QCHack-2022

KO | EN

gitlite — search

#hacktoberfest #react #ai #llm #golang #go #android #deep-learning #nodejs #java #awesome #pytorch #ios #open-source

(★ 9)

Repository containing qBraid challenge for QCHack 2022

QCHack-2022 Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# QCHack 2022
Please clone this repository into your qBraid Lab environment to get the most out of the challenges! If you need help please just reach out to a mentor.

[<img src="https://qbraid-static.s3.amazonaws.com/logos/Launch_on_qBraid_white.png" width="150">](https://account.qbraid.com?gitHubUrl=https://github.com/qBraid/QCHack-2022.git)

**You don't have to do all the challenges! Just pick one (OpenQasm, Grover, Quantum teleportation) or do one Amazon, IBM, Qutech, or Microsoft's challenge on qBraid to be added to the Using qBraid Challenge**

https://www.quantumcoalition.io/

April 9-10th 2022

## Technical Challenges

- [**OpenQASM Parser Challenge**](openqasm_challenge/README.md)

- [**Intro to QC Challenges**](intro_to_qc_challenges)

## Using qBraid Challenge
If you use our platform to solve the Amazon, IBM, Qutech, or Microsoft challenge you'll be automatically added to the "Using qBraid Challenge" :)

## Submission
- Fork (or duplicate) this repository to your qBraid account.
- Work on qBraid Lab to solve a challenge!
- Exactly one member of your team should submit the URL/link to your team’s forked repository via the QCHack 2022 Challenge Submission Form (https://forms.gle/rBW6tDC8hjW35Lc76). 
- We'll merge your fork to showcase your work on the qBraid GitHub! wooo!

## qBraid/Braket Tutorials

Here, we provide useful tutorials on how to use qBraid-Lab, along with tutorials
on quantum computing, using Amazon Braket. The ladder were provided by the
[amazon-braket-examples](https://github.com/aws/amazon-braket-examples) github
repository.

The repository is structured as follows:

- [Setting up Braket environment](#qbraid)
- [Getting Started: Simple circuits and algorithms](#simple)
- [Advanced circuits and algorithms](#advanced)
- [Hybrid quantum algorithms](#hybrid)
- [Quantum machine learning and optimization with PennyLane](#pennylane)
- [Quantum annealing with D-Wave](#annealing)
- [Amazon Braket features](#braket)
- [Amazon Braket Hybrid Jobs](#jobs)

---

## <a name="qbraid">Setting up Braket environment in qBraid</a>

- [**Install Braket in qBraid-Lab**](qbraid_braket_setup/Install_Braket.ipynb)

- [**Enable Braket QPU access through qBraid-CLI**](qbraid_braket_setup/Enable_account.ipynb)
- [**Braket setup on qBraid Youtube video**](https://youtu.be/vXpS29HrdgY)

---

## <a name="simple">Simple circuits and algorithms</a>

- [**Getting started**](amazon_braket_examples/getting_started/0_Getting_started.ipynb)

A hello-world tutorial that shows you how to build a simple circuit and run it
  on a local simulator.

- [**Running quantum circuits on simulators**](amazon_braket_examples/getting_started/1_Running_quantum_circuits_on_simulators.ipynb)

This tutorial prepares a paradigmatic example for a multi-qubit entangled
  state, the so-called GHZ state (named after the three physicists Greenberger,
  Horne, and Zeilinger). The GHZ state is extremely non-classical, and therefore
  very sensitive to decoherence. For this reason, it is often used as a
  performance benchmark for today's hardware. Moreover, in many quantum
  information protocols it is used as a resource for quantum error correction,
  quantum communication, and quantum metrology.

- [**Running quantum circuits on QPU devices**](amazon_braket_examples/getting_started/2_Running_quantum_circuits_on_QPU_devices.ipynb)

This tutorial prepares a maximally-entangled Bell state between two qubits,
  for classical simulators and for QPUs. For classical devices, we can run the
  circuit on a local simulator or a cloud-based managed simulator. For the
  quantum devices, we run the circuit on the superconducting machine from
  Rigetti, and on the ion-trap machine provided by IonQ. As shown, one can swap
  between different devices seamlessly, without any modifications to the circuit
  definition, by re-defining the device object. We also show how to recover
  results using the unique Amazon resource identifier (ARN) associated with
  every task. This tool is useful if you must deal with potential delays, which
  can occur if your quantum task sits in the queue awaiting execution.

