Quantum Deep Hedging - Plato AiStream V2.1

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ال امین چررات^1,2, سنیہل راج¹, Iordanis Kerenidis^1,2, Abhishek Shekhar³, Ben Wood³, Jon Dee³, شووانک چکربرتی⁴, رچرڈ چن⁴, Dylan Herman⁴, Shaohan Hu⁴, Pierre Minssen⁴, Ruslan Shaydulin⁴, Yue Sun⁴, Romina Yalovetzky⁴، اور مارکو پیسٹویا⁴

¹کیو سی ویئر
²Université de Paris, CNRS, IRIF
³Quantitative Research, JPMorgan Chase
⁴Global Technology Applied Research, JPMorgan Chase

اس کاغذ کو دلچسپ لگتا ہے یا اس پر بات کرنا چاہتے ہیں؟ SciRate پر تبصرہ کریں یا چھوڑیں۔.

خلاصہ

Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-critic algorithms that use quantum neural network architectures with orthogonal and compound layers for the policy and value functions. We prove that the quantum neural networks we use are trainable, and we perform extensive simulations that show that quantum models can reduce the number of trainable parameters while achieving comparable performance and that the distributional approach obtains better performance than other standard approaches, both classical and quantum. We successfully implement the proposed models on a trapped-ion quantum processor, utilizing circuits with up to $16$ qubits, and observe performance that agrees well with noiseless simulation. Our quantum techniques are general and can be applied to other reinforcement learning problems beyond hedging.

► BibTeX ڈیٹا

@article{Cherrat2023quantumdeephedging, doi = {10.22331/q-2023-11-29-1191}, url = {https://doi.org/10.22331/q-2023-11-29-1191}, title = {Quantum {D}eep {H}edging}, author = {Cherrat, El Amine and Raj, Snehal and Kerenidis, Iordanis and Shekhar, Abhishek and Wood, Ben and Dee, Jon and Chakrabarti, Shouvanik and Chen, Richard and Herman, Dylan and Hu, Shaohan and Minssen, Pierre and Shaydulin, Ruslan and Sun, Yue and Yalovetzky, Romina and Pistoia, Marco}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{“{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {7}, pages = {1191}, month = nov, year = {2023} }

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کی طرف سے حوالہ دیا گیا

[1] Enrico Fontana, Dylan Herman, Shouvanik Chakrabarti, Niraj Kumar, Romina Yalovetzky, Jamie Heredge, Shree Hari Sureshbabu, and Marco Pistoia, “The Adjoint Is All You Need: Characterizing Barren Plateaus in Quantum Ansätze”, آر ایکس سی: 2309.07902, (2023).

[2] ڈیلن ہرمن، کوڈی گوگین، ژاؤیوآن لیو، یو سن، الیکسی گالڈا، الیا سیفرو، مارکو پسٹویا، اور یوری الیکسیف، "کوانٹم کمپیوٹنگ فار فنانس"، فطرت کا جائزہ طبیعیات 5 8, 450 (2023).

[3] Alexandr Sedykh, Maninadh Podapaka, Asel Sagingalieva, Karan Pinto, Markus Pflitsch, and Alexey Melnikov, “Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes”, آر ایکس سی: 2304.11247, (2023).

مذکورہ بالا اقتباسات سے ہیں۔ SAO/NASA ADS (آخری بار کامیابی کے ساتھ 2023-11-29 13:34:05)۔ فہرست نامکمل ہو سکتی ہے کیونکہ تمام ناشرین مناسب اور مکمل حوالہ ڈیٹا فراہم نہیں کرتے ہیں۔

نہیں لا سکا کراس ریف کا حوالہ دیا گیا ڈیٹا آخری کوشش کے دوران 2023-11-29 13:34:04: Crossref سے 10.22331/q-2023-11-29-1191 کے لیے حوالہ کردہ ڈیٹا حاصل نہیں کیا جا سکا۔ یہ عام بات ہے اگر DOI حال ہی میں رجسٹر کیا گیا ہو۔

یہ مقالہ کوانٹم میں کے تحت شائع کیا گیا ہے۔ Creative Commons انتساب 4.0 انٹرنیشنل (CC BY 4.0) لائسنس کاپی رائٹ اصل کاپی رائٹ ہولڈرز جیسے مصنفین یا ان کے اداروں کے پاس رہتا ہے۔

SEO سے چلنے والا مواد اور PR کی تقسیم۔ آج ہی بڑھا دیں۔
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پلیٹو ای ایس جی۔ کاربن، کلین ٹیک، توانائی ، ماحولیات، شمسی، ویسٹ مینجمنٹ یہاں تک رسائی حاصل کریں۔
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ماخذ: https://quantum-journal.org/papers/q-2023-11-29-1191/

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