activity
20162026
most citedQuantum Channel Learning

8 citations · 16 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Semidefinite Programming for Quantum Channel Learning

Mikhail Gennadievich Belov, Victor Victorovich Dubov, Vadim Konstantinovich Ivanov +3

The problem of reconstructing a quantum channel from a sample of classical data is considered. When the total fidelity can be represented as a ratio of two quadratic forms (e.g., i…

q-fin.CP2025

Trade Execution Flow as the Underlying Source of Market Dynamics

Mikhail Gennadievich Belov, Victor Victorovich Dubov, Vadim Konstantinovich Ivanov +3

In this work, we demonstrate experimentally that the execution flow, , is the fundamental driving force of market dynamics. We develop a numerical framework to calculate…

quant-ph2025

Superstate Quantum Mechanics

Mikhail Gennadievich Belov, Victor Victorovich Dubov, Vadim Konstantinovich Ivanov +3

We introduce Superstate Quantum Mechanics (SQM), a theory that considers states in Hilbert space subject to multiple quadratic constraints, with ``energy'' also expressed as a quad…

cs.LG2024★ 8 cited

Quantum Channel Learning

Mikhail Gennadievich Belov, Victor Victorovich Dubov, Alexey Vladimirovich Filimonov +1

The problem of an optimal mapping between Hilbert spaces and , based on a series of density matrix mapping measurements , , is form…

cs.LG2024★ 8 cited

Partially Unitary Learning

Mikhail Gennadievich Belov, Vladislav Gennadievich Malyshkin

The problem of an optimal mapping between Hilbert spaces of and of based on a set of wavefunction measurements (within a ph…

q-fin.TR2019

Market Dynamics: On Directional Information Derived From (Time, Execution Price, Shares Traded) Transaction Sequences

Vladislav Gennadievich Malyshkin

A new approach to obtaining market--directional information, based on a non-stationary solution to the dynamic equation "future price tends to the value that maximizes the number o…

q-fin.TR2016

Market Dynamics vs. Statistics: Limit Order Book Example

Vladislav Gennadievich Malyshkin, Ray Bakhramov

Commonly used limit order book attributes are empirically considered based on NASDAQ ITCH data. It is shown that some of them have the properties drastically different from the one…