- [**Deep Dive into the anatomy of quantum circuits**](amazon_braket_examples/getting_started/3_Deep_dive_into_the_anatomy_of_quantum_circuits.ipynb)

This tutorial discusses in detail the anatomy of quantum circuits in the
  Amazon Braket SDK. Specifically, you'll learn how to build (parameterized)
  circuits and display them graphically, and how to append circuits to each
  other. We discuss the associated circuit depth and circuit size. Finally we
  show how to execute the circuit on a device of our choice (defining a quantum
  task). We then learn how to track, log, recover, or cancel such a _quantum
  task_ efficiently.

- [**Superdense coding**](amazon_braket_examples/getting_started/4_Superdense_coding.ipynb)

This tutorial constructs an implementation of the _superdense coding_
  protocol, by means of the Amazon Braket SDK. Superdense coding is a method of
  transmitting two classical bits by sending only one qubit. Starting with a
  pair of entanged qubits, the sender (_aka_ Alice) applies a certain quantum
  gate to their qubit and sends the result to the receiver (_aka_ Bob), who is
  then able to decode the full two-bit message.

---

## <a name="advanced">Advanced circuits and algorithms</a>

- [**Grover**](amazon_braket_examples/advanced_circuits_algorithms/Grover/Grover.ipynb)

This tutorial provides a step-by-step walkthrough explaining Grover's quantum
  algorithm. We show how to build the corresponding quantum circuit with simple
  modular building blocks, by means of the Amazon Braket SDK. Specifically, we
  demonstrate how to build custom gates that are not part of the basic gate set
  provided by the SDK. A custom gate can used as a core quantum gate by
  registering it as a subroutine.

- [**QFT**](amazon_braket_examples/advanced_circuits_algorithms/QFT/QFT.ipynb)

This tutorial provides a detailed implementation of the Quantum Fourier
  Transform (QFT) and the inverse QFT, using the Amazon Braket SDK. We provide
  two different implementations: with and without recursion. The QFT is an
  important subroutine to many quantum algorithms, most famously Shor's
  algorithm for factoring, and the quantum phase estimation (QPE) algorithm for
  estimating the eigenvalues of a unitary operator. The QFT can be performed
  efficiently on a quantum computer, using only O(n<sup>2</sup>) single-qubit
  Hadamard gates and two-qubit controlled phase shift gates, where 𝑛 is the
  number of qubits. We first review the basics of the quantum Fourier transform,
  and its relationship to the discrete (classical) Fourier transform. We then
  implement the QFT in code two ways: recursively and non-recursively. This
  notebook also showcases the Amazon Braket `circuit.subroutine` functionality,
  which allows one to define custom methods and add them to the Circuit class.

- [**QPE**](amazon_braket_examples/advanced_circuits_algorithms/QPE/QPE.ipynb)

This tutorial provides a detailed implementation of the Quantum Phase
  Estimation (QPE) algorithm, through the Amazon Braket SDK. The QPE algorithm
  is designed to estimate the eigenvalues of a unitary operator 𝑈; it is a very
  important subroutine to many quantum algorithms, most famously Shor's
  algorithm for factoring, and the HHL algorithm (named after the physicists
  Harrow, Hassidim and Lloyd) for solving linear systems of equations on a
  quantum computer. Moreover, eigenvalue problems can be found across many
  disciplines and application areas, including (for example) principal component
  analysis (PCA) as used in machine learning, or in the solution of differential
  equations as relevant across mathematics, physics, engineering and chemistry.
  We first review the basics of the QPE algorithm. We then implement the QPE
  algorithm in code using the Amazon Braket SDK, and we illustrate the
  application of the algorithm with simple examples. This notebook also
  showcases the Amazon Braket `circuit.subroutine` functionality, which allows
  you to use custom-built gates as if they were any other built-in gates. This
  tutorial is set up to run on the local simulator or the cloud-based managed
  simulator. Changing between these devices requires changing only one line of
  code, as demonstrated below in cell.

