Jupyter Tutorials

• Circuit Basics
  • Overview
  • Setup
  • Hello world example
  • Parameterized Quantum Circuit (PQC)
  • Advances for Circuit
    • Input State
    • Monte Carlo Noise Simulation
    • Apply Arbitrary Gate
    • Exponential Gate
    • Non-unitary Gate
• Quantum Approximation Optimization Algorithm (QAOA)
  • Overview
  • Max-Cut Problem
  • QAOA for Max-Cut
  • The code
    • Define the Graph
    • Parameterized Quantum Circuit (PQC)
    • Optimization
    • Results
  • Classical Method
  • Weighted Max-Cut Problem
• Optimizing QAOA by Bayesian Optimization (BO)
  • Overview
  • Setup
  • MAX-CUT Hamiltonian
    • Define the Graph
    • Brutal Force Result
  • QAOA Ansatz
  • Adam Optimizer
  • BO Optimizer
  • TuRBO Optimizer
  • DARBO Optimizer
  • Optimization
  • Results
• Quantum Approximation Optimization Algorithm (QAOA) for Not-all-equal 3-satisfiability (NAE3SAT)
  • Overview
  • Not-all-equal 3-satisfiability (NAE3SAT)
  • QAOA for NAE3SAT
    • Setup
    • Define the Graph
    • Parameterized Quantum Circuit (PQC)
    • Optimization
    • Results
  • Classical Method
  • Hard Problem
• Quantum Dropout for QAOA
  • Overview
  • NAE3SAT and Hard Problem
  • Quantum Dropout
    • Setup
    • Isotropic Quantum Dropout
    • Random Quantum Dropout
• VQE on 1D TFIM
  • Overview
  • Background
  • Setup
  • Higher-level API
    • Batched VQE Example
    • Different Backends
  • Lower-level API
• QML on MNIST Classification
  • Overview
  • Setup
  • Data Processing
  • Using vectorized_value_and_grad API
    • Jax Backend Compatibility
    • Training Using tf.data
  • Using tf.keras API
    • Quantum-Classical Hybrid Model in Keras
    • Hybrid Model in Jax
• QML in PyTorch
  • Overview
  • Setup
  • Wrap Quantum Function Using torch_interface
  • Batched Version
  • Hybrid Model with Classical Post-processing
• Quantum Machine Learning for Classification Task
  • Overview
  • Dataset and Pre-processing
  • Amplitude Encoding
  • Classical Post-processing
  • PCA Embedding
  • Data Re-uploading
• Variational Quantum Eigensolver (VQE) on Molecules
  • Overview
  • Setup
  • Generate Hamiltonian
  • VQE Setup
• VQE on 1D TFIM with Different Hamiltonian Representation
  • Overview
  • Setup
  • Parameters
  • Parameterized Quantum Circuits
  • Pauli-string Operators
    • Energy
    • Main Optimization Loop
  • Sparse Matrix, Dense Matrix, and MPO
    • Hamiltonian
    • Generate QuOperator
    • Energy
    • Main Optimization Loop
    • Sparse Matrix, Dense Matrix, and MPO
• MERA
  • Overview
  • Background
  • Setup
  • Energy
  • MERA circuit
  • Train
  • Compare
• Gradient Evaluation Efficiency Comparison
  • Overview
  • Setup
  • Qiskit Gradient Framework Benchmark
  • TensorCircuit Automatic Differentiation Benchmark
• The usage of contractor
  • Overview
  • Setup
  • Testbed System
  • Opt-einsum
  • Cotengra
• Operator spreading
  • Overview
  • Setup
    • Parameters
    • The Unitary Matrix \(U(t)\) of the Quantum Circuit
    • Operator at \(t=0\)
    • Operator Spreading
    • Results
  • Reference
• Optimization vs. expressibility of the circuit
  • Overview
  • Setup
    • Parameters
    • Exact Diagonalization
    • The Density Matrix of the Output State
    • Hamiltonian
    • Loss Function
    • Rényi Entanglement Entropy
    • Main Optimization Loop
    • Results
    • Reference
• Probing Many-body Localization by Excited-state VQE
  • Overview
  • Setup
    • Parameters
    • Construct Circuit
    • Construct Hamiltonian
    • Loss Function
    • Eigenstate Witness
  • Research Project
• Differentiable Quantum Architecture Search
  • Overview
  • Setup
  • Problem Description
  • Probability Ensemble Approach
  • Directly Optimize the Structure Parameters
  • Final Fine-tune
  • References
• Barren Plateaus
  • Overview
  • Setup
  • Parameters
  • Generating QNN
  • Batch Variance Computation
  • Results
• Solving QUBO Problem using QAOA
  • Overview
  • QUBO problem
    • What is QUBO?
    • General Case
    • An example
  • Setup
  • Solve this problem
  • Improve the performance with CVaR
• Portfolio Optimization
  • Introduction
  • Solving portfolio optimization problems with QAOA
  • Set up
  • Import data
  • Classical method
    • Use QAOA
  • Use XY mixer to improve the performance
• Solving the Ground State of Hamiltonian by Imaginary-time Evolution
  • Overview
  • Setup
  • Classical Hamiltonian
    • Define the Hamiltonian
    • Variational wave function
    • Optimization
    • Results
  • Quantum Hamiltonian
    • Define the Hamiltonian
    • Variational wave function
    • Optimization
    • Results
• Classical Shadows in Pauli Basis
  • Overview
  • Setup
  • Construct the Classical Shadow Snapshots
  • Estimate the Expectation Values of Observables
  • Estimate the Entanglement Entropy
  • Reconstruct the Density Matrix
• Support Vector Classification with SKLearn
  • Overview
  • Setup
  • Data Preprocessing
  • Quantum Model
  • Wrapping Quantum Model into a SVC
  • Create Traditional SVC
  • Test
  • Issue with SKLearn
  • Conclusion
• Demo on TensorCircuit SDK for Tencent Quantum Cloud
  • Import and Setup
  • Devices and properties
  • Tasks
  • Cloud simulator
  • Compling: gate decomposition and qubit mapping
  • Readout Error Mitigation
  • High level API