Quantum Computing: Theory to Simulation and Programming

Explore how quantum mechanics at the nanoscale reveals a probabilistic nature, superposition, and entanglement, and how these phenomena enable quantum gates and quantum computing.

Explore how classical bits differ from quantum bits and superposition, revealing a probabilistic state that collapses to a definite value upon observation and enables exponential data encoding.

Explore quantum entanglement and tunnelling and how these phenomena power quantum computers beyond classical limits, delivering instant correlations across distances and tunnelling through barriers to explore solution spaces.

Discover how two qubits yield four states and how entanglement enables exponential information, illustrating why a 300-qubit system may require more classical bits than atoms in the universe.

Learn to program and simulate quantum circuits with the Cirque Python framework, including Python installation and running code from the command line or a file, plus verifying a demo circuit.

Create a 3x3 circuit in Cirque, apply an edge gate to distribute 50/50 probabilities, add X gates on specific qubits, and run the quantum simulator to observe results.

Explore insert strategies for quantum circuits, defining the initial qubit set, operations, and movements, and measuring the output using various insert strategies with caesar and h gates.

Create a classical simulator on a computer to model quantum phenomena, building a two-qubit circuit with sqrt(X) operations and the caesar gate, then measure and run it.

Learn the quantum Fourier transform by building and simulating a circuit in the Cirque framework, demystifying energy redistribution, phase distributions, and eight cubits across eight states from theory to code.

Define the Hadamard gate within a quantum Fourier transform circuit by constructing a 2x2 qubit grid, implementing the from ops method, and applying rotations and swaps in Python.

Build a grid quantum circuit with caesar_swap gates at 0.5 and 0.25 rotations on qubits a, b, and c, then simulate on the xmon simulator and show the final state.

Run the quantum project by installing dependencies, navigating to the working directory, and running the Python script to create a four-qubit circuit with a one-fourth rotation per qubit.

Install dwave ocean sdk and set up a virtual environment to run graph problems on a real quantum computer via wave leap, configuring api token and solver endpoint for Python.

Explore how graph problems underpin traffic analysis and optimal camera placement, compare classical and quantum approaches, and illustrate solving such graphs with devate quantum computer.

Builds a star graph with NetworkX, solves it on a quantum processing unit using a sampler and embedding composite to form a binary quadratic model, and compares to classical solver.

Explore constraint scheduling with a quantum computer, formulating a binary quadratic model and solving via a D-Wave quantum processing unit.

Binary quadratic models are the native language of quantum processors, turning problems into a two-variable quadratic form with X^2, Y^2, and XY terms, demonstrated via a constraint scheduling example.

Define a scheduling method that converts a time-location-length-mandatory problem into a binary quadratic model using binary CSP, and generate, print, and compare its linear and quadratic forms.

Run a binary quadratic model on a quantum processing unit using embedding and sampler workflows to extract minimum energy states and readable outputs such as time and location.

Define the minimum energy from server response, extract the first energy value, and run a program comparing quantum and classical solutions to linear constraints via a binary quadratic model.

Explore quadratic unconstrained binary optimization (qubo) models, converting boolean constraints into math, and simulate solving these problems on a quantum computer using basic logic gates.

Build a not gate on a quantum computer to simulate classical logic gates, and use quadratic unconstrained optimization to model valid input-output cases and energy minimization.

Explore how quadratic unconstrained binary optimization (QUBO) models a problem using binary decision variables, a Q matrix of constants, and penalty objective to distinguish valid and invalid input output pairs.

Learn to build a binary quadratic model for a not gate, set up a QUBO, run it on a quantum processor with a wave sampler, and analyze energies and occurrences.

Explore how quantum machine learning uses entanglement, superposition, and tunneling to optimize loss functions beyond classical programming, illustrated by a cup of coffee analogy.