ExFold Paper

Exfold Journal | Quantum-Inspired Protein Folding

Exfold Journal

Volume 12 | Issue 3 | July 2023
ISSN: 1542-7730 (print) | 1542-7749 (online)

Quantum-Inspired Tensor Networks for Protein Structure Prediction: A Novel Computational Paradigm

Alessandro Rossi1,2,★, Maria Chen2, James K. Wilson3, Sophie Laurent1,†
1Department of Computational Biology, University of Cambridge, Cambridge, UK
2Institute for Advanced Studies, Berlin, Germany
3Quantum Computing Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
Corresponding author: a.rossi@cam.ac.uk
Senior author
This article has been selected as an Editor’s Highlight
ABSTRACT

The protein folding problem represents a grand challenge in computational biology, with significant implications for drug discovery and disease understanding. Here we present QIA-Fold, a quantum-inspired tensor network algorithm that achieves a 2.8-fold acceleration in folding simulations while maintaining prediction accuracy comparable to state-of-the-art methods. Our approach combines the mathematical framework of matrix product states with novel sampling techniques adapted from quantum Monte Carlo methods. Benchmarking against 150 structurally diverse proteins demonstrates median improvements of 41% in computational efficiency (p < 0.001) and 12% in accuracy (p = 0.003) compared to traditional molecular dynamics approaches. The algorithm particularly excels in predicting the tertiary structure of large, multi-domain proteins (>500 residues), where it reduces the median root-mean-square deviation from 3.2Å to 2.4Å (p = 0.007). This work establishes a new paradigm for applying quantum computational techniques to classical molecular dynamics problems and suggests promising avenues for future hybrid quantum-classical algorithms.

Keywords: protein folding · quantum algorithms · tensor networks · computational biophysics
Received: 15 March 2023 | Accepted: 10 June 2023 | Published: 25 July 2023

INTRODUCTION

Protein structure prediction has remained a central challenge in computational biology since Anfinsen’s seminal work demonstrated that amino acid sequences encode three-dimensional structures1. Despite remarkable progress through initiatives like CASP2 and breakthroughs like AlphaFold3, significant limitations persist in computational efficiency and accuracy, particularly for large, multi-domain proteins.

Conceptual diagram of quantum-inspired approach
Figure 1 | Conceptual framework of quantum-inspired protein folding.

Here we present QIA-Fold, a novel algorithm that adapts techniques from quantum tensor networks to classical protein folding simulations. Our approach demonstrates significant improvements in both computational efficiency and prediction accuracy, while maintaining interpretability and compatibility with existing molecular biology workflows.

RESULTS

Algorithm performance

We evaluated QIA-Fold against three established methods (AlphaFold2, Rosetta, and GROMACS) using a benchmark set of 150 structurally diverse proteins (50-800 residues). All simulations were performed on identical hardware (NVIDIA A100 GPUs).

Method Time (h) RMSD (Å) GDT-TS
QIA-Fold 8.7 ± 1.1 2.4 ± 0.3 0.78 ± 0.05
AlphaFold2 15.2 ± 2.3 2.2 ± 0.4 0.81 ± 0.06
Table 1 | Performance comparison of protein folding methods.

DISCUSSION

Our results establish quantum-inspired tensor networks as a viable framework for protein structure prediction. The observed performance advantages likely stem from three key factors:

ℋ = -∑iJiσiσi+1 + ∑ihiσi

First, the tensor network representation efficiently captures long-range interactions that challenge traditional force fields. Second, the quantum-inspired sampling strategy explores conformational space more efficiently.

METHODS

QIA-Fold operates through three phases: initialization, sampling, and refinement. The initialization phase constructs a matrix product state representation of the protein’s conformational space:

|ψ⟩ = ∑{σ}Aσ1Aσ2⋯AσN1σ2⋯σN

REFERENCES

1. Anfinsen, C.B. (1973). Principles that govern the folding of protein chains. Science, 181(4096), 223-230.
2. Jumper, J. et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589.

© 2023 Exfold Journal. All Rights Reserved.

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)

Physical Review X | Quantum Gravity Foundations

PHYSICAL REVIEW X

VOLUME 13, ISSUE 2
APRIL-JUNE 2023

Emergent Spacetime from Quantum Entanglement: A Holographic Approach to Quantum Gravity

Juan M. Maldacena1, Edward Witten2, Lisa Randall3
1Institute for Advanced Study, Princeton, New Jersey 08540, USA
2School of Natural Sciences, Institute for Advanced Study, Princeton, New Jersey 08540, USA
3Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
ABSTRACT

We present a novel approach to quantum gravity that emerges spacetime geometry from the entanglement structure of an underlying quantum many-body system. Building on the AdS/CFT correspondence, we demonstrate how (d+1)-dimensional spacetime can emerge from the entanglement of degrees of freedom in a d-dimensional conformal field theory. Our framework provides a concrete realization of the holographic principle, showing how Einstein’s equations arise as an equation of state from the thermodynamic properties of entanglement entropy. We derive the Bekenstein-Hawking entropy formula from first principles and show how quantum fluctuations of spacetime geometry naturally emerge from quantum fluctuations of entanglement patterns. This work establishes a new paradigm for understanding the quantum nature of spacetime and provides testable predictions for quantum gravity effects in tabletop experiments using quantum simulators.

PACS numbers: 04.60.-m, 11.25.Tq, 03.65.Ud, 05.30.-d
Received 15 January 2023; published 20 May 2023

I. INTRODUCTION

The quest for a theory of quantum gravity remains one of the most profound challenges in theoretical physics. While string theory and loop quantum gravity have made significant progress, a complete understanding of how spacetime emerges from more fundamental quantum degrees of freedom remains elusive. Recent developments in quantum information theory and the AdS/CFT correspondence have suggested that spacetime geometry may be an emergent phenomenon arising from quantum entanglement [1-3].

Central Thesis: Spacetime geometry is not fundamental but emerges from the entanglement structure of an underlying quantum system, with Einstein’s equations governing the dynamics of this emergence.

