1. Team Name

Voyager

2. Project Description

This project is a further study of Saesun Kim's research on applying QML in image classification (https://github.com/bagmk/Quantum_Machine_Learning_Express) We are going to detect the animal in infrared camera and the car object in night camera by using QML and Grover algorithm. After that , we are planning to compare the accuracy of detecting objects between those 2 methods. Our goal is to apply QML and Grover algorithm, which was usually dealt only in theory, in practical fields like image segmentation and find the optimal method.

3. Source Code

https://github.com/BrightSky77/Qhack_Quantum_Machine_Learning

4. Resource Estimate:

By having access to AWS credits, we will be able to use more qubits so that we can process more big size images.

Team Name: QH

Your team's name (matching the name used on the QHack Coding Challenges, if applicable)

Project Description: Analyzing Interaction between Proteome and Genome with Quantum Computers

A brief description of your project (1-2 paragraphs).

Severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) for COVID-19, including its variants, has been wide spread globally. It affects all ages, races and various medical conditions. Using traditional computers is quite challenging to analyze interactions between proteome and genome, including gene-gene Interactions among virus and human genetics. This project is to use currently available quantum computers to analyze interactions between proteome and genome, including gene-gene interactions in SARS CoV 2 and human genetics.

Virology and genetics have been two fields that I’m very interested since my medical school. After my honorable graduation from Tongji Medical School at Huazhong University of Science and Technology where I learned Western Medicine and some Chinese Medicine, I did my Residency on Internal Medicine at Union (Xie He) Hospital. I then went to Germany and did my doctoral thesis at Munich University. After obtaining my doctoral degree with honor from Munich University, I came to University of California at Los Angeles (UCLA) for my post-doctoral fellowship and subsequently worked there. I also took and successfully passed the U.S. National Board Step-1, National Board Step-2 and Clinical Skill Assessment (CSA), and am certified by the U.S. Educational Commission for Foreign Medical Graduates (ECFMG).

In 2006, I moved from Los Angeles (UCLA) to Washington, DC for my job at the National Institutes at Health (NIH), and further strength my interests in Virology and genetics, particular at the time of current COVID-19 pandemics. My e-mail is [email protected] and my phone # is 240-453-1534. I am looking forward to hearing from you about my draft of this QHack Open Hackathon project.

Many thanks, Sincerely,

Yining Xie

Source code:

The Quantum Programming Language Twist from MIT will be used in conjunction with PenneLane (Pythone, Numby, PyTorch, TensorFlow, JAX interface, Cirq, Strawberrybields, Qiskitt, Forest, etc)

https://doi.org/10.1145/3498691

Two Attachments here: 3498691.pdf; appendices.pdf 3498691.pdf appendices.pdf

A hyperlink to the draft source code for your team's hackathon project (e.g., a GitHub repo).

Resource Estimate:

If awarded, the access to IBM Quantum machine with IBM 16-qubit QPU will be used to finish this project.

The Quantum Programming Language Twist from MIT will be used in conjunction with PenneLane (Pythone, Numby, PyTorch, TensorFlow, JAX interface, Cirq, Strawberrybields, Qiskitt, Forest, etc).

A 1-2 paragraph written Resource Estimate, indicating how you expect to use the additional AWS credits, if awarded, to finish your Open Hackathon project.

Team - alekospagon

The project is qualified for the 3 categories:

1. QAOA Challenge
2. Hybrid Algorithms Challenge
3. IBM Qiskit Challenge

Project Description:

Abstract: The Project is an application of the QAOA algorithm on a KnapSack variant. Specifically, it is for a problem reducable to KnapSack for which we implemented a quantum circuit (with many optimizations). The problem was introduced to our team on IBM's Fall Challenge 2021. We took this problem further by implementing new mixers for faster convergence proposed in Quantum Optimization Heuristics with an Application to Knapsack Problems

Technical details: We transcribe the problem to the QAOA's C operator. We construct the circuit corresponding to it; then we propose 3 optimizations that reduce the circuit's depth and then we propose an optimization (new mixers) that improves the algorithm's convergence speed. After that, we define a metric for the circuit's accuracy and we measure it for different inputs and repetitions. Last, but not least, using our metric we demonstrate the clear advantage of the new mixers. We propose further optimizations.

