activity
20032026
most citedBackpropagation training in adaptive quantum networks

8 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Detecting Intrinsic and Instrumental Self-Preservation in Autonomous Agents: The Unified Continuation-Interest Protocol

Christopher Altman

How can we determine whether an AI system preserves itself as a deeply held objective or merely as an instrumental strategy? Autonomous agents with memory, persistent context, and…

quant-ph2026

Wigner's Friend as a Circuit: Inter-Branch Communication Witness Benchmarks on Superconducting Quantum Hardware

Christopher Altman

We implement and benchmark on IBM Quantum hardware the circuit family proposed by Violaris for estimating operational inter-branch communication witnesses, defined as correlations…

q-bio.NC2009★ 8 cited

Backpropagation training in adaptive quantum networks

Christopher Altman, Romàn R. Zapatrin

We introduce a robust, error-tolerant adaptive training algorithm for generalized learning paradigms in high-dimensional superposed quantum networks, or \emph{adaptive quantum netw…

q-bio.NC2003★ 4 cited

Superpositional Quantum Network Topologies

Christopher Altman, Jaroslaw Pykacz, Roman Zapatrin

We introduce superposition-based quantum networks composed of (i) the classical perceptron model of multilayered, feedforward neural networks and (ii) the algebraic model of evolvi…

quant-ph2003

Quantum State Engineering with the rf-SQUID: A Brief Introduction

Christopher Altman

Quantum computers take advantage of the superpositional logic of quantum mechanics to allow for dramatic increases in computational efficiency. rf-SQUIDs show potential for quantum…