{"schema":"BUIDL","d":{"id":11294,"members":[{"hackerId":134138,"projectMemberId":9334,"avatar":"https://cdn.dorahacks.io/static/files/18edd56226740276f898487440c8a51c.png","name":"tsr76","handle":"U_9c346518fe8e55","email":"***@***","status":"accepted","role":2,"title":null,"introduction":null},{"hackerId":134001,"projectMemberId":9333,"avatar":"https://cdn.dorahacks.io/static/files/18ed853b2c9d7bee31ac7fd431895ea9.png","name":"eileenr09","handle":"U_d70dc716bdce56","email":"***@***","status":"accepted","role":2,"title":null,"introduction":null},{"hackerId":null,"projectMemberId":9332,"avatar":null,"name":null,"handle":null,"email":"***@***","status":"pending","role":2,"title":null,"introduction":null},{"hackerId":null,"projectMemberId":9331,"avatar":null,"name":null,"handle":null,"email":"***@***","status":"pending","role":2,"title":null,"introduction":null}],"pictures":["https://cdn.dorahacks.io/static/files/18edd4af38ba6dc4b5eca954646b7ab1.jpg"],"roundProjects":[],"hackathonProjects":[{"id":11940,"hackathon":{"id":374,"tabSet":[{"id":640,"name":"Submission Guide","slug":"submission-guide","description":"# Getting Started [3-5 MIN]\n\n**1.** **You should submit your BUIDL via [DoraHacks.io](https://dorahacks.io/). Before that, make sure that you already have an account.**\n\nUse the **Sign in/up button** at the upper right corner of the page to access the sign-up options.\n\n

\n\nFor BUIDLers, we recommend you to connect the account to your **GitHub** to make more people know what you are building at any time.\n\n*You may be asked to receive a verification code via email. The email may take some time to arrive. No worries! The code will not expire in 10 minutes when you click “Send code”.\n\n**2. Before the submission of deadline, please make sure you have added the GitHub link to your project. You can submit the project first, and edit it later before the deadline.**\n\nClick “ Register As Hacker”-“Submit BUIDL”.\n\n

\n\n> **→ If you want to submit a new BUIDL project**\n\nClick **“Create a new BUIDL”** and fill in the form as required. You'll be directed to the \"Apply for YQuantum 2024\" page after the new BUIDL setup.\n\nFill in the form to offer the information about your project. Note that for demo, demo link and social media, please offer **URLs (with http/https://)**.\n\n

\n\nDon't forget to check \"I agree to the User Agreement\".\n\n

\n\nBack to Application window. Choose your BUIDL project from **\"Apply with Existing BUIDL\"** and identify the track you want to join.\n\nWhen submission succeeds, you'll see this notification.\n\n

\n\n# Project Verification\n\nAfter verification by the hackathon organizer through the manager portal, your project will be displayed in the [BUIDLs gallery](https://dorahacks.io/hackathon/yquantum2024/buidl).\n\n# Want to make some changes to your BUIDL?\n\nClick [Avatar] -> [Profile] to enter the Account Center, and click the \"Edit\" button on the top right of the BUIDL to update the BUIDL information.\n\n

\n\n
\n\n

"},{"id":647,"name":"IBM Quantum Challenge Track","slug":"ibm-quantum-challenge-track","description":"# YQuantum Hackathon - Dynamic Circuit Challenge: Creatively Maximizing Performance and Tayloring Noise\n\n# Background and motivation\n\nDynamic circuits are an exciting feature of IBM Quantum hardware that incorporates quantum circuits with real-time classical communication. Different from the static counterpart, dynamic circuits can not only implement a set of basic quantum operations like the Hardmard gate, CNOT gate, or qubit reset but also can implement measurement in the middle of a circuit, store the measurement results to classical bits, evaluate classical expressions on the fly, and determine what quantum operation to do next.\n\nThis capability has a variety of applications, for example quantum error correction, quantum simulation, and so on. We encourage you to explore what you can do with dynamic circuits in this challenge! Bonus point if you show improvement of your dynamic circuit implementation over its static counterpart - for example, use dynamic circuit feature to shorten the circuit depth, suppress error rate, and so on! After you create a dynamic circuit of your interest and are ready to run it on the hardware, bear in mind that the current quantum computers are noisy! In practical implementation, the operations in a dynamic circuit can be faulty, so it is important to quantify their fidelities and model the noise mechanism on their own or in the context of a circuit.\n\nIn the case of dynamic circuits, the types of error and how error propagates will be more complicated due to the hybrid quantum and classical nature of noise. We challenge you to characterize the noise in dynamic circuits. Fortunately, there is a suite of noise characterization and verification tools like tomography and randomized benchmarking, which are fairly well understood on static circuits, and extending them to dynamic circuits is an interesting research question to take on.\n\n# Challenge ideas\n\n* Find an application using dynamic circuits For example: prepare a large GHZ state, generate long-range entanglement, prepare a repetition code, do quantum phase estimation.\n\n* Demonstrate the improvement of implementation using the dynamic circuit feature. For example: use dynamic circuit feature to shorten circuit depth or suppress error rate in a real circuit execution.\n\n* Characterize noise and errors in dynamic circuits For example, study how error propagates in dynamic circuits: use a benchmark method to study the fidelities of individual operations and composite operations in dynamic circuits.\n\n* With deeper understanding of how noise affects dynamic circuits, could you think of ways to suppress noise?