We uploaded the Project Pdf, and one power point to present our work. The pdf contains all the details and the source code for the Qiskit Simulation.

QHACK___QAOA_KnapSack_Variants_with_Optimized_Mixers.pdf QHACK___QAOA_KnapSack_Variants_with_Optimized_Mixers___Power_Point___Non_Technincal.pdf

Team Name:

Your team's name (matching the name used on the QHack Coding Challenges, if applicable) Quan

Project Description:

A brief description of your project (1-2 paragraphs). Developing a quantum convolutional network with a skip connection network block.

Source code:

A hyperlink to the draft source code for your team's hackathon project (e.g., a GitHub repo).

Resource Estimate:

A 1-2 paragraph written Resource Estimate, indicating how you expect to use the additional AWS credits, if awarded, to finish your Open Hackathon project. Mostly used in running Amazon Braket.

Team Quest:
Quantum Monte Carlo for Pricing Financial Derivatives

Estimating how to price Financial Derivatives - like options such as puts and calls - is a difficult task due to the huge number of possible changes in variables. While estimation techniques such as Classical Monte Carlo exist, they can easily rack up large 'error' or uncertainty; getting rid of this is time-consuming and costly.

In the case of the Classical Monte Carlo, for example, error scales with 1/sqrt(M) where M is the number of simulations. Because of this, in order to halve the error, you must quadruple the simulation number. To reduce the error to useful amounts, the quadratic scaling can mean large numbers of simulations are needed. In Quantum Monte Carlo, however, we can offer a Quadratic Speedup, so error scales with 1/M. This has huge potential, since it can greatly improve the accuracy of Option Pricing while reducing the intensity of simulation required for them.

Team Quest hopes to explore this by implementing work in Quantum computational finance: Monte Carlo pricing of financial derivatives, seeing how Quantum Monte Carlo can be realised and executed.

Github Repository

Challenges:

• IBM Challenge
• Quantum Finance Challenge
• Google Quantum AI Challenge
• Quantum Entrepreneur Challenge
• Young Scientist Challenge

Members: @StreakSharn, @DSamuel1, @r-agni

Team Name:

Qanything

Project Description:

The optimisation of molecular geometry is one of many fundamental problems in quantum chemistry. The motivation of this problem stem from the experimental observation that an optimal molecular geometry, which are found via numerical calculations, often correspond to actual molecular structure found in Nature. There is motivation in studying optimal geometry of molecules as sometimes, the unique properties of the substance are attributed to its special molecular structure. For example, the V shaped structure of water explains ice formation and open structure of ice crystals with lower density than liquid water.

In this project, we shall investigate performance of Problem-Inspired Ansatze in solving the optimisation problem of molecular geometry, which are built using Given rotations [1] as simple building blocks. Given rotations are particle-preserving variational circuits for which are useful for approximating molecular ground states. In particular, problem-inspired ansatze will be constructed based on the Exact Decomposition of Unitary Coupled Cluster Single and Double (UCCSD) Unitary [2] which are known to be notoriously difficult to implement in practice with the current quantum devices due to its need for deep circuits. Importantly, we plan to employ creative optimization strategies on the both simulated and real hardware for simple molecules, such as H2 and others up to LiH . We also wish to study how the noise can affect the final accuracy on the molecular geometry.

Source code:

Qanything_Chem_Project

Resource Estimate:

The smallest test case, Hydrogen (H2) molecule will require a quantum circuit at least 4 qubits with 15 independent pauli strings observables. The largest test case, Lithium Hydride (LiH) molecule will require at least 12 qubits with 631 independent pauli strings observables.