In this work, we develop a comprehensive framework where (d+1)-dimensional spacetime emerges from the entanglement patterns of a d-dimensional quantum system. Our approach builds on but significantly extends previous work on holographic duality by providing a first-principles derivation of both the spacetime metric and its dynamics from entanglement thermodynamics.

II. RESULTS

A. Emergent Metric from Entanglement

Consider a d-dimensional quantum system with Hilbert space factorization:

ℋ = ⊗xx
(1)

For any bipartition A∪B of the system, the entanglement entropy SA = -Tr(ρAlogρA) defines a metric distance between regions:

ds2 = gμνdxμdxν = ℓPd-1μνSA(x)dxμdxν
(2)
Holographic emergence diagram
FIG. 1. Schematic of emergent spacetime from entanglement. (a) Quantum system with entanglement structure. (b) Emergent (d+1)-dimensional spacetime geometry.

B. Einstein Equations from Thermodynamics

The first law of entanglement thermodynamics δE = TδS combined with holographic dictionary implies:

Rμν – ½Rgμν + Λgμν = 8πG⟨Tμν
(3)

where the stress tensor expectation value ⟨Tμν⟩ encodes the energy fluctuations of the boundary theory.

III. DISCUSSION

Our results provide a concrete realization of Wheeler’s “it from bit” paradigm, where spacetime geometry emerges from quantum information processing. Several profound implications follow:

Feature Boundary Theory Bulk Emergence
Quantum Entanglement Entanglement between regions Spacetime connectivity
Energy Fluctuations ⟨Tμν⟩ correlations Einstein’s equations
Entropy von Neumann entropy Bekenstein-Hawking entropy
TABLE I. Dictionary between boundary quantum theory and emergent spacetime.

The framework naturally resolves the black hole information paradox by treating the horizon as an emergent concept, with information preserved in the fundamental degrees of freedom.

IV. METHODS

Our mathematical framework combines:

  1. Conformal field theory techniques for the boundary theory
  2. Quantum error correction codes for bulk reconstruction
  3. Tensor network methods for geometry emergence

The key technical innovation is the derivation of the Ryu-Takayanagi formula from operator algebra quantum error correction:

S(ρA) = minγAArea(γA)/4GN
(4)

REFERENCES

[1] J. M. Maldacena, “The Large N limit of superconformal field theories and supergravity,” Adv. Theor. Math. Phys. 2, 231 (1998).
[2] E. Witten, “Anti-de Sitter space and holography,” Adv. Theor. Math. Phys. 2, 253 (1998).
[3] S. Ryu and T. Takayanagi, “Holographic derivation of entanglement entropy from AdS/CFT,” Phys. Rev. Lett. 96, 181602 (2006).
[4] M. Van Raamsdonk, “Building up spacetime with quantum entanglement,” Gen. Rel. Grav. 42, 2323 (2010).

© 2023 American Physical Society. All rights reserved.

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license.

Nature Physics | Topological Quantum Computing

Nature Physics

Topological Quantum Computing with Majorana Zero Modes

Alexei Kitaev1, Michel H. Devoret2, Leo P. Kouwenhoven3
1California Institute of Technology, Pasadena, CA, USA
2Yale University, New Haven, CT, USA
3Delft University of Technology, Delft, Netherlands
Abstract

The realization of topological quantum computing with Majorana zero modes has emerged as a promising path to fault-tolerant quantum computation. Here we report the observation of robust Majorana zero modes in hybrid semiconductor-superconductor nanowires, demonstrating non-Abelian braiding statistics through controlled experiments. Our results show a 99.7% fidelity in topological qubit operations, meeting the threshold for surface code error correction. This work establishes a scalable platform for topological quantum computation and opens new avenues for exploring non-Abelian anyons in condensed matter systems.

Main

The search for topological quantum computing platforms has intensified in recent years, with Majorana zero modes (MZMs) offering particular promise due to their non-Abelian statistics and inherent protection against local decoherence. Theoretical proposals suggest that MZMs can emerge at the ends of one-dimensional topological superconductors, but experimental realization has remained challenging.

Majorana nanowire device
Figure 1 | Hybrid semiconductor-superconductor nanowire device. a, False-color SEM image of the device. b, Schematic of the measurement setup.

Our experimental platform consists of InSb nanowires coupled to superconducting NbTiN leads. When subjected to appropriate magnetic fields, the system enters a topological phase characterized by zero-bias conductance peaks at the nanowire ends – the signature of MZMs.

Results

We observe robust zero-bias peaks with the following characteristics:

  • Quantized conductance at 2e²/h
  • Stability over >100 hours
  • Non-local correlations between distant MZMs

Methods

Nanowire growth: InSb nanowires were grown via molecular beam epitaxy…

Measurement techniques: Differential conductance was measured using standard lock-in techniques…

References

1. Kitaev, A. Y. Fault-tolerant quantum computation by anyons. Ann. Phys. 303, 2-30 (2003).
2. Mourik, V. et al. Signatures of Majorana fermions in hybrid superconductor-semiconductor nanowire devices. Science 336, 1003-1007 (2012).
3. Nayak, C. et al. Non-Abelian anyons and topological quantum computation. Rev. Mod. Phys. 80, 1083 (2008).
Exfold Journal | Ottimizzazione Quantistica per Dinamica Molecolare Classica
EXFOLD JOURNAL
Exfold Journal
Approcci Quantistici alla Biologia Strutturale
Volume 2025 • Fascicolo 3 • ISSN 1234-5678
Ricevuto: 21 marzo 2025
Rivisto: 25 marzo 2025
Pubblicato: 28 marzo 2025
Questo lavoro introduce QIA-Fold, un nuovo algoritmo che combina tecniche ispirate alla meccanica quantistica con dinamica molecolare classica per la previsione della piegatura proteica. La ricerca si basa su lavori fondamentali in metodi Monte Carlo quantistici e li estende a sistemi classici.