\n\n# Judging criteria\n\nDynamic circuits are a new Qiskit feature and a cutting edge capability in the quantum world. As such, we will value your implementation effort, but also your ability to show you understood the concept, its applications, and its power. You are encouraged to put effort into showing your end result: prepare a clear presentation, contextualize dynamic circuits, and put emphasis on comparing dynamic circuits against \"vanilla\" quantum circuits. Remember: the end goal here is to make you learn useful skills and concepts that will help you along your journey into quantum information technology!\n\n# Getting started resources\n\n* What are dynamic circuits? Find out from the IBM’s blog post [here](https://www.ibm.com/quantum/blog/quantum-dynamic-circuits).\n\n* Documentation on dynamic circuits [here](https://docs.quantum.ibm.com/build/classical-feedforward-and-control-flow)\n\n* For testing and debugging the quantum circuits locally, the [qiskit_aer](https://github.com/Qiskit/qiskit-aer) simulators can be useful\n\nFurther reading about dynamic circuits:\n\n - [Exploiting Dynamic Quantum Circuits in a Quantum Algorithm with Superconducting Qubits](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.127.100501)\n - [Efficient Long-Range Entanglement using Dynamics Circuits](https://arxiv.org/pdf/2308.13065.pdf)\n - [A randomized benchmarking suite for mid-circuit measurements](https://iopscience.iop.org/article/10.1088/1367-2630/ad0e19)"},{"id":648,"name":"Cap/Hartford/Quantinuum Challenge Track","slug":"cap-hartford-quantinuum-challenge-track","description":"# YQuantum Hackathon - State preparation for quantum computing Monte Carlo\nBy The Hartford and Capgemini, with Platform Sponsor Quantinuum\n\n# Introduction\n\nQuantum computing, with its extraordinary promise of computational speedups, stands poised to revolutionize various industries. Among these, the insurance sector is particularly ripe for transformation. Enter the technique known as **Quantum Computing Monte Carlo** (QCMC), a quantum counterpart to the classical “Monte Carlo” method. By harnessing the unique properties of quantum systems, QCMC holds the potential to drive significant improvements in insurance processes.\n\n# Monte Carlo for insurance\n\nBefore we investigate QCMC, let’s look at Monte Carlo simulations on classical computers. Monte Carlo methods involve **random sampling** and **deterministic computation**. Here’s a concise breakdown:\n\n1. **Random Sampling**: We generate random inputs (often following specific probability distributions) for a system’s parameters. These inputs represent different scenarios.\n2. **Deterministic Computation**: For each set of sampled inputs, we perform a deterministic computation. The result provides an estimate for the desired quantity.\n\nThe big challenge is the fact that to get a good aggregation, we need many iterations. Specifically, to achieve an aggregation with an error , we need ) iterations.\n\n## Applications in insurance\n\n**Monte Carlo methods** find a special place in the insurance industry. Here are some applications of Monte Carlo methods in the insurance industry:\n\n- **Risk Assessment:** Simulating scenarios helps insurers understand potential outcomes.\n- **Premium Calculations:** Monte Carlo aids in estimating insurance premiums.\n- **Reserve Estimation:** Insurers use it to estimate reserves for future claims.\n- **Stress Testing:** Assessing financial stability through extreme scenarios.\n\nDuring this hackathon, you will focus on reserve estimation.\n\n## Conditional Value-at-Risk\n\nConditional Value-at-Risk (CVaR), also known as Expected Shortfall (ES), is a risk measure that quantifies the expected loss of an investment portfolio over a specific time under extreme market conditions. Unlike Value-at-Risk (VaR) which only considers the maximum loss at a certain confidence level, CVaR considers the severity of the losses beyond the VaR threshold. This makes CVaR a more comprehensive measure of risk as it provides an estimate of the expected value of the extreme losses. In the context of insurance, CVaR can be used to assess the potential losses an insurer could face in extreme scenarios, thereby aiding in reserve estimation and stress testing. It’s particularly useful in the context of tail risk management, where the focus is on extreme events that have a small probability of occurrence but could lead to significant losses.\n\n# Quantum Computing Monte Carlo\n\nMonte Carlo methods are ubiquitous in insurance. Sometimes, however, it is not possible to run the Monte Carlo simulations as you would like, because they take too much computer power. Quantum Computing Monte Carlo promises a quadratic speed-up, as it is based on Grover’s algorithm. The result is that the accuracy no longer scales as , rather the QCMC scales as .\n\nThe result is algorithmic speed-up, which translates to:\n\n- Faster results with the same accuracy\n- Higher accuracy in the same calculation time\n- More complex models (fewer assumptions) with the same accuracy in the same calculation time\n\n## Amplitude Estimation\n\nQCMC is based on Amplitude Estimation^{[\\[1\\]](#footnote-1)}. In Amplitude Estimation, you use phase estimation and amplitude amplification to estimate the amplitude of a marked state. The trick is to encode a value of interest in the amplitude of the marked state. Previous work has done this for risk analysis^{[\\[2\\]](#footnote-2)} and option pricing^{[\\[3\\]](#footnote-3)}.\n\nFor more information about how to practically run a CVaR calculation, please refer to the excellent tutorial by IBM attached.\n\n[ Note by Francesco: I don't seem to find any IBM tutorial attached. Here's a qiskit link that refers to the topic - hopefully it was the one originally intended :D https://qiskit-community.github.io/qiskit-finance/tutorials/09_credit_risk_analysis.html#Conditional-Value-at-Risk ]\n\n\n\n## State Preparation\n\nThe question now is how to load the value of interest in the amplitude. There exist methods for this^{[\\[4\\]](#footnote-4)}, however, loading the state has such high overhead in terms of circuit depth, that this sub procedure might negate any computational advantage that QCMC has to offer^{[\\[5\\]](#footnote-5)}.