Challenge Attempting

• Quantum Chemistry Challenge
• Simulation Challenge
• Hybrid Algorithms Challenge
• IBM Qiskit Challenge

References:

[1] Arrazola, J. M., Matteo, O. D., Quesada, N., Jahangiri, S., Delgado, A., & Killoran, N. (2021). Universal quantum circuits for quantum chemistry.

[2] Evangelista, F. A., Chan, G. K.-L., & Scuseria, G. E. (2019). Exact parameterization of fermionic wave functions via unitary coupled cluster theory. The Journal of Chemical Physics, 151(24), 244112.

Team Name:

CloudKite

Project Description:

In current NISQ devices, circuit noise hinders us from obtaining a satisfying outcome. Even for those quantum algorithms specifically designed for NISQ devices like variational quantum algorithms, noise could induce barren plateaus and affect the optimization performance[1].

A recent article proposed the idea for error mitigation using a noise-estimation circuit[2]: Before running the working circuit, an estimation circuit based on the working circuit will be executed and used to measure the noise scale. The author has shown that this idea could improve performance when simulating the Heisenberg model, and the improvement is even larger when circuit depth becomes larger. We are going to replicate the result using real noisy backends and apply the idea to more use cases like variational algorithms and QAOA.

[1] Wang, S., Fontana, E., Cerezo, M. et al. Noise-induced barren plateaus in variational quantum algorithms. Nat Commun 12, 6961 (2021). [2] Urbanek, Miroslav, et al. "Mitigating depolarizing noise on quantum computers with noise-estimation circuits." Physical Review Letters 127.27 (2021): 270502.

Source code:

Here

Resource Estimate:

The 16-qubit QPU could provide the chance to explore the idea's gain for quantum circuits with a large size, which allows us to verify the dominant range in terms of system(qubit) size for this error mitigation protocol. After we implemented the six-qubit example shown in ref [2], we will increase the system size step by step to a 16-qubit example. The number of total shots could be estimated by 16(steps)*3(one without error mitigation, two with error mitigation)*8192(shots) for each simulation.

The access of the 16-qubit device could also give the possibility for us to try more use cases. In general, variational algorithms with noise-estimation circuits would need twice the amount of original shots. In the optimization process, we estimate ~200 iteration to achieve significant performance improvement.

Team Name:

BladeRunner

Project Description:

A brief description of your project (1-2 paragraphs). We are deciding to do a Quantum Computational Human Neural Network that mimics the human brain as given from BCI data. We will be using GAN's, QNN's, Autoencoders and LSTM's. We know that even in the biology field we still don't know everything about the human brain, but the synapses and connectivity of the parts such as the frontal, temporal, occipital and parietal lobes are key to basic functionality of life. We will be using Quantum Optical Neural Networks to achieve this, in this way we can use both pennylane and strawberry fields.

Source code:

A hyperlink to the draft source code for your team's hackathon project (e.g., a GitHub repo). https://github.com/hrahman12/QHack-2022-Open-Project-

Resource Estimate:

A 1-2 paragraph written Resource Estimate, indicating how you expect to use the IBM 16-qubit QPU, if awarded, to finish your Open Hackathon project. We will use it to benchmark cases for the Neural Network against other devices(both IBM and Amazon).

QHack 2022

Team

Hey there! I am José Ignacio Espinoza Camacho, a Master's student doing my research in quantum computing. I am taking part of the Coding Challenge under the team name JIEC.

Description

Clustering is a set of mathematical and computational methods that are part of the unclassified learning techniques in Machine Learning. Clustering is frequently used to generate initial information from data sets about which little is known [1]. There are several families of algorithms. This work focuses on Spectral Clustering, specifically in Normalized Cuts [2]. In 2000, J. Shi and J. Malik designed the Normalized Cuts algorithm for image segmentation based on previous spectral clustering works. This algorithm holds an important characteristic: we can retrieve different segments of an image by using not only the second eigenvector (like usual spectral clustering algorithms), but a small set of eigenvectors.