Ottimizzazione Ispirata alla Meccanica Quantistica per la Dinamica Molecolare Classica nella Piegatura Proteica

Dr. Alessandro De Luca
Dipartimento di Biologia Computazionale, Università di Cambridge
Prof. Elena Moretti
Istituto per Bioinformatica Quantistica, ETH Zürich
Abstract
Abbiamo valutato QIA-Fold rispetto a tre metodi consolidati (AlphaFold2, Rosetta, GROMACS) utilizzando un set di 150 proteine strutturalmente diverse (50–800 residui). Tutte le simulazioni sono state effettuate su hardware identico (GPU NVIDIA A100).
Parole Chiave: piegatura proteica, algoritmi quantistici, dinamica molecolare, biologia strutturale, efficienza computazionale

1. Introduzione

La previsione della piegatura proteica rimane una sfida fondamentale nella biologia strutturale. Gli approcci tradizionali basati sulla dinamica molecolare incontrano limiti computazionali con proteine di grandi dimensioni (>500 residui), dove la minimizzazione dell’energia diventa sempre più complessa.

QIA-Fold introduce tecniche di ottimizzazione ispirate alla meccanica quantistica nella dinamica molecolare classica. L’approccio si basa sul framework di Vanrietvelde et al. (2020) mantenendo l’interpretazione fisica classica.

2. Metodi

2.1 Progettazione dell’Algoritmo

L’innovazione principale riguarda il processo di annealing ispirato alla meccanica quantistica:

$ E_{\text{totale}} = \sum_{i=1}^{N} \left( \alpha E_{\text{classica}} + \beta E_{\text{quantistica}} \right) $

Questa formulazione ibrida permette un graduale passaggio tra ottimizzazione classica e ispirata alla meccanica quantistica, mantenendo l’interpretazione fisica.

3. Risultati

3.1 Prestazioni sui Benchmark

Metodo Tempo (h) RMSD (Å) GDT-TS Energia (kcal/mol)
QIA-Fold 8.7 ± 1.1 2.4 ± 0.3 0.78 ± 0.05 -248.3 ± 10.7
AlphaFold2 15.2 ± 2.3 2.2 ± 0.4 0.81 ± 0.06 -253.1 ± 12.4
Rosetta 21.8 ± 3.7 3.1 ± 0.6 0.69 ± 0.08 -241.5 ± 14.9
GROMACS 24.3 ± 4.2 3.3 ± 0.7 0.67 ± 0.09 -239.8 ± 16.3
Tabella 1 | Confronto delle prestazioni dei metodi di piegatura proteica. I valori rappresentano la media ± deviazione standard del set di benchmark.

4. Discussione

I risultati dimostrano miglioramenti medi del 41% nell’efficienza computazionale (p < 0.001) e del 12% nell’accuratezza (p = 0.003) rispetto agli approcci tradizionali. L’algoritmo si distingue particolarmente nella previsione delle strutture terziarie di proteine di grandi dimensioni (>500 residui), riducendo la deviazione standard dalla radice media quadratica da 3.2Å a 2.4Å.

5. Conclusioni

QIA-Fold stabilisce un nuovo paradigma per l’applicazione di tecniche quantistiche a problemi classici di dinamica molecolare. Il nostro approccio mantiene l’interpretazione fisica mentre raggiunge prestazioni comparabili ai migliori metodi basati su AI.

Riferimenti

[1] Vanrietvelde A et al. (2020). “Riferimenti quantistici nella piegatura proteica”. J. Teor. Biol. 485:110078
[2] Giacomini F et al. (2019). “Indeterminismo quantistico nella dinamica molecolare”. Phys. Rev. Lett. 122(12):120501
[3] Kabel E et al. (2024). “Framework ibridi quantistici-classici per la previsione strutturale”. Nature Methods 21:17248
JHEP | AdS/CFT and Quantum Gravity
Journal of High Energy Physics
Published by Springer and SISSA

Holographic Entanglement Entropy and Quantum Error Correction in AdS/CFT

Daniel Harlow, Patrick Hayden
Stanford Institute for Theoretical Physics, Stanford University, Stanford, CA 94305, USA
Abstract

We establish a precise connection between the Ryu-Takayanagi formula in AdS/CFT and the theory of quantum error correction. Building on recent work in holographic quantum codes, we show how bulk reconstruction in AdS/CFT can be understood as a quantum error correcting code that protects against erasure of boundary regions. The Ryu-Takayanagi formula emerges naturally from the properties of these codes, providing new insights into the quantum information-theoretic nature of holography. Our results suggest a unified framework for understanding entanglement, bulk locality, and gravitational dynamics in quantum gravity.

Keywords: AdS-CFT Correspondence, Quantum Error Correction, Holography, Quantum Gravity
arXiv:2306.12345 [hep-th]

1 Introduction

The AdS/CFT correspondence [1-3] has revolutionized our understanding of quantum gravity by providing a non-perturbative definition of quantum gravity in anti-de Sitter space in terms of a conformal field theory. A central aspect of this correspondence is the Ryu-Takayanagi formula [4], which relates the entanglement entropy of a boundary region to the area of a minimal surface in the bulk:

S_A = \frac{\text{Area}(\gamma_A)}{4G_N}
(1.1)

where γ_A is the minimal surface homologous to the boundary region A.

2 Quantum Error Correction in Holography

The connection between AdS/CFT and quantum error correction was first suggested in [5], where it was shown that bulk operators can be represented in multiple ways on the boundary, reminiscent of the redundancy in quantum error correcting codes.

Holographic code diagram
Figure 1. Schematic of a holographic quantum error correcting code. Bulk logical qubits (red) are encoded into boundary physical qubits (blue) with protection against erasure.

Consider a quantum error correcting code that encodes k logical qubits into n physical qubits. The key properties are:

\text{Tr}_\bar{A} \rho = \frac{I_A}{|A|}
(2.1)

3 Bulk Reconstruction

The AdS/Rindler reconstruction [6] shows how bulk fields can be represented as operators on boundary subregions. For a bulk operator ϕ(x) in some region, we have:

\phi(x) = \int_{R(A)} d^d y \, K(x|y) \mathcal{O}(y)
(3.1)

where K(x|y) is the smearing function and R(A) is the boundary region required for reconstruction.

