Release Note

New Features

More quantum gates

• Add controlled gates cswap(), cy(), cz(), crx(), cyy(), crz(), and ccx() in the UAnsatz class.
• Add the S dagger gate sdg() and the T dagger gate tdg() in the UAnsatz class

Gradient calculation

• Add the ExpecVal class in paddle_quantum.expecval, which is a PaddlePaddle Python operator for calculating the expectation value of an observable w.r.t the quantum state output by a quantum circuit. It supports the computation of the gradient w.r.t. the circuit's parameters either through the finite difference method or the parameter-shift method.
• Add the paddle_quantum.optimizer module for using SciPy optimizers to train a circuit, including ConjugateGradient, NewtonCG, Powell, SLSQP, and CustomOptimizer. The CustomOptimizer class is a base class for all the other optimizers.
• Add finite_difference_gradient(), param_shift_gradient(), and linear_combinations_gradient() in the UAnsatz class for computing the gradient of the expectation of an observable w.r.t. the parameters in a circuit.

Hamiltonian

Add the Hamiltonian class in paddle_quantum.utils for processing Hamiltonian. The main functions of this class are:

• Construct a Hamiltonian from a Pauli string;
• Get the matrix corresponding to the Hamiltonian;
• Get the Pauli string corresponding to the Hamiltonian;
• Addition, subtraction and scalar multiplication.

Classical shadow

• Add shadow_sample() in paddle_quantum.shadow for sampling local Pauli measurement.
• Add shadow_trace() in the UAnsatz class for estimating the expectation value of an observable through classical shadows.

Quantum finance

• Add the paddle_quantum.finance module for handling some finance optimization problems.

Other features

• Add swap_test() in paddle_quantum.circuit for constructing a swap test circuit.
• Add reset_state() in the UAnsatz class for resetting a partial state.
• Update basis_encoding(), amplitude_encoding(), and angle_encoding() to support encoding classical data to partial quantum state.
• Update vec() in paddle_quantum.state to support generating arbitrary computational basis state.
• Add plot_state_in_bloch_sphere() and plot_rotation_in_bloch_sphere() in paddle_quantum.utils for drawing a Bloch sphere.

New Tutorials

Gradient calculation

• Add the tutorial Calculating Gradient Using Quantum Circuit under the qnn_research folder, which explains how to calculate gradient with quantum circuits and then use PaddlePaddle's or SciPy's optimizers to train an ansatz.

Quantum finance

• Add three tutorials on Arbitrage Opportunity Optimization, Portfolio Optimization, and Portfolio Diversification under the combinatorial_optimization folder, which use the new paddle_quantum.finance module to solve these problems with quantum optimization algorithms.

Bug Fix

• Fix the bug of t() gate in paddle_quantum.locc.