This work lies amidst quantum machine learning and quantum image processing. Quantum Machine Learning (QML) is one of the fastest growing areas in quantum computing. In contrast with classical machine learning, QML finds atypical patterns more efficiently [3]. Quantum Image Processing is a relatively new area in quantum computing. This field focuses on storing, processing, and retrieving visual information (i.e. images and video) using quantum systems [4]. Based on the work of L. Tse, et al. [5], I pretend to explain and implement the QAOA algorithm they propose using Qiskit.

External Links

Here you will find the link to the work made by L. Tse, et al [5].

• https://arxiv.org/abs/1812.03050

In this link you will find an explanatory jupyter notebook of my project Graph Cut Segmentation via QAOQ implemented with Qiskit

• https://colab.research.google.com/drive/13blIGHwYVCr4Oh7jf3d69XCRwXbtGWO9?usp=sharing

Finally, in the following link you will find the final source code of my project

• https://colab.research.google.com/drive/1Ui-NDEs02IQHRm6mFcBcuZNivm5hGAy5?usp=sharing

Open Hackathon Challenges

Given the nature of the work [5], the Challenges I would like to apply are:

1. Access to 16-qubit IBM Quantum machine. The Dataset uses images of 4x4 pixels, each pixel is represented by 1 qubit. Hence, the algorithm would fit perfectly in the 16-qubit IBM Quantum machine.

2. IBM Qiskit Challenge, Sponsored by IBM Quantum and Université de Sherbrooke

3. Hybrid Algorithms Challenge, Sponsored by AQT. QAOA algorithms are Variational Algorithms, hence, they are also hybrid - quantum algorithms.

4. QAOA Challenge, sponsored by Entropica

References

[1] A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: A review. ACM Comput. Surv., 31(3):264–323, September 1999.

[2] Jianbo Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888–905, 2000.

[3] Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, NathanWiebe, and Seth Lloyd. Quantum machine learning.Nature, 549(7671):195–202,Sep 2017.

[4] Fei Yan and Salvador E. Venegas-Andraca.Quantum Image Processing. SpringerNature Singapore Pte Ltd., 2020.

[5] Lisa Tse, Peter Mountney, Paul Klein, and Simone Severini. Graph cut segmen-tation methods revisited with a quantum algorithm.CoRR, https://arxiv.org/abs/1812.03050.

Team Name:

anonymousr007

Project Description:

This project aims to use a truncated (regularized) Hamiltonian for the matrix quantum mechanics models. This Hamiltonian is constructed by considering a truncated Hilbert space in the Fock basis. The truncated Hilbert space is constructed starting from the individual matrix degrees of freedom.

Two types of matrix quantum mechanics models are used

• A Yang-Mills-type bosonic 2-matrix model with SU(2) gauge group, which has 6 bosonic degrees of freedom in total.
• A Supersymmetric 2-matrix model with SU(2) gauge group which corresponds to with the minimal number of degrees of freedom 6 bosons and 3 fermions.

Quantum mechanics with matrix degrees of freedom plays an important role in gauge-gravity duality. Gauge-gravity duality translates difficult problems in quantum gravity to well-defined problems in non-gravitational quantum theories. Although it originated from string–M-theory, connections to various other fields, including

• Quantum Information Theory
• Condensed Matter Theory
• Cosmology
• Holographic simulation of Quantum Black Holes
• Complex high-dimensional supergravity theories

We use the Variational Quantum EigenSolver (VQE) to estimate the low-energy spectrum As for the VQE, the specific architecture that we use does not show a satisfactory performance at strong coupling, perhaps due to the variational forms parametrized by the quantum circuits not adequately probing the full gauge-invariant Hilbert space. This result shows that going beyond the VQE and using more complicated or fully quantum algorithms is not the correct way to approach matrix quantum mechanics for now, because they would require even deeper quantum circuits that are more prone to noise on actual quantum hardware.

