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https://github.com/qbraid/qchack-2022

Repository containing qBraid challenge for QCHack 2022
https://github.com/qbraid/qchack-2022

quantum quantum-computing

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Repository containing qBraid challenge for QCHack 2022

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README 

        
# 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.



[](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)



---



## Setting up Braket environment in qBraid



- [**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)



---



## Simple circuits and algorithms



- [**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.



---



## Advanced circuits and algorithms



- [**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(n2) 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 π‘ˆ; 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.



---



## Hybrid quantum algorithms



- [**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.



---



## Quantum machine learning and optimization with PennyLane



- [**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.



---



## Quantum annealing with D-Wave



- [**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.



---



## Amazon Braket features



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.



---



## Amazon Braket Hybrid Jobs



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.