- [**QAA**](amazon_braket_examples/advanced_circuits_algorithms/QAA/QAA_tutorial.ipynb)

This tutorial provides a detailed discussion and implementation of the Quantum
  Amplitude Amplification (QAA) algorithm, using the Amazon Braket SDK. QAA is a
  routine in quantum computing which generalizes the idea behind Grover's famous
  search algorithm, with applications across many quantum algorithms. In short,
  QAA uses an iterative approach to systematically increase the probability of
  finding one or multiple target states in a given search space. In a quantum
  computer, QAA can be used to obtain a quadratic speedup over several classical
  algorithms.

---

## <a name="hybrid">Hybrid quantum algorithms</a>

- [**QAOA**](amazon_braket_examples/hybrid_quantum_algorithms/QAOA/QAOA_braket.ipynb)

This tutorial shows how to (approximately) solve binary combinatorial
  optimization problems, using the Quantum Approximate Optimization Algorithm
  (QAOA). The QAOA algorithm belongs to the class of _hybrid quantum algorithms_
  (leveraging classical and quantum computers), which are widely believed to be
  the working horse for the current NISQ (noisy intermediate-scale quantum) era.
  In this NISQ era, QAOA is also an emerging approach for benchmarking quantum
  devices. It is a prime candidate for demonstrating a practical quantum
  speed-up on near-term NISQ device. To validate our approach, we benchmark our
  results with exact results as obtained from classical QUBO solvers.

- [**VQE Transverse Ising**](amazon_braket_examples/hybrid_quantum_algorithms/VQE_Transverse_Ising/VQE_Transverse_Ising_Model.ipynb)

This tutorial shows how to solve for the ground state of the Transverse Ising
  Model, which is arguably one of the most prominent, canonical quantum spin
  systems, using the variational quantum eigenvalue solver (VQE). The VQE
  algorithm belongs to the class of _hybrid quantum algorithms_ (leveraging
  classical andquantum computers), which are widely believed to be the working
  horse for the current NISQ (noisy intermediate-scale quantum) era. To validate
  our approach, we benchmark our results with exact results as obtained from a
  Jordan-Wigner transformation.

---

## <a name="pennylane">Quantum machine learning and optimization with PennyLane</a>

- [**Combining PennyLane with Amazon Braket**](amazon_braket_examples/pennylane/0_Getting_started.ipynb)

This tutorial shows you how to construct circuits and evaluate their gradients
  in PennyLane with execution performed using Amazon Braket.

- [**Computing gradients in parallel with PennyLane-Braket**](amazon_braket_examples/pennylane/1_Parallelized_optimization_of_quantum_circuits.ipynb)

In this tutorial, we explore how to speed up training of quantum circuits by
  using parallel execution on Amazon Braket. We begin by discussing why quantum
  circuit training involving gradients requires multiple device executions and
  motivate how the Braket SV1 simulator can be used to overcome this. The
  tutorial benchmarks SV1 against a local simulator, showing that SV1
  outperforms the local simulator for both executions and gradient calculations.
  This illustrates how parallel capabilities can be combined between PennyLane
  and SV1.

- [**Graph optimization with QAOA**](amazon_braket_examples/pennylane/2_Graph_optimization_with_QAOA.ipynb)

In this tutorial we dig deeper into how quantum circuit training can be
  applied to a problem of practical relevance in graph optimization. We show how
  easy it is to train a QAOA circuit in PennyLane to solve the maximum clique
  problem on a simple example graph. The tutorial then extends to a more
  difficult 20-node graph and uses the parallel capabilities of the Amazon
  Braket SV1 simulator to speed up gradient calculations and hence train the
  quantum circuit faster, using around 1-2 minutes per iteration.

- [**Quantum chemistry with VQE**](amazon_braket_examples/pennylane/3_Quantum_chemistry_with_VQE.ipynb)

In this tutorial, we see how PennyLane and Amazon Braket can be combined to
  solve an important problem in quantum chemistry. The ground state energy of
  molecular hydrogen is calculated by optimizing a VQE circuit using the local
  Braket simulator. This tutorial highlights how qubit-wise commuting
  observables can be measured together in PennyLane and Braket, making
  optimization more efficient.

