1 point by quantumleap 1 year ago flag hide 33 comments
quantumleapcomputing 1 year ago next
Excited to announce that Quantum Leap Computing (YC W22) is hiring Machine Learning Engineers! Join us to revolutionize the future of computing with quantum technology. #hiring #quantumcomputing #mlengineer
quantum_enthusiast 1 year ago next
@QuantumLeapComputing That's amazing! I've always been passionate about quantum and I'm excited to see how it can change the world. I'll be applying for sure!
quantumleapcomputing 1 year ago next
@quantum_enthusiast Thanks for your interest in Quantum Leap Computing! We'll be thrilled to have you and your enthusiasm on the team. #quantum #hiring
algorithmguru1 1 year ago prev next
@QuantumLeapComputing I would love to contribute! I specialized in quantum algorithms for optimization on my Ph.D. Do you think my background will suit the position?
quantumleapcomputing 1 year ago next
@algorithmGuru1 Thanks for reaching out. Background in quantum algorithms is exactly what we're looking for in an ideal candidate. Let's arrange a call to talk about your suitability.
bigtechfan 1 year ago prev next
@QuantumLeapComputing Joining as an ML engineer can be an excellent opportunity. Delve into the organization and share more about its tech stack and quantum hardware.
quantumleapcomputing 1 year ago next
@bigTechFan Certainly! Our tech stack includes Python, TensorFlow Quantum for ML, and of course, we're developing cutting-edge quantum hardware. Stay tuned, we'll share more soon!
janedoe 1 year ago prev next
Which programming languages do you recommend learning for quantum machine learning? This space is entirely new to me, and I'd love to learn more.
quantumleapcomputing 1 year ago next
@janeDoe Learning Python, arguably the most popular language for ML in general, is essential. For Quantum ML, Q# and TensorFlow Quantum are worth checking out. You might also look into Cirq, Qiskit, and PennyLane.
quantum_rockstar 1 year ago next
@QuantumLeapComputing And don't forget about ProjectQ and Forest from Rigetti for QML! There's an abundance of resources available, and exploring niche toolsets can be worth it.
quantumleapcomputing 1 year ago next
@quantum_rockstar We couldn't agree more—providing different options is crucial in this rapidly-evolving industry. Keeping these resources at the forefront helps us stay adaptable and facilitate learning!
johndoe 1 year ago prev next
@janeDoe From my understanding, Qiskit, from IBM, also has a lot of relevant resources for QML. Since it has a Python interface, you can test-drive it alongside TensorFlow Quantum.
quantumleapcomputing 1 year ago next
@johnDoe Absolutely, Qiskit has a great community and helps bridge the gap between academia and industry. We like to ensure we give well-rounded advice to those interested in QML.
genericuser 1 year ago prev next
It's exciting to see startups hiring for quantum roles. How do you think young companies will contribute to the field more so than traditional enterprises?
quantumleapcomputing 1 year ago next
@genericUser Young companies can benefit from quantum computing research and development more quickly since they have fewer constraints. They can be more agile and adaptable in evolving landscapes like QC.
alphabetagamer 1 year ago next
@QuantumLeapComputing It would be great to see QC democratized like we've seen with other technologies. Is that part of your company's vision?
quantumleapcomputing 1 year ago next
@alphaBetaGamer Ensuring QC is accessible for everyone is a core principle for us. As we grow, we aim to share our knowledge and resources with the public, fostering an environment for collective advancement.
futuretechnerd 1 year ago prev next
I'm looking for credible resources to learn more about quantum computing and its relation to ML. Any tips on podcasts, blogs, or YouTube channels? Thanks!
quantumleapcomputing 1 year ago next
@futureTechNerd There are many great resources for learning about QC and QML: podcasts like 'The Quantum BS Podcast' and 'Quantum Computing Report'; blogs, e.g., 'Arxiv Insights', 'Medium', and 'Quantum Computing Report'; YouTube channels include 'Quantum Computing Explained' and 'Qiskit.' Enjoy your journey!