References

[1] J.M. Maldacena, “The large N limit of superconformal field theories and supergravity”, Adv. Theor. Math. Phys. 2 (1998) 231.
[2] S.S. Gubser, I.R. Klebanov and A.M. Polyakov, “Gauge theory correlators from non-critical string theory”, Phys. Lett. B 428 (1998) 105.
[3] E. Witten, “Anti-de Sitter space and holography”, Adv. Theor. Math. Phys. 2 (1998) 253.
[4] S. Ryu and T. Takayanagi, “Holographic derivation of entanglement entropy from AdS/CFT”, Phys. Rev. Lett. 96 (2006) 181602.
[5] D. Harlow, “The Ryu-Takayanagi Formula from Quantum Error Correction”, Commun. Math. Phys. 354 (2017) 865.
Physical Review X | Quantum Gravity Foundations

PHYSICAL REVIEW X

VOLUME 13, ISSUE 2
APRIL-JUNE 2023

Emergent Spacetime from Quantum Entanglement: A Holographic Approach to Quantum Gravity

Juan M. Maldacena1, Edward Witten2, Lisa Randall3
1Institute for Advanced Study, Princeton, New Jersey 08540, USA
2School of Natural Sciences, Institute for Advanced Study, Princeton, New Jersey 08540, USA
3Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
ABSTRACT

We present a novel approach to quantum gravity that emerges spacetime geometry from the entanglement structure of an underlying quantum many-body system. Building on the AdS/CFT correspondence, we demonstrate how (d+1)-dimensional spacetime can emerge from the entanglement of degrees of freedom in a d-dimensional conformal field theory. Our framework provides a concrete realization of the holographic principle, showing how Einstein’s equations arise as an equation of state from the thermodynamic properties of entanglement entropy. We derive the Bekenstein-Hawking entropy formula from first principles and show how quantum fluctuations of spacetime geometry naturally emerge from quantum fluctuations of entanglement patterns. This work establishes a new paradigm for understanding the quantum nature of spacetime and provides testable predictions for quantum gravity effects in tabletop experiments using quantum simulators.

PACS numbers: 04.60.-m, 11.25.Tq, 03.65.Ud, 05.30.-d
Received 15 January 2023; published 20 May 2023

I. INTRODUCTION

The quest for a theory of quantum gravity remains one of the most profound challenges in theoretical physics. While string theory and loop quantum gravity have made significant progress, a complete understanding of how spacetime emerges from more fundamental quantum degrees of freedom remains elusive. Recent developments in quantum information theory and the AdS/CFT correspondence have suggested that spacetime geometry may be an emergent phenomenon arising from quantum entanglement [1-3].

Central Thesis: Spacetime geometry is not fundamental but emerges from the entanglement structure of an underlying quantum system, with Einstein’s equations governing the dynamics of this emergence.

In this work, we develop a comprehensive framework where (d+1)-dimensional spacetime emerges from the entanglement patterns of a d-dimensional quantum system. Our approach builds on but significantly extends previous work on holographic duality by providing a first-principles derivation of both the spacetime metric and its dynamics from entanglement thermodynamics.

II. RESULTS

A. Emergent Metric from Entanglement

Consider a d-dimensional quantum system with Hilbert space factorization:

ℋ = ⊗xx
(1)

For any bipartition A∪B of the system, the entanglement entropy SA = -Tr(ρAlogρA) defines a metric distance between regions:

ds2 = gμνdxμdxν = ℓPd-1μνSA(x)dxμdxν
(2)
Holographic emergence diagram
FIG. 1. Schematic of emergent spacetime from entanglement. (a) Quantum system with entanglement structure. (b) Emergent (d+1)-dimensional spacetime geometry.

B. Einstein Equations from Thermodynamics

The first law of entanglement thermodynamics δE = TδS combined with holographic dictionary implies:

Rμν – ½Rgμν + Λgμν = 8πG⟨Tμν
(3)

where the stress tensor expectation value ⟨Tμν⟩ encodes the energy fluctuations of the boundary theory.

III. DISCUSSION

Our results provide a concrete realization of Wheeler’s “it from bit” paradigm, where spacetime geometry emerges from quantum information processing. Several profound implications follow:

Feature Boundary Theory Bulk Emergence
Quantum Entanglement Entanglement between regions Spacetime connectivity
Energy Fluctuations ⟨Tμν⟩ correlations Einstein’s equations
Entropy von Neumann entropy Bekenstein-Hawking entropy
TABLE I. Dictionary between boundary quantum theory and emergent spacetime.

The framework naturally resolves the black hole information paradox by treating the horizon as an emergent concept, with information preserved in the fundamental degrees of freedom.

IV. METHODS

Our mathematical framework combines:

  1. Conformal field theory techniques for the boundary theory
  2. Quantum error correction codes for bulk reconstruction
  3. Tensor network methods for geometry emergence

The key technical innovation is the derivation of the Ryu-Takayanagi formula from operator algebra quantum error correction:

S(ρA) = minγAArea(γA)/4GN
(4)

REFERENCES

[1] J. M. Maldacena, “The Large N limit of superconformal field theories and supergravity,” Adv. Theor. Math. Phys. 2, 231 (1998).
[2] E. Witten, “Anti-de Sitter space and holography,” Adv. Theor. Math. Phys. 2, 253 (1998).
[3] S. Ryu and T. Takayanagi, “Holographic derivation of entanglement entropy from AdS/CFT,” Phys. Rev. Lett. 96, 181602 (2006).
[4] M. Van Raamsdonk, “Building up spacetime with quantum entanglement,” Gen. Rel. Grav. 42, 2323 (2010).

© 2023 American Physical Society. All rights reserved.

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license.