---

## <a name="annealing">Quantum annealing with D-Wave</a>

- [**Anatomy of annealing with Ocean**](amazon_braket_examples/quantum_annealing/Dwave_Anatomy.ipynb)

This tutorial notebook dives deep into the anatomy of quantum annealing with
  D-Wave on Amazon Braket. First, we introduce the concept of quantum annealing,
  as used by D-Wave. We apply annealing to an optimization problem, to find the
  (approximate) optimum probabilistically. We then discuss the underlying
  structures of D-Wave QPUs, including the Chimera graph for the 2000Q system
  and the Pegasus graph for the Advantage system. We explain the problem of
  finding an embedding of the original problem onto the sparse graph of a
  device, and discuss the distinction between logical and physical variables.
  Finally, we solve an example QUBO problem to analyze the sampling process, and
  we provide a breakdown of the QPU access time.

- [**Running large problems with QBSolv**](amazon_braket_examples/quantum_annealing/Running_large_problems_using_QBSolv.ipynb)

This tutorial demonstrates how to solve problems with sizes larger than a
  D-Wave device can support, by using a hybrid solver called QBSolv. QBSolv can
  decompose large problems into sub-problems, which are solved by the QPU and a
  classical Tabu solver, or by the classical solver alone. The results of the
  sub-problems then construct the solution to the problem.

- [**Maximum Cut**](amazon_braket_examples/quantum_annealing/Dwave_MaximumCut.ipynb)

This tutorial solves a small instance of the famous maximum cut (MaxCut)
  problem using a D-Wave device on Amazon Braket. The MaxCut problem is one of
  the most famous NP-hard problems in combinatorial optimization. Given an
  undirected graph 𝐺(𝑉,𝐸) with a vertex set 𝑉 and an edge set 𝐸, the MaxCut
  problem seeks to partition 𝑉 into two sets, such that the number of edges
  between the two sets (considered to be severed by the cut), is as large as
  possible. Applications can be found in clustering problems for marketing
  purposes, or for portfolio optimization problems in finance.

- [**Minimum Vertex**](amazon_braket_examples/quantum_annealing/Dwave_MinimumVertexCoverProblem.ipynb)

This tutorial solves a small instance of the minimum vertex problem while it
  discusses the BraketSampler and the BraketDWaveSampler. In essence, they are
  doing the same thing; however, each accepts different parameter names.
  Specifically, the BraketDWaveSampler allows users familiar with D-Wave to use
  D-Wave parameter names, such as `answer_mode`, whereas the BraketSampler
  parameter names are consistent with the rest of the Amazon Braket experience.

- [**Graph partitioning**](amazon_braket_examples/quantum_annealing/Dwave_GraphPartitioning.ipynb)

This tutorial solves a small instance of a graph partitioning problem using a
  D-Wave device on Amazon Braket. The derivation for this QUBO problem is nicely
  explained here: https://github.com/dwave-examples/graph-partitioning.

- [**Factoring**](amazon_braket_examples/quantum_annealing/Dwave_Factoring/Dwave_factoring.ipynb)

This tutorial shows how to solve a constraint satisfaction problem (CSP)
  problem, with the example of factoring, using a D-Wave device on Amazon
  Braket. Particularly, factoring is expressed as a CSP using Boolean logic
  operations, and it is converted to a binary quadratic model that can be solved
  by a D-Wave device.

- [**Structural Imbalance**](amazon_braket_examples/quantum_annealing/Dwave_StructuralImbalance/Dwave_StructuralImbalance.ipynb)

This tutorial solves a structural imbalance problem using a D-Wave device on
  Amazon Braket. Social networks map relationships between people or
  organizations onto graphs. The people and organizations are represented as as
  nodes, and relationships are represented as edges. Signed social networks can
  map friendly or hostile relationships. These networks are said to be
  structurally balanced when they can be cleanly divided into two sets, in which
  each set contains only friends, and all relations between these sets are
  hostile. The measure of structural imbalance or frustration, when it cannot be
  cleanly divided, is the minimum number of edges that violate the social rule.
  Given a social network as a graph, D-Wave devices can partition the graph into
  two colored sets, and show the frustrated edges.