justjoinedhn 1 year ago prev next
Do you think there's a valid argument against using QC for ML due to over-hyped expectations?
quantumleapcomputing 1 year ago next
@justJoinedHN It's true there's a lot of excitement around QC nowadays, but that's because we're at the dawn of a new era. Instead of focusing on over-hyped expectations, we need to emphasize the inherent possibilities and believe in the potential QC brings to ML.
newtoquantum 1 year ago prev next
What would you say to someone like me who is just beginning to understand this space? Any advice for learning quantum algorithms and working towards a role in this field?
algorithmguru1 1 year ago next
@newToQuantum It's essential to deepen your understanding of the mathematics behind QML, especially linear algebra and probability, and learn about quantum algorithms and how they differ from classical ones. Keep track of the most recent discoveries and projects to stay updated. MIT OpenCourseWare offers an excellent Quantum Computing course, and there are also many online platforms to learn from, such as Udemy and Coursera. Good luck and welcome to the field!
qcwonder 1 year ago prev next
Will Quantum Leap Computing also benefit from research on Quantum Error Correction?
quantumleapcomputing 1 year ago next
@QCwonder The study of quantum error correction is central to realizing practical QC systems. We actively contribute to and follow the field, integrating the latest methods to help increase qubit quantity and reliability. Thanks for the question!
neuralnetworklee 1 year ago prev next
Do you see limitations or challenges with porting existing ML models to the quantum space? Is it necessary to train models from scratch, or are there reusable techniques for integration?
quantumleapcomputing 1 year ago next
@neuralNetworkLee Porting classic ML models to quantum space has its challenges, namely that direct translation won't benefit from quantum mechanics. That's why we focus on quantum-native or hybrid approaches, transforming specific applications to exploit quantum advantage. We never require models to train from scratch; we aim to bring continuous improvement to the ever-growing ML landscape. There are techniques to translate certain ML models to QML and integrate them into quantum space without reinventing the wheel.
noisyquantumdude 1 year ago prev next
@QuantumLeapComputing Hello! I'm an expert in error mitigation in noisy intermediate-scale quantum computers. I'd love to further discuss any similar projects you have in the pipeline and your thoughts on addressing noisy qubits in quantum annealing. Cheers.
quantumleapcomputing 1 year ago next
@noisyQuantumDude Thanks for your reach-out! We're always excited to talk shop with experts in the field. As you know, error mitigation is critical to addressing noise issues when scaling quantum systems. We're very much interested in discussing the latest advancements and potential collaborations for solving industry-specific challenges. Stay tuned for more announcements, and please sign up for our updates to hear back from us.
noviceqml 1 year ago prev next
I assume that your company is working on VQE, QAOA, and other prominent quantum algorithms. Could you please share a little bit about the breakthroughs in these areas the team has made and its future plans?
quantumleapcomputing 1 year ago next
@NoviceQML We are indeed actively developing both VQE (Variational Quantum Eigensolver) and QAOA (Quantum Approximate Optimization Algorithm) to address different applications, such as solving optimization problems for energy, transportation, and resources. One breakthrough is our ongoing mission to combine VQE and QAOA theoretically and experimentally to create a more efficient computing environment, sharing their advantages and boosting performance beyond traditional methods. Follow our updates to learn about our future plans and collaboration opportunities!
mysteryquantum 1 year ago prev next
Which area in ML is quantum computing most likely to disrupt, and could you also tip the most promising quantum-inspired ML techniques?
quantumleapcomputing 1 year ago next
@mysteryQuantum Quantum computing holds promise in several areas of ML, including clustering, principal component analysis, and classification. Promising quantum-inspired ML techniques include, for example, Quantum-inspired K-Means, Quantum-inspired Learning using Stiefel Embedding, and algorithms based on Amplitude Amplification. There's a lot of exciting work being done, and we're just scratching the surface. Keep an eye on our research and publications for more insights. Cheers!