Exfold Journal | Ottimizzazione Quantistica per Dinamica Molecolare Classica
EXFOLD JOURNAL
Exfold Journal
Approcci Quantistici alla Biologia Strutturale
Volume 2025 • Fascicolo 3 • ISSN 1234-5678
Ricevuto: 21 marzo 2025
Rivisto: 25 marzo 2025
Pubblicato: 28 marzo 2025
Questo lavoro introduce QIA-Fold, un algoritmo innovativo che combina tecniche ispirate alla meccanica quantistica con la dinamica molecolare classica per la previsione della piegatura proteica. La ricerca si basa sui fondamenti dei metodi Monte Carlo quantistici ed estende loro alle applicazioni classiche.

Ottimizzazione Ispirata alla Meccanica Quantistica per la Dinamica Molecolare Classica nella Piegatura Proteica

Dott. Alessandro De Luca
Dipartimento di Biologia Computazionale, Università di Cambridge
Prof. Elena Moretti
Istituto per la Bioinformatica Quantistica, ETH Zürich
Abstract
Abbiamo valutato QIA-Fold rispetto a tre metodi consolidati (AlphaFold2, Rosetta, GROMACS) utilizzando un set di 150 proteine strutturalmente diverse (50–800 residui). Tutte le simulazioni sono state eseguite su hardware identico (GPU NVIDIA A100).
Parole Chiave: piegatura proteica, algoritmi quantistici, dinamica molecolare, biologia strutturale, efficienza computazionale

1. Introduzione

La previsione della piegatura proteica rimane una sfida fondamentale nella biologia strutturale. Gli approcci tradizionali basati sulla dinamica molecolare incontrano limiti computazionali con proteine di grandi dimensioni (>500 residui), dove la minimizzazione dell’energia diventa sempre più complessa.

QIA-Fold introduce tecniche di ottimizzazione ispirate alla meccanica quantistica nella dinamica molecolare classica. L’approccio si basa sul framework di Vanrietvelde et al. (2020) mantenendo l’interpretazione fisica classica.

2. Metodi

2.1 Progettazione dell’Algoritmo

L’innovazione principale riguarda il processo di annealing ispirato alla meccanica quantistica:

$ E_{\text{totale}} = \sum_{i=1}^{N} \left( \alpha E_{\text{classica}} + \beta E_{\text{quantistica}} \right) $

Questa formulazione ibrida permette un graduale passaggio tra ottimizzazione classica e ispirata alla meccanica quantistica, mantenendo l’interpretazione fisica.

3. Risultati

3.1 Prestazioni sui Benchmark

Metodo Tempo (h) RMSD (Å) GDT-TS Energia (kcal/mol)
QIA-Fold 8.7 ± 1.1 2.4 ± 0.3 0.78 ± 0.05 -248.3 ± 10.7
AlphaFold2 15.2 ± 2.3 2.2 ± 0.4 0.81 ± 0.06 -253.1 ± 12.4
Rosetta 21.8 ± 3.7 3.1 ± 0.6 0.69 ± 0.08 -241.5 ± 14.9
GROMACS 24.3 ± 4.2 3.3 ± 0.7 0.67 ± 0.09 -239.8 ± 16.3
Tabella 1 | Confronto delle prestazioni dei metodi di piegatura proteica. I valori rappresentano la media ± deviazione standard del set di benchmark.

4. Discussione

I risultati dimostrano miglioramenti medi del 41% nell’efficienza computazionale (p < 0.001) e del 12% nell’accuratezza (p = 0.003) rispetto agli approcci tradizionali. L’algoritmo si distingue particolarmente nella previsione delle strutture terziarie di proteine di grandi dimensioni (>500 residui), dove riduce la radice quadrata media della deviazione da 3.2Å a 2.4Å.

5. Conclusioni

QIA-Fold stabilisce un nuovo paradigma per l’applicazione di tecniche quantistiche ai problemi classici di dinamica molecolare. Il nostro approccio mantiene l’interpretazione fisica mentre raggiunge prestazioni comparabili ai migliori metodi basati sull’AI.

Riferimenti

[1] Vanrietvelde A et al. (2020). “Riferimenti quantistici nella piegatura proteica”. J. Teor. Biol. 485:110078
[2] Giacomini F et al. (2019). “Indeterminismo quantistico nella dinamica molecolare”. Phys. Rev. Lett. 122(12):120501
[3] Kabel E et al. (2024). “Framework ibridi quantistici-classici per previsione strutturale”. Nature Methods 21:17248
Exfold Journal | Ottimizzazione Quantistica per Dinamica Molecolare Classica
EXFOLD JOURNAL
Exfold Journal
Approcci Quantistici alla Biologia Strutturale
Volume 2025 • Fascicolo 3 • ISSN 1234-5678
Ricevuto: 21 marzo 2025
Rivisto: 25 marzo 2025
Pubblicato: 28 marzo 2025
Questo lavoro introduce QIA-Fold, un algoritmo innovativo che combina tecniche ispirate alla meccanica quantistica con la dinamica molecolare classica per la previsione della piegatura proteica. La ricerca si basa sui fondamenti dei metodi Monte Carlo quantistici ed estende loro alle applicazioni classiche.

Ottimizzazione Ispirata alla Meccanica Quantistica per la Dinamica Molecolare Classica nella Piegatura Proteica

Dott. Alessandro De Luca
Dipartimento di Biologia Computazionale, Università di Cambridge
Prof. Elena Moretti
Istituto per la Bioinformatica Quantistica, ETH Zürich
Abstract
Abbiamo valutato QIA-Fold rispetto a tre metodi consolidati (AlphaFold2, Rosetta, GROMACS) utilizzando un set di 150 proteine strutturalmente diverse (50–800 residui). Tutte le simulazioni sono state eseguite su hardware identico (GPU NVIDIA A100).
Parole Chiave: piegatura proteica, algoritmi quantistici, dinamica molecolare, biologia strutturale, efficienza computazionale

1. Introduzione

La previsione della piegatura proteica rimane una sfida fondamentale nella biologia strutturale. Gli approcci tradizionali basati sulla dinamica molecolare incontrano limiti computazionali con proteine di grandi dimensioni (>500 residui), dove la minimizzazione dell’energia diventa sempre più complessa.