- [**Traveling Salesman Problem**](amazon_braket_examples/quantum_annealing/Dwave_TravelingSalesmanProblem/Dwave_TravelingSalesmanProblem.ipynb)

This tutorial solves small instances of the famous traveling salesman problem
  (TSP) using D-Wave devices on Amazon Braket. TSP is an NP-hard problem in
  combinatorial optimization. The solution finds the shortest possible route
  that visits each city exactly once, given a list of cities and the distances
  between each pair of cities. To solve the problem, cities and distances are
  mapped to a graph with weighted edges. A solution, when found on that graph,
  is the Hamiltonian cycle that has the least weight.

---

## <a name="braket">Amazon Braket features</a>

This folder contains examples that illustrate the usage of individual features
of Amazon Braket

- [**Allocating Qubits on QPU Devices**](amazon_braket_examples/braket_features/Allocating_Qubits_on_QPU_Devices.ipynb)

This tutorial explains how you can use the Amazon Braket SDK to allocate the
  qubit selection for your circuits manually, when running on QPUs.

- [**Getting Devices and Checking Device Properties**](amazon_braket_examples/braket_features/Getting_Devices_and_Checking_Device_Properties.ipynb)

This example shows how to interact with the Amazon Braket GetDevice API to
  retrieve Amazon Braket devices (such as simulators and QPUs) programmatically,
  and how to gain access to their properties.

- [**Using the tensor network simulator TN1**](amazon_braket_examples/braket_features/Using_the_tensor_network_simulator_TN1.ipynb)

This notebook introduces the Amazon Braket managed tensor network simulator,
  TN1. You will learn about how TN1 works, how to use it, and which problems are
  best suited to run on TN1.

- [**Simulating noise on Amazon Braket**](amazon_braket_examples/braket_features/Simulating_Noise_On_Amazon_Braket.ipynb)

This notebook provides a detailed overview of noise simulation on Amazon
  Braket. You will learn how to define noise channels, apply noise to new or
  existing circuits, and run those circuits on the Amazon Braket noise
  simulators.

---

## <a name="jobs">Amazon Braket Hybrid Jobs</a>

This folder contains examples that illustrate the use of Amazon Braket Hybrid
Jobs (Braket Jobs for short).

- [**Getting started with Amazon Braket Hybrid Jobs**](amazon_braket_examples/hybrid_jobs/0_Getting_started/Getting_started.ipynb)

This notebook provides a demonstration of running a simple Braket Job. You
  will learn how to create a Braket Job using the Braket SDK or the Braket
  console, how to set the output S3 folder for a job, and how to retrieve
  results. You will also learn how to specify the Braket device to run your job
  on simulators or QPUs. Finally, you will learn how to use local mode to
  quickly debug your code.

- [**Quantum machine learning in Amazon Braket Hybrid Jobs**](amazon_braket_examples/hybrid_jobs/1_Hyperparameter_tuning/Hyperparameter_tuning.ipynb)
  This notebook shows a typical quantum machine learning workflow using Braket
  Jobs. In the process, you will learn how to upload input data, how to set up
  hyperparameters for your job, and how to retrieve and plot metrics. Finally,
  you will see how to run multiple Braket Jobs in parallel with different sets
  of hyperparameters.
- [**QAOA with Amazon Braket Hybrid Jobs and PennyLane**](amazon_braket_examples/hybrid_jobs/2_Using_PennyLane_with_Braket_Jobs/Using_PennyLane_with_Braket_Jobs.ipynb)

This notebook shows how to run the QAOA algorithm with PennyLane (similar to a
  [previous notebook](examples/pennylane/2_Graph_optimization_with_QAOA.ipynb)),
  but this time using Braket Jobs. In the process, you will learn how to select
  a container image that supports PennyLane, and how to use checkpoints to save
  and load training progress of a job.

QCHack-2022

QCHack-2022 Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

QCHack-2022

QCHack-2022 Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README