\n\n# The challenge\n\nYour challenge is to create a toy example Quantum Computing Monte Carlo pipeline (e.g., using Qiskit/TKET) to estimate risk and subsequently estimate how much reserves the insurer should keep.\n\nIn that pipeline, focus on the state preparation method and try to come up with a method that fits the NISQ constraints, i.e., relatively many qubits, with relatively poor quality. I.e., use ancilla qubits to decrease the circuit depth. (hint: which gates can a quantum computer execute?)\n\nNow, can you estimate how your pipeline scales? In terms of circuit depth, circuit width, and accuracy.\n\n# Setup\n\n1. Clone the repository:\n ```\n git clone https://github.com/camilledevalk/yquantum-hartford-capgemini\n ```\n1. Navigate to the cloned repository:\n ```\n cd yquantum-hartford-capgemini\n ```\n\n1. Create a new conda environment from the `environment.yml` file:\n ```\n conda env create -f environment.yml -n yquantum\n ```\n\n1. Activate the new environment:\n ```\n conda activate yquantum\n ```\n\nNow, you're ready to run the project!\n\nIn `notebooks/problem-setting.ipynb`, the problem is expressed in code.\n\n# Quantum Hardware\n\nQuantinuum is the platform sponsor for this challenge. To gain access to their technology, follow the instructions here: https://github.com/CQCL/Quantinuum-hackathons\n\nTo test your projects, you must use the Quantinuum backend.\n\n# Prize\n\nThe prize you can win is a summer internship project with Capgemini's Quantum Lab on the use case you worked on\n\n# References\n\n1. Brassard, G., Høyer, P., Mosca, M. & Tapp, A. _Quantum Amplitude Amplification and Estimation_. (2000). [↑](#footnote-ref-1)\n\n1. Woerner, S. & Egger, D. J. Quantum risk analysis. _npj Quantum Information_ **5**, (2019). [↑](#footnote-ref-2)\n\n1. Stamatopoulos, N. _et al._ Option Pricing using Quantum Computers. (2019) doi:[10.22331/q-2020-07-06-291](https://doi.org/10.22331/q-2020-07-06-291). [↑](#footnote-ref-3)\n\n1. Grover, L. & Rudolph, T. Creating superpositions that correspond to efficiently integrable probability distributions. Preprint at (2002). [↑](#footnote-ref-4)\n\n1. Herbert, S. The Problem with Grover-Rudolph State Preparation for Quantum Monte-Carlo. _Phys. Rev. E_ **103**, 063302 (2021). [↑](#footnote-ref-5)\n\n1. Akhalwaya, I. Y. _et al._ A Modular Engine for Quantum Monte Carlo Integration. Preprint at http://arxiv.org/abs/2308.06081 (2023)."},{"id":649,"name":"QuEra Challenge Track","slug":"quera-challenge-track","description":"# Challenge Statement: An excursio on many-body quantum scars\n\n*Background:* many-body quantum scars are arguably the first, and only, discovery of new physical phenomena achieved by experimentation with quantum computers to date. In this phenomenon, a many-body quantum system is driven into one state of a degenerate ordered phase and given a sudden “drive kick” – technically known as a quench. This quench onsets a melting of the ordered phase, which then evolves under the influence of quantum mechanics. Organically, and surprisingly, the melted system “refreezes” in another instance of the set of degenerate phases. The process keeps repeating itself – naturally losing coherence.\n\nChallenge goals: In most hackathons, particularly in quantum ones, a big emphasis is given to application development. In this case, the application comes ready for you: you will be playing with quantum scars. The phenomenon still holds mysteries in physics and holds promising application directions as an algorithmic resource to enhance optimization protocols. In this challenge, we want to bring out the experimentalist in you and focus on close interactions with real hardware and data-analysis to extract design directives for performant quantum computing. \n\n## Activities\n\nWith a total of 7000 shots of quantum compute, you are tasked with:\n* Securing experiments that demonstrate 1 scar revival between 2 ordered phases (one going to the other, no return) while seeking\n * Largest possible number of atoms\n * Largest probability of revival\n\nWe expect as deliverables\n* a write up on your experiment showcasing your understanding and analysis of the problem\n* a Bloqade python module to deploy readout error mitigation methodologies on local observables\n\nOptimization directions for your experiment may include:\n-\tLattice choice.\n-\tWaveform choice.\n- Readout error mitigation strategies.\n\n\nAnd to keep things exciting, remember: we want to bring out the experimentalist in you! The prize goes to the best experimental demonstration, so creativity counts! Even if large systems with high probability are the target, showing scars in novel geometries, implementing new protocols for data analysis, or other novel approaches to the phenomenon analysis will count towards your chance for victory!\n\n\n### References\n\n1) https://www.science.org/doi/full/10.1126/science.abg2530\n2) https://www.nature.com/articles/nature24622\n3) https://arxiv.org/pdf/1711.03528.pdf\n4) https://arxiv.org/pdf/2006.14044.pdf\n5) https://mitiq.readthedocs.io/en/stable/guide/rem-5-theory.html\n\n\n# Read Me\n\nHere you will learn all the details needed to operate QuEra resources for YQuantum 2024. \n\nFor basic materials on Bloqade and Aquila usage, consult QuEra's [educational platform](https://qbook.quera.com/).\n\n## Installing Bloqade and other packages \n\nBloqade, QuEra's neutral atom emulator and SDK, is going to be the key software you will operate for this challenge. Bloqade has both Julia-based and Python-based versions and we suggest focusing on the latter here.