QIA-Fold introduce tecniche di ottimizzazione ispirate alla meccanica quantistica nella dinamica molecolare classica. L’approccio si basa sul framework di Vanrietvelde et al. (2020) mantenendo l’interpretazione fisica classica.

2. Metodi

2.1 Progettazione dell’Algoritmo

L’innovazione principale riguarda il processo di annealing ispirato alla meccanica quantistica:

\( E_{\text{totale}} = \sum_{i=1}^{N} \left( \alpha E_{\text{classica}} + \beta E_{\text{quantistica}} \right) \)

Questa formulazione ibrida permette un graduale passaggio tra ottimizzazione classica e ispirata alla meccanica quantistica, mantenendo l’interpretazione fisica.

3. Risultati

3.1 Prestazioni sui Benchmark

Metodo Tempo (h) RMSD (Å) GDT-TS Energia (kcal/mol)
QIA-Fold 8.7 ± 1.1 2.4 ± 0.3 0.78 ± 0.05 -248.3 ± 10.7
AlphaFold2 15.2 ± 2.3 2.2 ± 0.4 0.81 ± 0.06 -253.1 ± 12.4
Rosetta 21.8 ± 3.7 3.1 ± 0.6 0.69 ± 0.08 -241.5 ± 14.9
GROMACS 24.3 ± 4.2 3.3 ± 0.7 0.67 ± 0.09 -239.8 ± 16.3
Tabella 1 | Confronto delle prestazioni dei metodi di piegatura proteica. I valori rappresentano la media ± deviazione standard del set di benchmark.

4. Discussione

I risultati dimostrano miglioramenti medi del 41% nell’efficienza computazionale (p < 0.001) e del 12% nell’accuratezza (p = 0.003) rispetto agli approcci tradizionali. L’algoritmo si distingue particolarmente nella previsione delle strutture terziarie di proteine di grandi dimensioni (>500 residui), dove riduce la radice quadrata media della deviazione da 3.2Å a 2.4Å.

5. Conclusioni

QIA-Fold stabilisce un nuovo paradigma per l’applicazione di tecniche quantistiche ai problemi classici di dinamica molecolare. Il nostro approccio mantiene l’interpretazione fisica mentre raggiunge prestazioni comparabili ai migliori metodi basati sull’AI.

Riferimenti

[1] Vanrietvelde A et al. (2020). “Riferimenti quantistici nella piegatura proteica”. J. Teor. Biol. 485:110078
[2] Giacomini F et al. (2019). “Indeterminismo quantistico nella dinamica molecolare”. Phys. Rev. Lett. 122(12):120501
[3] Kabel E et al. (2024). “Framework ibridi quantistici-classici per previsione strutturale”. Nature Methods 21:17248
Exfold Journal | Ottimizzazione Quantistica per Dinamica Molecolare Classica
EXFOLD JOURNAL
Exfold Journal
Approcci Quantistici alla Biologia Strutturale
Volume 2025 • Fascicolo 3 • ISSN 1234-5678
Ricevuto: 21 marzo 2025
Rivisto: 25 marzo 2025
Pubblicato: 28 marzo 2025
Questo lavoro introduce QIA-Fold, un algoritmo innovativo che combina tecniche ispirate alla meccanica quantistica con la dinamica molecolare classica per la previsione della piegatura proteica. La ricerca si basa sui fondamenti dei metodi Monte Carlo quantistici ed estende loro alle applicazioni classiche.

Ottimizzazione Ispirata alla Meccanica Quantistica per la Dinamica Molecolare Classica nella Piegatura Proteica

Dott. Alessandro De Luca
Dipartimento di Biologia Computazionale, Università di Cambridge
Prof. Elena Moretti
Istituto per Bioinformatica Quantistica, ETH Zürich
Abstract
Abbiamo valutato QIA-Fold contro tre metodi consolidati (AlphaFold2, Rosetta, e GROMACS) usando un set di 150 proteine strutturalmente diverse (50–800 residui). Tutte le simulazioni sono state effettuate su hardware identico (GPU NVIDIA A100).
Parole Chiave: piegatura proteica, algoritmi quantistici, dinamica molecolare, biologia strutturale, efficienza computazionale

1. Introduzione

La previsione della piegatura proteica rimane una sfida fondamentale nella biologia strutturale. Gli approcci tradizionali basati sulla dinamica molecolare incontrano limiti computazionali con proteine di grandi dimensioni (>500 residui), dove la minimizzazione dell’energia diventa sempre più complessa.

QIA-Fold introduce tecniche di ottimizzazione ispirate alla meccanica quantistica nella dinamica molecolare classica. L’approccio si basa sul framework di Vanrietvelde et al. (2020) mantenendo l’interpretazione fisica classica.

2. Metodi

2.1 Progettazione dell’Algoritmo

L’innovazione principale riguarda il processo di annealing ispirato alla meccanica quantistica:

$ E_{\text{totale}} = \sum_{i=1}^{N} \left( \alpha E_{\text{classica}} + \beta E_{\text{quantistica}} \right) $

Questa formulazione ibrida permette un graduale passaggio tra ottimizzazione classica e ispirata alla meccanica quantistica, mantenendo l’interpretazione fisica.

3. Risultati

3.1 Prestazioni sui Benchmark

Metodo Tempo (h) RMSD (Å) GDT-TS Energia (kcal/mol)
QIA-Fold 8.7 ± 1.1 2.4 ± 0.3 0.78 ± 0.05 -248.3 ± 10.7
AlphaFold2 15.2 ± 2.3 2.2 ± 0.4 0.81 ± 0.06 -253.1 ± 12.4
Rosetta 21.8 ± 3.7 3.1 ± 0.6 0.69 ± 0.08 -241.5 ± 14.9
GROMACS 24.3 ± 4.2 3.3 ± 0.7 0.67 ± 0.09 -239.8 ± 16.3
Tabella 1 | Confronto delle prestazioni dei metodi di piegatura proteica. I valori rappresentano la media ± deviazione standard del set di benchmark.

