Pinning down a precise understanding of information flow within physical interactions remains a central concern to fields such as stochastic thermodynamics and quantum information science. In both spheres a careful accounting of bits (or qubits) enables a deeper understanding of the physical nature of information. In this work we propose a measure of information flow as a saddle-point solution of the mutual information. This approach places a lower bound on the channel capacity between a particle and an interacting environment. The measure is given by P_{0}/2E_{0} in nats/sec, with P_{0} the average power flux between the particle and its environment, and E_{0} the initial average energy of the particle, all computed in a frame where the particle has zero average momentum. We use a communication theory lens to suggest an associated channel analogy, in which this bound is interpreted as a signal-to-noise ratio. We find that this measure can also quantify early-time information flow for a particle interacting with a thermal bath.
Pinning down a precise understanding of information flow within physical interactions remains a central concern to fields such as stochastic thermodynamics and quantum information science. In both spheres a careful accounting of bits (or qubits) enables a deeper understanding of the physical nature of information. In this work we propose a measure of information flow as a saddle-point solution of the mutual information. This approach places a lower bound on the channel capacity between a particle and an interacting environment. The measure is given by P_{0}/2E_{0} in nats/sec, with P_{0} the average power flux between the particle and its environment, and E_{0} the initial average energy of the particle, all computed in a frame where the particle has zero average momentum. We use a communication theory lens to suggest an associated channel analogy, in which this bound is interpreted as a signal-to-noise ratio. We find that this measure can also quantify early-time information flow for a particle interacting with a thermal bath.
Abstract Information stored in quantum states naturally decays under noise. Many earlier works have quantified and demonstrated lower bounds on decay rates of quantum relative entropy, showing exponential decay in a wide variety of contexts. Here we study the converse question: are there uniform upper bounds on the ratio of post-noise to initial information when noise is sufficiently weak? In several scenarios, including when a quantum channel induces decay of relative entropy to its own fixed point, we find such multiplicative converse bounds. However, there are other cases in which the ratio can fall by an arbitrary extent. Even for simple noise such as qubit dephasing or depolarizing, mutual information may fall by an unbounded factor under arbitrarily weak noise. As an application, we find families of channels with non-zero private capacity despite arbitrarily high probability of transmitting an arbitrarily good copy of the input to the environment.
We will begin with a simple type of stationary stochastic1 systems of quantum physics using them within a frame of the Shannon Information Theory and Thermodynamics but starting with their algebraic representation. Based on this algebraic description a model of information transmission in those systems by defining the Shannon information will be stated in terms of variable about the system state. Measuring on these system is then defined as a spectral decomposition of measured quantities operators. The information capacity formulas, now of the narrow-band nature, are derived consequently, for the simple system governed by the Bose–Einstein (B–E) Law [bosonic (photonic) channel] and that one governed by the Fermi-Dirac (F–D) Law [fermionic (electron) channel]. The not-zero value for the average input energy needed for information transmission existence in F–D systems is stated [11, 12].
I derive a universal upper bound on the capacity of any communication channel between two distant systems. The Holevo quantity, and hence the mutual information, is at most of order EΔt/ℏ, where E is the average energy of the signal, and Δt is the amount of time for which detectors operate. The bound does not depend on the size or mass of the emitting and receiving systems, nor on the nature of the signal. No restrictions on preparing and processing the signal are imposed. As an example, I consider the encoding of information in the transverse or angular position of a signal emitted and received by systems of arbitrarily large cross section. In the limit of a large message space, quantum effects become important even if individual signals are classical, and the bound is upheld.
Characterizing information-carrying capacities of bosonic communication channels is of significant practical interest. For thermal noise channels, using coherent (product) states yields an achievable rate for classical communication which is conjectured to be optimal. However, it is not known whether coding strategies using entanglement may perform better. Here we discuss upper bounds on classical capacities of thermal noise channels. These imply that coherent-state coding is close to optimal. Our main tool is a quantum analog of the entropy power inequality introduced Shannon. It gives a lower bound on the output von Neumann entropy when two independent signals combine at a beamsplitter.
Autonomous quantum thermal machines have recently been proposed as physics-based computing substrates where logical inputs and outputs are encoded in temperature gradients. While such ``thermodynamic neurons'' exhibit a clear trade-off between computational fidelity and heat dissipation, the fundamental information-theoretic limits of temperature-encoded computation remain uncharacterized. Here, we derive rigorous bounds linking average error probability, channel capacity, and entropy production for finite-capacity thermal reservoirs operating far from equilibrium. We prove that the minimal dissipation required to achieve a target average error probability ⟨ξ⟩ diverges as ⟨ξ⟩ approaches a fundamental minimum error floor $\epsMin$ imposed by finite-reservoir thermal fluctuations. We further establish a thermodynamic channel capacity that saturates at high dissipation, and quantify the minimal dissipation required for cascaded networks to maintain target fidelity. We demonstrate that the required dissipation grows linearly O(L) with network depth for shallow networks, but diverges as the network depth L approaches a fundamental maximum limit $L_{\max} = \epsNet/\epsMin$ imposed by the minimum error floor. Furthermore, under strong noise amplification conditions, the required dissipation can grow up to O(L3). Our framework bridges stochastic thermodynamics, finite-time information theory, and autonomous computation, providing rigorous design principles for energy-efficient analog thermodynamic hardware.
The transport properties of condensed matter are traditionally described in terms of charge currents, spin currents, and heat currents, each associated with a distinct flux of physical quantities. In this work we introduce a new concept, the internal information flow, denoted by J_I, which quantifies the rate of change of quantum information residing within a material system. Unlike conventional currents, J_I is not directly associated with a conserved charge or energy but rather with the redistribution of correlations and entanglement among the degrees of freedom. We formulate J_I in a rigorous operator framework, show that it obeys continuity-like equations, and illustrate its distinct physical consequences through several model systems. We further discuss possible measurement schemes based on quantum tomography and weak measurement techniques, and highlight the potential of internal information flow to bridge quantum information science and materials physics. The theoretical framework presented here opens a new avenue for exploring quantum thermodynamics, quantum phase transitions, and ultimately paves the way for a deeper understanding of the interplay between information and matter.