\n\nFor this event, you will need to operate it from inside qBraid's platform, but if you want to run emulations by yourself, you can easily install Bloqade locally via `pip install bloqade`.\n\nGuidelines for the Julia version can also be found [here](https://queracomputing.github.io/Bloqade.jl/dev/).\n\n## Working on qBraid\n\nWhile simulations and emulations of your program can be done locally on your computer, operation of QuEra's systems for this event will mandatorily go via qBraid (where you can also do the emulations, if you want). \n\nTo access the service, simply sign up at [qBraid's website](https://www.qbraid.com/) and use the promo code `QUERA_QBRAID_YALE` to get your credits for the event.\n\nFor other questions or additional help using qBraid, see [Lab User Guide](https://qbraid-qbraid.readthedocs-hosted.com/en/latest/lab/overview.html), or reach out on [Discord](https://discord.gg/gwBebaBZZX).\n\n## Quantum resources availability and code of conduct\nTo guarantee fair sharing of resources among teams, here are the guidelines for usage of QuEra's quantum computing resources during the hackaton:\n* Tasks are to be limited to **500 repetition shots**.\n* Hybrid optimization jobs will **not** be allowed.\n* Aquila will be continuously available to participants but will break for one hour for calibration on *Sunday 7a.m.*\n\n## Documentation\n\nThis year’s YQuantum challenges require a write-up/documentation portion that is heavily considered during judging. The write-up is a chance for you to be creative in describing your approach and describing your process. It should clearly explain the problem, the approach you used, your implementation with results from simulation and hardware, and how you accessed the quantum hardware (total number of shots used, backends used, etc.).\n\n## Submission\n\nTo submit the challenge, place all of your materials - code you wrote, presentation documents, Bloqade module - on your DoraHacks repo! Consult DoraHacks' team for directions!"},{"id":650,"name":"Classiq Challenge Track","slug":"classiq-challenge-track","description":"Classiq’s Challenge for YQuantum 2024: Sparse State-Preparation\n\nWelcome to Classiq’s Sparse State-Preparation Challenge, for |Y⟩Quantum 2024. This document serves as a comprehensive challenge guide, designed for quantum computing enthusiasts at all levels. From beginners to seasoned experts, we aim to enrich your understanding of sparse state-preparation and spur innovation in the field of quantum computing, specifically focusing on the nuanced domain of sparse state-preparation. Classiq’s platform, QMOD language, and SDK offer powerful abstractions for expediting the process of quantum algorithm design, prototyping, implementation, and testing. Your team will leverage these abstractions in order to develop a competitive solution within a 24 hour period.\n\nBackground: State Preparation\nState preparation underpins quantum computing, acting as the foundational step for a multitude of quantum algorithms across diverse fields such as quantum linear algebra, quantum chemical modeling, quantum search protocols, and quantum machine learning. It involves configuring a quantum state to represent a specific vector of probabilities or a predefined function. The efficiency of state preparation is a critical determinant in harnessing the true potential of quantum computational advantages, particularly in scenarios where classical computation becomes impractical due to exponential increases in resource requirements. Classiq’s SDK and QMOD language have built-in functions for quantum state preparation. However, it is not computationally efficient for sparse state preparation.\n\nThe Problem: Sparse State-Preparation\n\nSparse state-preparation, a specialized subset of state preparation, emphasizes the generation of quantum states characterized by a significant proportion of zero amplitudes. This feature is prevalent in numerous quantum computing applications, where the desired quantum state comprises a small number of non-zero amplitudes amidst a potentially vast set. By exploiting the inherent sparsity, sparse state-preparation algorithms aim to drastically reduce the quantum resources required, such as quantum gates and circuit complexity. This aspect is vital for the practical execution of complex quantum algorithms on near-term quantum hardware, which is constrained by limitations in qubit count, gate fidelity, and coherence times.\n\nChallenge Brief\n\nObjective: The challenge is designed to inspire participants to develop quantum circuits capable of efficiently preparing sparse quantum states. Entrants are tasked with creating circuits that load a quantum state with probabilities corresponding to a given sparse vector of probabilities, ensuring the presence of \n\n\nonly a constant number of non-zero probabilities. The key lies in achieving this with minimal resource utilization and maximal fidelity. \"An Efficient Algorithm for Sparse Quantum State Preparation\" by Niels Gleinig and Torsten Hoeﬂer provides a two-part algorithm for sparse state preparation. Participants should read, understand, and base their implementation off of the paper.\n\nGiven a list of indices and their respective probabilities, your team is tasked with creating a circuit such that it loads a state with amplitudes in the magnitudes of these probabilities. The circuit will not take phase amplitude into account, meaning that ai will always be a square root of a real number. \n\n$ |\\psi⟩_{n}=\\sum_{I}a_{I}|I⟩_{n} such that a_{I}^2=pi $.\n(notice that your team has the freedom to load the amplitude with an arbitrary phase).