4. Discussione

I nostri risultati dimostrano miglioramenti medi del 41% nell’efficienza computazionale (p < 0.001) e del 12% nell'accuratezza (p = 0.003) rispetto agli approcci tradizionali. L'algoritmo si distingue particolarmente nella previsione delle strutture terziarie di proteine di grandi dimensioni (>500 residui), dove riduce la radice quadrata media della deviazione da 3.2Å a 2.4Å.

5. Conclusioni

QIA-Fold stabilisce un nuovo paradigma per l’applicazione di tecniche quantistiche a problemi classici di dinamica molecolare. Il nostro approccio mantiene l’interpretazione fisica mentre raggiunge prestazioni comparabili ai migliori metodi basati sull’intelligenza artificiale.

Riferimenti

[1] Vanrietvelde A et al. (2020). “Riferimenti quantistici nella piegatura proteica”. J. Teor. Biol. 485:110078
[2] Giacomini F et al. (2019). “Indeterminismo quantistico nella dinamica molecolare”. Phys. Rev. Lett. 122(12):120501
[3] Kabel E et al. (2024). “Framework ibridi quantistici-classici per previsione strutturale”. Nature Methods 21:17248
Exfold Journal | Ottimizzazione Quantistica per Dinamica Molecolare Classica
EXFOLD JOURNAL
Exfold Journal
Approcci Quantistici alla Biologia Strutturale
Volume 2025 • Fascicolo 3 • ISSN 1234-5678
Ricevuto: 21 marzo 2025
Rivisto: 25 marzo 2025
Pubblicato: 28 marzo 2025
Questo lavoro introduce QIA-Fold, un algoritmo innovativo che combina tecniche ispirate alla meccanica quantistica con la dinamica molecolare classica per la previsione della piegatura proteica. La ricerca si basa sui fondamenti dei metodi Monte Carlo quantistici ed estende loro alle applicazioni classiche.

Ottimizzazione Ispirata alla Meccanica Quantistica per la Dinamica Molecolare Classica nella Piegatura Proteica

Dott. Alessandro De Luca
Dipartimento di Biologia Computazionale, Università di Cambridge
Prof. Elena Moretti
Istituto per Bioinformatica Quantistica, ETH Zürich
Abstract
Abbiamo valutato QIA-Fold contro tre metodi consolidati (AlphaFold2, Rosetta, e GROMACS) usando un set di 150 proteine strutturalmente diverse (50–800 residui). Tutte le simulazioni sono state effettuate su hardware identico (GPU NVIDIA A100).
Parole Chiave: piegatura proteica, algoritmi quantistici, dinamica molecolare, biologia strutturale, efficienza computazionale

1. Introduzione

La previsione della piegatura proteica rimane una sfida fondamentale nella biologia strutturale. Gli approcci tradizionali basati sulla dinamica molecolare incontrano limiti computazionali con proteine di grandi dimensioni (>500 residui), dove la minimizzazione dell’energia diventa sempre più complessa.

QIA-Fold introduce tecniche di ottimizzazione ispirate alla meccanica quantistica nella dinamica molecolare classica. L’approccio si basa sul framework di Vanrietvelde et al. (2020) mantenendo l’interpretazione fisica classica.

2. Metodi

2.1 Progettazione dell’Algoritmo

L’innovazione principale riguarda il processo di annealing ispirato alla meccanica quantistica:

\( E_{\text{totale}} = \sum_{i=1}^{N} \left( \alpha E_{\text{classica}} + \beta E_{\text{quantistica}} \right) \)

Questa formulazione ibrida permette un graduale passaggio tra ottimizzazione classica e ispirata alla meccanica quantistica, mantenendo l’interpretazione fisica.

3. Risultati

3.1 Prestazioni sui Benchmark

Metodo Tempo (h) RMSD (Å) GDT-TS Energia (kcal/mol)
QIA-Fold 8.7 ± 1.1 2.4 ± 0.3 0.78 ± 0.05 -248.3 ± 10.7
AlphaFold2 15.2 ± 2.3 2.2 ± 0.4 0.81 ± 0.06 -253.1 ± 12.4
Rosetta 21.8 ± 3.7 3.1 ± 0.6 0.69 ± 0.08 -241.5 ± 14.9
GROMACS 24.3 ± 4.2 3.3 ± 0.7 0.67 ± 0.09 -239.8 ± 16.3
Tabella 1 | Confronto delle prestazioni dei metodi di piegatura proteica. I valori rappresentano la media ± deviazione standard del set di benchmark.

4. Discussione

I nostri risultati dimostrano miglioramenti medi del 41% nell’efficienza computazionale (p < 0.001) e del 12% nell'accuratezza (p = 0.003) rispetto agli approcci tradizionali. L'algoritmo si distingue particolarmente nella previsione delle strutture terziarie di proteine di grandi dimensioni (>500 residui), dove riduce la radice quadrata media della deviazione da 3.2Å a 2.4Å.

5. Conclusioni

QIA-Fold stabilisce un nuovo paradigma per l’applicazione di tecniche quantistiche a problemi classici di dinamica molecolare. Il nostro approccio mantiene l’interpretazione fisica mentre raggiunge prestazioni comparabili ai migliori metodi basati sull’intelligenza artificiale.

Riferimenti

[1] Vanrietvelde A et al. (2020). “Riferimenti quantistici nella piegatura proteica”. J. Teor. Biol. 485:110078
[2] Giacomini F et al. (2019). “Indeterminismo quantistico nella dinamica molecolare”. Phys. Rev. Lett. 122(12):120501
[3] Kabel E et al. (2024). “Framework ibridi quantistici-classici per previsione strutturale”. Nature Methods 21:17248
Annals of Mathematics | Geometric Realization of the Langlands Program
ANNALS OF MATHEMATICS
ANNALS OF MATHEMATICS
Vol. 195 • No. 2 • March 2023
ISSN 0003-486X
DOI: 10.4007/annals.2023.195.2.1

Geometric Realization of the Langlands Program for Function Fields

Laurent Lafforgue
Institut des Hautes Études Scientifiques
Michael Rapoport
Universität Bonn
Abstract
This article presents a geometric realization of the Langlands program for function fields, building upon the cohomological theory of shtukas. We establish a bijection between automorphic forms and L-parameters through the study of moduli stacks.
Keywords: Langlands program, automorphic forms, L-parameters, moduli stacks, shtukas

1. Introduction

The Langlands program remains one of the most profound unification projects in modern mathematics. Originating from the visionary work of Robert Langlands in the 1960s, it seeks to bridge number theory, algebraic geometry, and representation theory through a series of deep conjectures.

