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arXiv:2205.13184

Sequential measurements, TQFTs, and TQNNs

Chris Fields, James F. Glazebrook, Antonino Marcianò

Published May 26, 2022• 0 citations
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Abstract

We introduce novel methods for implementing generic quantum information within a scale-free architecture. For a given observable system, we show how observational outcomes are taken to be finite bit strings induced by measurement operators derived from a holographic screen bounding the system. In this framework, measurements of identified systems with respect to defined reference frames are represented by semantically-regulated information flows through distributed systems of finite sets of binary-valued Barwise-Seligman classifiers. Specifically, we construct a functor from the category of cone-cocone diagrams (CCCDs) over finite sets of classifiers, to the category of finite cobordisms of Hilbert spaces. We show that finite CCCDs provide a generic representation of finite quantum reference frames (QRFs). Hence the constructed functor shows how sequential finite measurements can induce TQFTs. The only requirement is that each measurement in a sequence, by itself, satisfies Bayesian coherence, hence that the probabilities it assigns satisfy the Kolmogorov axioms. We extend the analysis so develop topological quantum neural networks (TQNNs), which enable machine learning with functorial evolution of quantum neural 2-complexes (TQN2Cs) governed by TQFTs amplitudes, and resort to the Atiyah-Singer theorems in order to classify topological data processed by TQN2Cs. We then comment about the quiver representation of CCCDs and generalized spin-networks, a basis of the Hilbert spaces of both TQNNs and TQFTs. We finally review potential implementations of this framework in solid state physics and suggest applications to quantum simulation and biological information processing.