\n\nExample Scenario:\n- Input: A probabilities dictionary, such as:\nSparse_states = {‘00000001’: 0.25, ‘00010001’: 0.5, ‘11001000’: 0.25}\n\n- Output Goal: Efficiently load a quantum state reflecting these probabilities, such as:\n|⟩8=0.25|1⟩8+0.5|17⟩8+0.25|200⟩8\n\n- A sample output for the specified example:\n\n\n\n\nDetailed Considerations for Participants\n- Auxiliary Qubits: Their use is permitted, provided they do not remain entangled or in non-zero states post-operation.\n- Adaptability and Efficiency: Solutions should be versatile, considering the trade-offs between circuit depth, number of auxiliary qubits, gate count, and other circuit parameters.\n- Innovative Approach: Encourage exploration beyond conventional methodologies.\n\n\n\nComprehensive Submission Guidelines\nParticipants are required to submit:\n1. `.qmod` and `.qprog` files showcasing their quantum circuit design and implementation.\n2. Results demonstrating the execution of their circuit, saved as JSON.\n3. The Python code employed for model generation.\n4. An in-depth explanation detailing their approach and methodology. Create a README, outlining an abstract overview of your team's implementation. The README should outline the functional outline of all Quantum Functions used in your program, including their parameters/arguments and outputs.\n\nEvaluation Criteria\nSubmissions will be assessed based on:\n1.) Target Problem Solving: Correct sparse quantum state preparation for the following scenarios:\nFor 6 qubits: Probs = {‘000011’: 0.4, ‘111111’: 0.6}\nFor 10 qubits: Probs = {‘0000000101’: 0.33, ‘0000010001’: 0.33, ‘0001100010’: 0.34}\nFor 20 qubits: Probs = {‘00000000000000000101’: 0.6, ‘00000000000000010001’: 0.4}\n2.) Optimization Balance: Utilizing Pareto analysis to evaluate the balance between circuit depth and auxiliary qubit usage.\n3.) Originality and Creativity: Encouraging unique and innovative solutions.\n4.) Scalability and Versatility: The ability to efficiently manage different numbers of qubits and non-zero coefficients.\n\nGuidelines for Implementation\n- Foundational Understanding: Solidify your grasp of quantum state preparation fundamentals and the critical role of sparsity.\n- Resource Optimization: Prioritize minimizing quantum resources while maximizing performance and accuracy.\n- Circuit Design Exploration: Engage with various quantum circuit design and optimization strategies to discover the most efficient approach for sparse state preparation.\n- Rigorous Testing: Ensure thorough testing and validation of your circuits to confirm the accurate preparation of the desired sparse quantum states.\n\nKeep Note\n\nYou're tasked with creating quantum circuits to prepare specific states. You are permitted to utilize auxiliary qubits. Nonetheless, it's essential to ensure that at the circuit's conclusion, there are no residual non-zero or entangled qubits apart from the designated loaded state. You will be provided a template notebook with some already defined functions. You are tasked with implementing the remaining logic based on the two-part algorithm described in the provided research paper, \"An Efficient Algorithm for Sparse Quantum State Preparation\".\n\nThere's a spectrum of potential solutions for this task. The efficiency of particular approaches might fluctuate based on problem dimensions. Furthermore, one must navigate trade-offs between multiple circuit parameters: the circuit's depth, the count of auxiliary qubits, gate numbers, and so on. Take note of the following example comparing prepare_sparse_states and the built-in prepare_state. Notice that a proper implementation of prepare_sparse_states creates a circuit with depth of 8. In comparison the built-in prepare_state function creates the same quantum state with a circuit depth of 63.\n\n\n\n@qfunc\ndef main(psi: Output[QArray[QBit]]):\n sparse_state_dic ={'000':0.25, '001':0.5, '111':0.25}\n prepare_sparse_state(dictionary=sparse_state_dic,out=psi)\n\n\n\n\n\n@qfunc\ndef main(psi: Output[QArray[QBit]]):\n sparse_state_dic ={'000':0.25, '001':0.5, '111':0.25}\n sparse_state_array = convert_dic2array(sparse_state_dic)\n prepare_state(sparse_state_array,0.001,psi)\n\nBest of Luck to All Participants!\n\nParticipating in this challenge presents a unique opportunity to contribute to the advancement of quantum computing technology and to explore new frontiers in the quantum domain. We invite you to leverage your creativity, knowledge, and passion for quantum computing to tackle this engaging and challenging problem.\n\n\n\n\n\n\n\n\n\n\n\n\n\n![Screenshot 2024-04-11 at 3.15.48 PM.png](https://cdn.dorahacks.io/static/files/18ed7d37731dfd3d1b0dbaf48b687784.png)"},{"id":651,"name":"SandboxAQ Challenge Track","slug":"sandboxaq-challenge-track","description":"# The Challenge\n![Screenshot 2024-04-12 at 10.39.22 PM.png](https://cdn.dorahacks.io/static/files/18ed409753c849f6421bfb94379b8e3c.png)\n\n# The Tools\n![Tangelo Image](https://lh7-us.googleusercontent.com/ns6bh8g3TnAZs9cdBjipERNuYosRuB5IFVlTmEbn7JMfbMpTb4b-u9jgeaFiM201jZvhZbAviE76hTOS07fLPZVXL0j38PCyIIypOclyOktqwR6qvUf9tgUe3J6_H5T28rEQ_p_Smvtq2mCdqIH8D7E)\n\nangelo is an open-source python package for both NISQ and FT chemistry workflows on quantum computers, available through pypi and github. It was created by Good Chemistry and is now owned by SandboxAQ.\n\n\nIt features a growing collection of building-blocks and algorithms for chemistry simulation, built on top of a straightforward yet powerful backend-agnostic layer leveraging well-known frameworks in the quantum ecosystem, compatible with qBraid and many other well-known ones.\n\n![Tangelo Workflow](https://lh7-us.googleusercontent.com/Fhuig8GCrei3jAqg8_g9AY75g3OOCl-wJo4eKW6UPJ0pjrqZ_rahNGdXKOmL-GbFkhsnKZe1ARbLrXPuhswbHUHI623F8FdG-FBpQPQlGp-yaAguJJMhcdD38S5BR6bAPrz4OE0U_ZVof1iTcgzyOJQ)\n\n## Embarking on a hackathon ? Good news: you can do a lot in little time with Tangelo.