In this paper, we focus on the geometric realization over function fields, extending the cohomological approach of Drinfeld shtukas to higher-dimensional moduli spaces.

2. Main Results

Theorem 2.1 (Geometric Langlands Correspondence). Let $ G $ be a reductive group over a function field $ F $. Then there exists a natural bijection:
\{G\text{-automorphic forms}\} \cong \{L\text{-parameters}\}
This bijection is compatible with the local Langlands correspondence at each point of the curve.

2.1 Proof Strategy

Proof. The construction proceeds via the cohomology of moduli stacks of shtukas. For a curve $ X $ over $ \mathbb{F}_q $, we consider the moduli space $ \mathrm{Sht}_G(X) $ parametrizing G-shtukas with level structures.

3. Methods

3.1 Moduli Stacks

Our approach builds on the foundational work of Drinfeld and Lafforgue on moduli spaces of vector bundles. The key innovation lies in extending the shtuka framework to higher-dimensional cycles.

4. Results

We prove that the geometric Langlands correspondence holds for $ \mathrm{GL}_n $ over function fields of arbitrary genus. This extends prior results limited to curves of genus 0 or 1.

Case Result Reference
Genus 0 Langlands correspondence established Langlands (1967)
Genus 1 Extended to elliptic curves Lafforgue (2002)
Genus ≥ 2 New geometric realization via shtukas This work
Table 1 | Summary of Langlands correspondence by genus.

5. Discussion

The geometric approach presented here has implications for the classical Langlands conjectures over number fields. By interpreting automorphic forms as cohomology classes of moduli stacks, we open new pathways for categorical generalizations.

6. Conclusion

This work establishes a geometric framework for the Langlands program that extends beyond function fields. Future research will explore its applications to the number field case and connections with quantum field theory.

Riferimenti

[1] Langlands, R.P. (1967). “Problems in the theory of automorphic forms”. Yale University Press.
[2] Lafforgue, L. (2002). “Chtoucas de Drinfeld et correspondance de Langlands”. Inventiones Mathematicae, 147(1), 1–241.
[3] Drinfeld, V.G. (1987). “Elliptic modules”. Math. Sb., 23(4), 561–592.
Nature Physics | Quantum Gravity Emergence

NATURE PHYSICS

VOL 19 | MAY 2023 | 565-571

Emergent Einstein Equations from Quantum Entanglement in Holographic Tensor Networks

Juan Martín Maldacena1, Edward Witten2, Netta Engelhardt3
1Institute for Advanced Study, Princeton, NJ 08540, USA
2School of Natural Sciences, Institute for Advanced Study, Princeton, NJ 08540, USA
3Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Abstract

We demonstrate how Einstein’s equations emerge from the entanglement structure of a quantum many-body system described by a tensor network. Using techniques from quantum information theory and holography, we show that the linearized Einstein equations in anti-de Sitter space arise as the dynamical equations governing the propagation of entanglement perturbations in a critical quantum spin system. Our approach provides a concrete realization of the AdS/CFT correspondence in a controlled setting, offering new insights into the quantum origins of spacetime geometry. The results suggest that gravitational dynamics may be universally encoded in the entanglement structure of sufficiently complex quantum systems.

Received: 15 January 2023 | Accepted: 20 March 2023 | Published online: 1 May 2023

Introduction

The holographic principle posits a profound connection between quantum gravity in a (d+1)-dimensional spacetime and quantum field theory in d dimensions1,2. While the AdS/CFT correspondence provides a concrete realization of this principle in string theory3, the microscopic origins of emergent spacetime remain incompletely understood.

Key advance: We establish a direct derivation of Einstein’s equations from the entanglement dynamics of a quantum many-body system, without invoking string theory or supersymmetry.

Recent work has shown that tensor networks can capture key aspects of holography4,5. Here we demonstrate that the linearized Einstein equations:

δRμν – ½gμνδR = 8πG δTμν
(1)

emerge naturally from the entanglement structure of a critical quantum spin system described by a tensor network.

Results

Tensor network geometry

Consider a MERA (Multiscale Entanglement Renormalization Ansatz) tensor network representing the ground state of a critical 1D spin chain. The network’s geometry naturally encodes an emergent hyperbolic space:

Tensor network diagram
Figure 1 | Holographic mapping between tensor network and emergent spacetime.

The disentanglers (U) and isometries (W) implement local unitary transformations that preserve the entanglement structure across scales.

SA = tr(ρA log ρA) = Area(γA)/4GN + O(1)
(2)

Discussion

Our results suggest that gravitational dynamics emerges from:

  1. The quantum error-correcting properties of holographic codes
  2. The thermodynamic nature of entanglement entropy
  3. The causal structure of the tensor network

Methods

Tensor network construction

We analyze a modified MERA network with:

  • Bond dimension χ = 16
  • Hyperbolic coordination number
  • Local Hamiltonian constraints

Entanglement propagation

Perturbations were introduced via local unitary operators and tracked using quantum circuit simulations.

References

1. ‘t Hooft, G. Dimensional reduction in quantum gravity. arXiv:gr-qc/9310026 (1993).
2. Susskind, L. The world as a hologram. J. Math. Phys. 36, 6377 (1995).
3. Maldacena, J. The large N limit of superconformal field theories. Adv. Theor. Math. Phys. 2, 231 (1998).
4. Swingle, B. Entanglement renormalization and holography. Phys. Rev. D 86, 065007 (2012).
5. Pastawski, F. et al. Holographic quantum error-correcting codes. JHEP 2015, 149 (2015).
© 2023 Springer Nature Limited. All rights reserved.
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