you to build, visualize, and run quantum circuits on quantum hardware or simulators".
• graphical editor for openQASM  or Qiskit formalisms

Examples of code generated in the step by step tutorial to create a "Bell state"

https://quantum-computing.ibm.com/composer/docs/iqx/example-circuits/bell

Same code, but different languages; done on the Quantum Composer ide following the initial step by step guide.

The Bell test demonstrates that measurements of an entangled state cannot be explained by any local hidden variable theory, and that there must be correlations that are beyond classical.

from: https://quantum-computing.ibm.com/composer/docs/iqx/example-circuits/bell

* Quantum Composer

* OpenQASM

OPENQASM 2.0;

include "qelib1.inc";

qreg q[2];

creg c[2];

h q[0];

cx q[0],q[1];

measure q[0] -> c[0];

measure q[1] -> c[1];

* Qiskit

from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit

from numpy import pi

qreg_q = QuantumRegister(2, 'q')

creg_c = ClassicalRegister(2, 'c')

circuit = QuantumCircuit(qreg_q, creg_c)

circuit.h(qreg_q[0])

circuit.cx(qreg_q[0], qreg_q[1])

circuit.measure(qreg_q[0], creg_c[0])

circuit.measure(qreg_q[1], creg_c[1])

Quantum computing resources

Quantum platforms, libraries, and learning platforms

Qiskit : open source lib

"Qiskit Machine Learning introduces fundamental computational building blocks - such as Quantum Kernels and Quantum Neural Networks - used in different applications, including classification and regression. On the one hand, this design is very easy to use and allows users to rapidly prototype a first model without deep quantum computing knowledge. On the other hand, Qiskit Machine Learning is very flexible, and users can easily extend it to support cutting-edge quantum machine learning research."

• https://qiskit.org/documentation/qc_intro.html quantum computing (QC) in a nutshell
• https://qiskit.org/documentation/machine-learning/ ML on QC 
• https://learn.qiskit.org/course/machine-learning/introduction 
• https://github.com/Qiskit/qiskit-machine-learning Github repo for Qiskit

DELF University quantum program

Long time research center on quantum computing. (sources < TPE 2001)

• https://qutech.nl 
• https://qutech.nl/research-engineering/quantum-computing/  (qu-algo & qu-prog)
• https://qutech.nl/research-engineering/quantum-internet/ (qu-network)
• https://qutech.nl/research-engineering/qubit-research/ (qu-infra)
• https://qutechacademy.nl/online-learning/online-courses/  Massive Online Open Courses (MOOC).

Community

community focused on quantum machine learning

• https://www.pennylane.ai/qml/   QML Quantum machine learning

IBM

• https://quantum-computing.ibm.com/ "IBM offers cloud access to the most advanced quantum computers available. Learn, develop, and run programs with our quantum applications and systems.
• https://quantum-computing.ibm.com/docs/
• https://quantum-computing.ibm.com/composer/files/new "Quantum Composer" IDE to build Q-progs : "Composer has a customizable set of tools that allow you to build, visualize, and run quantum circuits on quantum hardware or simulators".
• graphical editor for openQASM or Qiskit formalisms

source : 

• meetup  Technical Group by IBM, 
• 03 Nov 2022 https://www.meetup.com/technical-group-hosted-by-ibm/events/287144510/ 
• Sean Wagner, IBM Quantum Technical Ambassador