\n\nA lot of what Tangelo has to offer can be immediately applied to fields other than chemistry or drug discovery out of the box:\n\n- Define your circuit, specify how you’d like to simulate it, pick the best backend for your needs by changing one single keyword (state vector simulator ? GPU?) and go. Further options even support noise models, mid-circuit measurements and retrieving data corresponding to desired mid-circuit measurement results.\n\n`\nsim = get_backend(\"cirq\", n_shots=ns)\nfreqs, sv = sim.simulate(circuit, return_statevector=True)\n`\n\n- Easily convert circuits and operators between Tangelo and your favorite frameworks. Why constrain yourself to one platform when you can leverage all the innovation from the community in your projects ? Want to reuse your existing Qiskit code, plug that into Tangelo and then push the results into qBraid’s API ? Go for it !\n\n`\nfrom tangelo.linq import translate_circuit, translate_operator\n\nc_qiskit = translate_circuit(c, target=\"qiskit\")\nop_qiskit = translate_operator(op, source=\"tangelo\", target=\"qiskit\")\n` \n\n- We believe running experiments on hardware should be simple:\n\n`\nfrom tangelo.linq.qpu_connection import IBMConnection\n\nconn = IBMConnection(ibm_quantum_token='your_ibm_token')\njob_id = conn.job_submit('sampler', 'ibmq_qasm_simulator', n_shots=10**5, tangelo_circuit)\njob_results = conn.job_results(job_id)\n`\n\nMore about available algorithms, resource estimation and advanced toolboxes in our tutorials !\n\n## Quickly get up to speed with tutorials and hands-on exercises\n\nTangelo comes with an abundance of tutorials about quantum computing, chemistry modeling, and detailed experiments run on quantum hardware. It’s not just about code: you get to learn from researchers and developers how to take your exploration further. We tag tutorials so that you can quickly find the ones relevant to your interests.\n\n![Tangelo tutorial](https://lh7-us.googleusercontent.com/6t3Mx7U-VpfEJMxCAQYGZCW1doF5CoKsh4aTH2FS-OEWWMWDvn-pxmlhpl5sSA5Y-AICFTfeV-BJH9PUnMQYdU4kSQENvvA4-cWqqC3rKjjq2FYU8e3lW5p9y1XJGQdJ4mOfYNS13qMeBlrY9MRk0Cw)\n\nYou can get started with our hands-on exercises with no local install: just click on ![colab image](https://lh7-us.googleusercontent.com/rSROzZiFbe4SfIrrTqpL5FplCfEaI0P3WYsDlP9c9OZtclc-wevtH1byNC93_FNKj0lST-rchs4sLAQ1TtP0gC2gO0sjZuad14Udkvb4J2xzC7Cf93cfAnZ6Br3lKCNwPoLmnFEE7NYTfPTFGQTojQI) on the front page of the tutorial Github repository, scroll down to open the “hands-on” Jupyter notebooks and voila: you are ready to learn and hack ! From basic operations about gates and circuits to toolboxes for hardware experiments, FT operations and chemistry algorithms: you can cover a lot quickly. We ran a tutorial last year at IEEE quantum week (QCE23): attendees explored the content at their leisure and got started in 2 minutes, some just using a tablet !\n\n## What will you do with Tangelo ?\n\nHave fun at the hackathon ! Do not hesitate to reach out on Github or to SandBoxAQ if you encounter bugs or have questions: coding is tough, and so is finding what you need inside software packages.\n\nContributions and feedback are super appreciated and needed in order to always improve on our tools ! 🍊"},{"id":652,"name":"DoraHacks Challenge Track","slug":"dorahacks-challenge-track","description":"# AI-inspired Classification of Quantum Computers\n\n## Prizes\nA maximum of $300 USDC in prizes will be distributed, contingent upon the presentation of meaningful results in the submissions. If multiple teams produce significant results, the prizes will be divided among the winners. The prizes will be distributed through DoraHacks' DAO Bounties platform.\n\n# Motivation\nQuantum computers promise diverse applications with fault-tolerant architectures. Near-term quantum computers are comprised of different physical entities, such as superconducting qubits, propagating photons, trapped ions and tweezer-based neutral atoms. \n\nRandomness lies in the cornerstone of quantum mechanics, especially in the dispute of reality and non-locality [1]. After decades of experimental progress [2], loophole-free Bell inequality was demonstrated by several groups using photons [3, 4] and superconducting qubits [5]. As a result, device-independent certified quantum random number generator [6] and randomness expansion [7, 8] are cutting-edge and realistic applications. Besides, one of the potential applications of NISQ quantum advantages (e.g. random circuits sampling) is also certified randomness [9, 10].\n\nCurrent cloud-based quantum computers service is ready for producing quantum random numbers for cryptography. However, due to the notorious decoherence processes, the raw entanglement between isolated qubits is deteriorated without any digital error-mitigation technique (like readout correction). Thus, we are curious about the randomness degradation. More specifically, is it possible to distinguish the given random numbers are produced from a specific quantum computer?\n\nTake the output from a quantum random number generator as training data, the data is produced by different IBMQ simulators (including noise). Find a way to create a model that classifies new random number output, and identifies which machine the random number is produced from. The quantum random number generator functions as described in https://github.com/dorahacksglobal/quantum-randomness-generator. Run experiments by yourself, and compare different models. Is this classification task possible? What are the possible ways to improve the model so that it's useful?\n\nMore generally, this problem is related to the topic of cross-platform comparison of quantum circuits [11, 12].\n\n# Training Data\nData file link [here](https://drive.google.com/file/d/1xXu_8GGO7QxnQhwX4H1yVFXwErNaYFHH/view?usp=sharing). \n\n# References\n[1] Einstein, Albert, Boris Podolsky, and Nathan Rosen. \"Can quantum-mechanical description of physical reality be considered complete?.\" Physical review 47.10 (1935): 777.
