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pro vyhledávání: '"Bera, Debajyoti"'
Autor:
Bera, Debajyoti, SAPV, Tharrmashastha
Branching programs are quite popular for studying time-space lower bounds. Bera et al. recently introduced the model of generalized quantum branching program aka. GQBP that generalized two earlier models of quantum branching programs. In this work we
Externí odkaz:
http://arxiv.org/abs/2407.06872
Autor:
Bera, Debajyoti, Sapv, Tharrmashastha
Classical branching programs are studied to understand the space complexity of computational problems. Prior to this work, Nakanishi and Ablayev had separately defined two different quantum versions of branching programs that we refer to as NQBP and
Externí odkaz:
http://arxiv.org/abs/2307.11395
The agnostic setting is the hardest generalization of the PAC model since it is akin to learning with adversarial noise. In this paper, we give a poly$(n,t,{\frac{1}{\varepsilon}})$ quantum algorithm for learning size $t$ decision trees with uniform
Externí odkaz:
http://arxiv.org/abs/2210.00212
Autor:
Bera, Debajyoti, Sapv, Tharrmashastha
Amplitude filtering is concerned with identifying basis-states in a superposition whose amplitudes are greater than a specified threshold; probability filtering is defined analogously for probabilities. Given the scarcity of qubits, the focus of this
Externí odkaz:
http://arxiv.org/abs/2208.00162
Categorical attributes are those that can take a discrete set of values, e.g., colours. This work is about compressing vectors over categorical attributes to low-dimension discrete vectors. The current hash-based methods compressing vectors over cate
Externí odkaz:
http://arxiv.org/abs/2112.00362
In this work, we present a dimensionality reduction algorithm, aka. sketching, for categorical datasets. Our proposed sketching algorithm Cabin constructs low-dimensional binary sketches from high-dimensional categorical vectors, and our distance est
Externí odkaz:
http://arxiv.org/abs/2111.07163
Publikováno v:
Quantum Mach. Intell. 5, 33 (2023)
Boosting is an ensemble learning method that converts a weak learner into a strong learner in the PAC learning framework. Freund and Schapire designed the Godel prize-winning algorithm named AdaBoost that can boost learners, which output binary hypot
Externí odkaz:
http://arxiv.org/abs/2110.12793
Representation learning using network embedding has received tremendous attention due to its efficacy to solve downstream tasks. Popular embedding methods (such as deepwalk, node2vec, LINE) are based on a neural architecture, thus unable to scale on
Externí odkaz:
http://arxiv.org/abs/2109.04206
Autor:
Bera, Debajyoti, Sapv, Tharrmashastha
We introduce the problem of determining if the mode of the output distribution of a quantum circuit (given as a black-box) is larger than a given threshold, named HighDist, and a similar problem based on the absolute values of the amplitudes, named H
Externí odkaz:
http://arxiv.org/abs/2103.09033
Autor:
Bera, Debajyoti, Sapv, Tharrmashastha
Publikováno v:
ACM Transactions on Quantum Computing 2, 2, Article 5 (July 2021)
Non-linearity of a Boolean function indicates how far it is from any linear function. Despite there being several strong results about identifying a linear function and distinguishing one from a sufficiently non-linear function, we found a surprising
Externí odkaz:
http://arxiv.org/abs/2103.07934