Source code:

QHack 2022 Open Hackathon Project

Resource Estimate:

• Higher AR memory and joint data prediction across countries/states require more qubits.
• 16-qubit QPU would be a good size for Matrix Model Simulation

Challenges:

• Simulation Challenge
• Young Scientist Challenge
• IBM Qiskit Challenge
• Quantum Chemistry Challenge
• Science Challenge

Team Member: @anonymousr007

There is a typo in the docstring, the example operators do not commute. Change:

def join_operators(op1, op2):
    """This function will receive two operators that we know can be simplified
    and returns the operator corresponding to the union of the two previous ones.

    Args:
        - op1 (list(str)): First Pauli word (list of Pauli operators), e.g., ["Y", "I", "Z", "I"].
        - op2 (list(str)): Second Pauli word (list of Pauli operators), e.g., ["Y", "I", "X", "I"].

    Returns:
        - (list(str)): Pauli operator corresponding to the union of op1 and op2.
        For the case above the output would be ["Y", "X", "Z", "I"]
    """

to

def join_operators(op1, op2):
    """This function will receive two operators that we know can be simplified
    and returns the operator corresponding to the union of the two previous ones.

    Args:
        - op1 (list(str)): First Pauli word (list of Pauli operators), e.g., ["Y", "I", "Z", "I"].
        - op2 (list(str)): Second Pauli word (list of Pauli operators), e.g., ["Y", "X", "I", "I"].

    Returns:
        - (list(str)): Pauli operator corresponding to the union of op1 and op2.
        For the case above the output would be ["Y", "X", "Z", "I"]
    """

Team Name:

Infinite QDev

Project Description:

According to the quantum no-cloning theorem, an unknown quantum state cannot be cloned perfectly due to the postulate that unitary transformations are linear. Hence our project aims to create a universal cloning machine that can produce a copy close to the original state. We are hence, replicating the paper from Yang et. al, 2019.

This circuit using Qiskit python library will also confirm that entanglement is input state independent and can demonstrate universal cloning of an individual qubit with a circuit QED setup.

Presentation:

https://docs.google.com/presentation/d/1FS-QCJpSR2QN_DUuyJU8IgC5NywpCZs_EKC1zOuPjIE/edit?usp=sharing

Source code:

GitHub - QiyangGeng/QHackOH

Which challenges/prizes would you like to submit your project for?

Team Members: Ethan Rajkumar William Gervasio Qiyang Geng Brett Capistrano Mauricio Sorocco

Challenges IBM Qiskit Challenge Hybrid Algorithms Challenge Science Challenge Simulation Challenge

AnotherQubitBitesTheDust - QHack22

Project: QGenes

Description of the project

Developments in microarray technologies have revolutionized life sciences by giving us the capability to simultaneously measure thousands of gene expression values. This presents us with the golden opportunity to use quantum computers to process these vast amount of information to train machine learning models, which we can use to accurately predict the possibility of developing genetic disorders from gene expressions of particular individuals.

Data source: Kaggle - Bruno Grisci

Source code:

GitHub Repo

List of Open Hackathon challenges:

• Bio-QML Challenge
• Quantum entrepeneur challenge

Team Name: qmlBuddies

Your team's name (if your team took part in the QHack Coding Challenge, the name here should match)

Project Description:

How information flows in a many-body system is a problem of physical interest. One way to model the information flow is to perturb the many-body system and study the time evolution of the physical observables. This problem of "quantum quenches" has been studied in many papers. In this project, we study this problem using a quantum simulator. We focus on the XXZ model and investigate the time evolution of a two-point function following a quantum quench. We consider two protocols of quantum quench. In the first protocol, we perturb the ground state of the XXZ model in the gapless phase. In the second protocol, we deform the Hamiltonian of the XXZ model by changing the value of the parameter $\Delta$.

Presentation:

A hyperlink to an explanatory presentation of your team’s hackathon project in a non-technical form (e.g., video, blog post, jupyter notebook, website, slideshow, etc.).

Source code:

(https://github.com/ArunM69/Hackathons-)

Description: The notebooks are different trials we ran with different delta values and perturbation types. The main presentation is included in the file named 'XXZ_10_correlation_state_perturbation - 0.1 (1)'

Which challenges/prizes would you like to submit your project for?