\n[2] Hensen, Bas, et al. \"Loophole-free Bell inequality violation using electron spins separated by 1.3 kilometres.\" Nature 526.7575 (2015): 682-686.
\n[3] Giustina, Marissa, et al. \"Significant-loophole-free test of Bell’s theorem with entangled photons.\" Physical review letters 115.25 (2015): 250401.
\n[4] Shalm, Lynden K., et al. \"Strong loophole-free test of local realism.\" Physical review letters 115.25 (2015): 250402.
\n[5] Storz, Simon, et al. \"Loophole-free Bell inequality violation with superconducting circuits.\" Nature 617.7960 (2023): 265-270.
\n[6] Abellán, Carlos, et al. \"Generation of fresh and pure random numbers for loophole-free Bell tests.\" Physical review letters 115.25 (2015): 250403.
\n[7] Liu, Wen-Zhao, et al. \"Device-independent randomness expansion against quantum side information.\" Nature Physics 17.4 (2021): 448-451.
\n[8] Shalm, Lynden K., et al. \"Device-independent randomness expansion with entangled photons.\" Nature Physics 17.4 (2021): 452-456.
\n[9] Aaronson, Scott, and Shih-Han Hung. \"Certified randomness from quantum supremacy.\" Proceedings of the 55th Annual ACM Symposium on Theory of Computing. 2023.
\n[10] Morvan, Alexis, et al. \"Phase transition in random circuit sampling.\" arXiv preprint arXiv:2304.11119 (2023).
\n[11] Zhu, Daiwei, et al. \"Cross-platform comparison of arbitrary quantum states.\" Nature communications 13.1 (2022): 6620.
\n[12] Elben, Andreas, et al. \"Cross-platform verification of intermediate scale quantum devices.\" Physical review letters 124.1 (2020): 010504.
\n\n# Judging Criteria\nThis is an open-ended challenge. There are no specific deliverable requirements, and we're excited to see any meaningful results from the submissions.\n\n# Internship Opportunities\nWe offer two internship opportunities, available to all YQuantum participants: one in **DI-QRNG Product Research and Development**, and the other in **Post-Quantum Blockchain Cryptography Research**. Please reach out to us if you are interested.\n\n# Contact Information\nEmail: xibei@dorahacks.com \n\n# About DoraHacks\nDoraHacks is a global hackathon organizer and one of the world's most active developer incentive platforms. It creates a global hacker movement in space tech, blockchain and quantum computing, and provides a wide range of toolkits to help developers around the world team up and fund their ideas and BUIDLs via hackathons, bounties, grants, idea networks, developer games and more.\n\nBy far, more than 7,000 startup teams from the DoraHacks community have received over $60 million in grants and other forms of contributions from supporters worldwide. A large number of open source communities, companies and blockchain ecosystems are actively using Dora's infrastructure (https://dorahacks.io) for open source funding and community governance."},{"id":702,"name":"Results","slug":"results","description":"# Announcing YQuantum 2024 Winners\n\nYQuantum, Yale's leading quantum computing hackathon, made its debut on April 13th, 2024, organized by the Yale Quantum Institute. This inaugural event attracted thinkers and innovators from around the globe to confront tomorrow's quantum challenges. With 300 registrants hailing from 10 countries and representing 32 universities, YQuantum 2024 formed 36 teams to tackle 6 challenges set by industry sponsors. Without further ado, let's unveil the winners of the Grand Prizes and each track.\n\n\n# THE YALE QUANTUM INSTITUTE GRAND PRIZES Sponsored by DoraHacks \n\n## 1st Place\n\n**Winner: Quantum Consortium**\n\nTeam members: Caden Kacmarynski, Xavier Moskala, Adrian Harkness, Dmitrii Khitrin, David Nizovsky\n\n[BUIDL](https://dorahacks.io/buidl/11284)\n\nBUIDL link: https://dorahacks.io/buidl/11284\n\n## 2nd Place\n\n**Winner: The Unitary Operators**\n\nTeam members: Dale Jacobs, Randy Dang, Mateusz Zubrzycki, Daniel Harrington, Shariq Rifai\n\n[BUIDL](https://dorahacks.io/buidl/11275)\n\nBUIDL link: https://dorahacks.io/buidl/11275\n\n## 3rd Place\n\n**Winner: Qubruin**\n\nTeam members: Zhuoyang Ye, Qiyu Liu, Manvi Agrawal, Changsoo Kim, Haochen Wang\n\n[BUIDL](https://dorahacks.io/buidl/11299)\n\nBUIDL link: https://dorahacks.io/buidl/11299\n\n# **INDIVIDUAL TRACK PRIZES**\n\n## **IBM Quantum**\n \n**Winner: Quantum Nexus**\n \nTeam members: Bryanna