no code implementations • 14 May 2024 • Jamie Heredge, Niraj Kumar, Dylan Herman, Shouvanik Chakrabarti, Romina Yalovetzky, Shree Hari Sureshbabu, Changhao Li, Marco Pistoia
We establish conditions on the encoding map such as classical simulatability, overlap with DLA basis, and its Fourier frequency characteristics that enable such a privacy breach of VQC models.
no code implementations • 7 Dec 2023 • Changhao Li, Niraj Kumar, Zhixin Song, Shouvanik Chakrabarti, Marco Pistoia
Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limitations of individual quantum nodes.
no code implementations • 19 Oct 2023 • Changhao Li, Boning Li, Omar Amer, Ruslan Shaydulin, Shouvanik Chakrabarti, Guoqing Wang, Haowei Xu, Hao Tang, Isidor Schoch, Niraj Kumar, Charles Lim, Ju Li, Paola Cappellaro, Marco Pistoia
Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes.
no code implementations • 29 Mar 2023 • El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, Marco Pistoia
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.
no code implementations • 26 Mar 2023 • Xuchen You, Shouvanik Chakrabarti, Boyang Chen, Xiaodi Wu
In this work, we study the dynamics of QNNs and show that contrary to popular belief it is qualitatively different from that of any kernel regression: due to the unitarity of quantum operations, there is a non-negligible deviation from the tangent kernel regression derived at the random initialization.
no code implementations • 29 Nov 2022 • Lucas Slattery, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Sami Khairy, Stefan M. Wild
We show that the general-purpose hyperparameter tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well-approximated by a classical kernel, removing the possibility of quantum advantage.
no code implementations • 25 May 2022 • Xuchen You, Shouvanik Chakrabarti, Xiaodi Wu
The Variational Quantum Eigensolver (VQE) is a promising candidate for quantum applications on near-term Noisy Intermediate-Scale Quantum (NISQ) computers.
no code implementations • 9 Sep 2021 • Marco Pistoia, Syed Farhan Ahmad, Akshay Ajagekar, Alexander Buts, Shouvanik Chakrabarti, Dylan Herman, Shaohan Hu, Andrew Jena, Pierre Minssen, Pradeep Niroula, Arthur Rattew, Yue Sun, Romina Yalovetzky
In fact, finance is estimated to be the first industry sector to benefit from Quantum Computing not only in the medium and long terms, but even in the short term.
no code implementations • 11 Dec 2020 • Tongyang Li, Chunhao Wang, Shouvanik Chakrabarti, Xiaodi Wu
We give a sublinear classical algorithm that can interpolate smoothly between these two cases: for any fixed $q\in (1, 2]$, we solve the matrix game where $\mathcal{X}$ is a $\ell_{q}$-norm unit ball within additive error $\epsilon$ in time $\tilde{O}((n+d)/{\epsilon^{2}})$.
1 code implementation • 2 Apr 2020 • Shaopeng Zhu, Shih-Han Hung, Shouvanik Chakrabarti, Xiaodi Wu
We also conduct a case study of training a VQC instance with controls, which shows the advantage of our scheme over existing auto-differentiation for quantum circuits without controls.
1 code implementation • NeurIPS 2019 • Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu
The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines.
no code implementations • 4 Apr 2019 • Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu
We design sublinear quantum algorithms for the same task running in $\tilde{O}(\sqrt{n} +\sqrt{d})$ time, a quadratic improvement in both $n$ and $d$.