1. Simulation Challenge
2. Hybrid Algorithms Challenge
3. Science Challenge

Team Name:

The Superpositioned States of America

Project Description:

Our work focuses on Quantum Graph Neural Networks (QGNNs), to solve the particle tracking reconstruction challenge. Specifically, we are looking to focus on the detailed analysis of the vanishing gradient problem, long training times, and how robust the overall approach is to noise from real quantum computers, which have been mentioned but not addressed yet in prior work. Our work aims to improve the viability of the QGNN method for particle tracking problems.

Presentation:

CERN_Project_Report.pdf

Source code:

https://github.com/amirebrahimi/QHack-2022-Hackathon https://github.com/amirebrahimi/qtrkx-gnn-tracking/

Which challenges/prizes would you like to submit your project for?

CERN AQT Google Quantum Simulation Challenge Young Scientist (we have an undergrad from IQT)

Team Name:

David Quiroga

Project Description:

Variational quantum algorithms have enabled the use of quantum devices in the noisy intermediate-scale quantum (NISQ) era for a wide variety of use cases, showing great flexibility in the types of problems that can be solved. From finance to chemistry and machine learning, variational quantum algorithms make it possible to overcome the noise present in quantum computers to obtain useful results. In general, problems that can be formulated as a quadratic unconstrained binary optimization (QUBO) problem benefit from these algorithms.

The main goal of this project is to explore the problem of finding the excited state energy of the H2 molecule using Qamuy, a quantum chemistry software that provides solutions for chemistry problems, and to apply a benchmark to find the best combination of ansatzes and optimizers for the fastest solution. For this, we use the variational quantum deflation (VQD) solver as the solver which enables finding both the ground state and the excited state energy of a Hamiltonian, contrary to the variational quantum eigensolver that can only find the ground state energy.

Presentation:

https://github.com/Raijeku/qhack2022

Source code:

https://github.com/Raijeku/qhack2022

Which challenges/prizes would you like to submit your project for?

Quantum Chemistry Challenge

Team Name:

Qillers

Project Description:

In the stock market, every investor wants to maximize their profit while also reducing the risk. For this purpose, every investor wishes to optimize their portfolio using the latest optimization techniques. One of such techniques is by running Variational Quantum EigenSolver using Quantum Computers. The goal of portfolio optimization is to minimize risks (financial loss) and maximize returns (financial gain). But this process is not as simple as it may seem. Gaining high returns with little risk is indeed too good to be true. Risks and returns usually have a trade-off relationship which makes optimizing your portfolio a little more complicated. Portfolio optimization can be mathematically formulated as a combinatorial optimization problem subject to certain constraints. There exist a few classical methods (e.g. Minimum Eigen solver) to solve this type of problem. But these methods usually have exponential time complexity and suffer from the computational bottleneck when the problem size is very large. Unfortunately, portfolio optimization problems in the real world usually involve a large number of assets (e.g. 1000 assets) and we have to select an optimal combination of a certain number of assets from them. It would take a very long time (e.g. a few weeks, a few months) to solve these problems, which is impractical in real-world business.

The goal of this project is to find the efficient frontier for an inherent risk using a quantum approach. We will use Qiskit's Finance application modules to convert our portfolio optimization problem into a quadratic program so we can then use variational quantum algorithms such as VQE to solve our optimization problem.

Presentation:

https://docs.google.com/presentation/d/1bdgKT_xmzTUT3jJ2MJX8VxVDnRFxaMWLdoKMBSEqMjo/edit?usp=sharing

Business Pitch Deck(For Quantum Entrepreneur Challenge)

https://docs.google.com/presentation/d/1sP9jO8zEbPEl4PA4LeVt96eey_8Pug1qM-h-onz13ro/edit?usp=sharing

Source code:

https://github.com/Siddharthgolecha/Qillers

Which challenges/prizes would you like to submit your project for?

IBM Qiskit Challenge Hybrid Algorithms Challenge Quantum Entrepreneur Challenge Quantum Finance Challenge Young Scientist Challenge