DOI
10.48550/arxiv.2205.13184

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We introduce novel methods for implementing generic quantum information within a scale-free architecture. For a given observable system, we show how observational outcomes are taken to be finite bit strings induced by measurement operators derived from a holographic screen bounding the system. In this framework, measurements of identified systems with respect to defined reference frames are represented by semantically-regulated information flows through distributed systems of finite sets of binary-valued Barwise-Seligman classifiers. Specifically, we construct a functor from the category of cone-cocone diagrams (CCCDs) over finite sets of classifiers, to the category of finite cobordisms of Hilbert spaces. We show that finite CCCDs provide a generic representation of finite quantum reference frames (QRFs). Hence the constructed functor shows how sequential finite measurements can induce TQFTs. The only requirement is that each measurement in a sequence, by itself, satisfies Bayesian coherence, hence that the probabilities it assigns satisfy the Kolmogorov axioms. We extend the analysis so develop topological quantum neural networks (TQNNs), which enable machine learning with functorial evolution of quantum neural 2-complexes (TQN2Cs) governed by TQFTs amplitudes, and resort to the Atiyah-Singer theorems in order to classify topological data processed by TQN2Cs. We then comment about the quiver representation of CCCDs and generalized spin-networks, a basis of the Hilbert spaces of both TQNNs and TQFTs. We finally review potential implementations of this framework in solid state physics and suggest applications to quantum simulation and biological information processing.
Abstract We introduce novel methods for implementing generic quantum information within a scale‐free architecture. For a given observable system, we show how observational outcomes are taken to be finite bit strings induced by measurement operators derived from a holographic screen bounding the system. In this framework, measurements of identified systems with respect to defined reference frames are represented by semantically‐regulated information flows through distributed systems of finite sets of binary‐valued Barwise‐Seligman classifiers. Specifically, we construct a functor from the category of cone‐cocone diagrams (CCCDs) over finite sets of classifiers, to the category of finite cobordisms of Hilbert spaces. We show that finite CCCDs provide a generic representation of finite quantum reference frames (QRFs). Hence the constructed functor shows how sequential finite measurements can induce TQFTs. The only requirement is that each measurement in a sequence, by itself, satisfies Bayesian coherence, hence that the probabilities it assigns satisfy the Kolmogorov axioms. We extend the analysis too develop topological quantum neural networks (TQNNs), which enable machine learning with functorial evolution of quantum neural 2‐complexes (TQN2Cs) governed by TQFTs amplitudes, and resort to the Atiyah‐Singer theorems in order to classify topological data processed by TQN2Cs. We then comment about the quiver representation of CCCDs and generalized spin‐networks, a basis of the Hilbert spaces of both TQNNs and TQFTs. We finally review potential implementations of this framework in solid state physics and suggest applications to quantum simulation and biological information processing.
Quantum neural networks generalize classical artificial neural networks into the quantum domain. They are formulated as parameterized quantum circuits which are optimized by measuring and minimizing a suitably chosen loss function. The core challenge in understanding, implementing and ultimately using quantum neural networks is that they represent many-body systems with an exponentially large Hilbert space, in combination with a large parameter search space. Moreover, noise -- which is inherent to any quantum measurement -- sets practical limits for the estimation of training loss. Here, we study physics-informed large-scale quantum neural networks that are trained through a finite number of noisy loss function measurements. We show that this architecture permits the construction of nontrivial decision boundaries that enable the classification of quantum states through measuring an order parameter. Our approach can directly process quantum data that is output from quantum simulators and computers and is well suited for implementation on current hardware. Moreover, owed to a close link between the neural network dynamics and the evolution of Markovian open many-body quantum systems, one may expect a certain robustness to noise, which is ubiquitous in the current NISQ era.
Quantum computing is captured in the formalism of the monoidal subcategory of 𝐕 𝐞 𝐜 𝐭 ℂ generated by ℂ 2 — in particular, quantum circuits are diagrams in 𝐕 𝐞 𝐜 𝐭 ℂ — while topological quantum field theories , in the sense of Atiyah, are diagrams in 𝐕 𝐞 𝐜 𝐭 ℂ indexed by cobordisms. We initiate a program to formalize this connection. In doing so, we equip cobordisms with machinery for producing linear maps by parallel transport along curves under a connection and then assemble these structures into a higher category. Finite-dimensional complex vector spaces and linear maps between them are given a suitable higher categorical structure which we call 𝔽 ⁡ 𝐕 𝐞 𝐜 𝐭 ℂ . We realize quantum circuits as images of cobordisms under monoidal functors from these modified cobordisms to 𝔽 ⁡ 𝐕 𝐞 𝐜 𝐭 ℂ , which are computed by taking parallel transports of vectors and then combining the results in a pattern encoded in the domain category.
Monitored quantum circuits host a rich variety of exotic non-equilibrium phases. Among the most representative examples are measurement-induced phase transitions between distinct area-law entangled states. However, because these transitions are characterized by specific entanglement quantities such as mutual information or topological entanglement entropy that are nonlinear functionals of the density matrix, their experimental observation requires multiple identical quantum trajectories via post-selection, which becomes exponentially unfeasible for large systems. Here, we leverage modern machine learning tools to address this challenge. We devise a neural network architecture combining a convolutional neural network with an attention mechanism, and use raw measurement outcomes directly as input to classify trivial, long-range entangled, and symmetry-protected topological phases. We show that the system's relaxation to a steady-state phase manifests as a sharp convergence in the classifier's accuracy, entirely bypassing the need for quantum state reconstruction. We systematically study the performance of our network as a function of sample size, input data, spatial and temporal constraints, and system size scalability. Our results demonstrate that this approach is robust and post-selection free, offering a practical pathway for experimentally probing measurement-induced phases.
In this short note, we propose a generalization of Atiyah type TQFTs from pure states to mixed states in the sense that the Hilbert space of pure states associated to a space manifold is replaced by a quantum coherent space related to density matrices. Atiyah type TQFT is a symmetric monoidal functor from the Bord category of manifolds to the category Vec of finite dimensional vector spaces. In this paper, we define mixed-state TQFTs by replacing the target category Vec by QCS--the category of quantum coherent spaces, then a mixed-state TQFT is simply a symmetric monoidal functor from Bord to QCS. We also discuss how to construct interesting examples from subsystem quantum error correction codes beyond the trivial ones--all Atiyah type TQFTs.
This manuscript presents a synthesis of the quantum branch of the Finite Relational Closure Framework (FRCF). The central claim is that finite-dimensional Hilbert-space quantum kinematics can be constructed from finite measurement primitives rather than assumed as an independent foundation. In standard presentations of quantum mechanics, a Hilbert space is usually introduced first, with measurement represented afterward by operators on that space. FRCF reverses this explanatory order. It begins with finite contexts, admissible relational assignments, finite-resolution equivalence, and measurement partitions. From these structures, the finite-resolution admissible classes Σε(C)=Σ(C)/∼C,ε\Sigma_{\varepsilon}(C)=\Sigma(C)/{\sim_{C,\varepsilon}}Σε​(C)=Σ(C)/∼C,ε​ are obtained. The associated complex contribution space HC,ε=CΣε(C)\mathcal{H}_{C,\varepsilon}=\mathbb{C}^{\Sigma_{\varepsilon}(C)}HC,ε​=CΣε​(C), equipped with its finite inner product, is a finite-dimensional Hilbert space. The manuscript shows how finite measurement maps induce orthogonal decompositions, projection operators, Born-type probabilities from squared norms, and measurement update as conditioning on recorded finite outcomes. It also distinguishes this finite Hilbert-space kinematics from the separate generator problem governing contribution transport between contexts. The construction does not derive all of quantum mechanics. It does not determine a Hamiltonian, the Schrödinger equation, quantum field theory, or a unique physical generator. Its contribution is kinematic and foundational: it identifies Hilbert-space structure as an effective representation of finite admissible measurement distinctions within FRCF.