Boroze, Shees Siddiqy, Jack Angelastro\n\n[BUIDL](https://dorahacks.io/buidl/11285)\n \nBUIDL link: https://dorahacks.io/buidl/11285\n \n## **QuEra Computing**\n \n**Winner: RabiFlops**\n \nTeam members: Kenneth Kohn, Andrii Torchylo, Sam Jett, Dhruv Mangtani, Boxin Zhang\n \n[BUIDL](https://dorahacks.io/buidl/11301)\n\nBUIDL link: https://dorahacks.io/buidl/11301\n \n## **Classiq Technologies**\n \n**Winner: Wavefunction Wizards**\n \nTeam members: Jose Zamora Orellana, Maximilian Rieger, Thomas Greenleaf, Aniruddha Sen\n\n[BUIDL](https://dorahacks.io/buidl/11293)\n \nBUIDL link: https://dorahacks.io/buidl/11293\n \n## **DoraHacks**\n \n**Winner: Spooners**\n \nTeam members: Siddarth Chava, Siji Doherty, Shrey Khater, Aditi Baghel, Akshat Rakheja\n\n[BUIDL](https://dorahacks.io/buidl/11292)\n \nBUIDL link: https://dorahacks.io/buidl/11292\n \n- **Capgemini // Quantinuum // The Hartford**\n \n**Winner: skittlez**\n \nTeam members: Eileen Rashduni, Tanusri Ramakrishnan, Jesus Fileto, Danny Assi, Pradyun Solai\n \n[BUIDL](https://dorahacks.io/buidl/11294)\n\nBUIDL link: https://dorahacks.io/buidl/11294\n \n**Honorable mention: BB24**\n\nTeam members: Jennifer Fomenko, Pravin Mahendran, Priyam Gupta, Sahana Ganesh, Kharissa Moore\n\n[BUIDL](https://dorahacks.io/buidl/11289)\n\nBUIDL link: https://dorahacks.io/buidl/11289\n\n## **SandboxAQ**\n \n**Winner: Quantum Quails**\n \nTeam members: Sanskriti Shindadkar, Khaled Khan, Maksym Tymchyshyn, Kunal Lalwani, Richa Yadav\n \n[BUIDL](https://dorahacks.io/buidl/11283)\n\nBUIDL link: https://dorahacks.io/buidl/11283\n \n\n**Honorable mention: Huskies**\n\nTeam members: Shannen Espinosa, Siona Tagare, Gun Suer\n\n[BUIDL](https://dorahacks.io/buidl/11288)\n\nBUIDL link: https://dorahacks.io/buidl/11288"}],"skills":null,"applied":false,"isCurrentHackerRegistered":false,"buidlsCount":28,"applicationsCount":28,"organization":{"id":2121,"name":"YQuantum","logo":"https://cdn.dorahacks.io/static/files/18ea1b8b784d0a022b38ca3425cbaa48.png"},"description":"# Welcome to YQuantum 2024!\n\nThis platform will be the heart of all hackathon operations for YQuantum 2024. You will get all the necessary information for the challenges here, including judging criteria, and you will use this platform to submit your hacks (also known as BUIDLs). If you have any questions about anything that happens during the hackathon, use the #ask-an-organizer channel on Discord.\n\n# Challenges\n\nTo get detailed information about your challenge, please visit the dedicated tab. Submission will be done through the BUIDL tab, where you can select the track that you are submitting to. **You may only submit to one track.**\n\n**TRACK PRIZES**\n\n- **QuEra Computing**: QuEra swag pack, public QuEra blog post, potential speaking opportunity @ QuEra HQ based on quality of project.\n- **IBM Quantum**: TBA.\n- **Classiq**: Quantum cloud credits ($1500) to be redeemed on any quantum hardware, public Classiq blog post.\n- **DoraHacks**: A total of up to $300 USDC will be distributed among teams that excel in generating meaningful results for the challenge. Additionally, DoraHacks is excited to offer two internship opportunities, available to all: one in DI-QRNG Product Research and Development, and the other in Post-Quantum Blockchain Cryptography Research.\n- **SandboxAQ**: SandboxAQ swag pack, presentation opportunity @ SandboxAQ HQ, career connections and networking with staff.\n- **Capgemini/The Hartford/Quantinuum**: Summer internship at Capgemini's Quantum Lab to develop your project further, presentation for Capgemini Financial Services w/ Quantum.\n\n**GRAND PRIZES**\n- *3rd Place Overall*: $500, Presentation opportunity in front of Yale researchers and professors, inclusion in presentation at the Yale Innovation Summit, YQI Swag including Quantum Sound vinyl.\n- *2nd Place Overall*: $1000, Presentation opportunity in front of Yale researchers and professors, inclusion in presentation at the Yale Innovation Summit, YQI Swag including Quantum Sound vinyl.\n- *1st Place Overall*: $2000, Presentation opportunity in front of Yale researchers and professors, inclusion in presentation at the Yale Innovation Summit, YQI Swag including Quantum Sound vinyl.\n\n\n\n\n# Rules\n\n1. 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