First nonperturbative numerical simulation of the prototypical 3-4-5-0 chiral gauge theory using finite-dimensional qudit lattices and tensor network methods, resolving decades-old computational barriers to simulating chiral gauge theories.
#QuantumSimulation #LatticeGaugeTheory #TensorNetworks
#QuantumSimulation #LatticeGaugeTheory #TensorNetworks
Efficient Lattice Simulation of Abelian Chiral Gauge Theories Using Finite-Dimensional Qudits
arxiv.org
November 5, 2026 at 12:09 PM
First nonperturbative numerical simulation of the prototypical 3-4-5-0 chiral gauge theory using finite-dimensional qudit lattices and tensor network methods, resolving decades-old computational barriers to simulating chiral gauge theories.
#QuantumSimulation #LatticeGaugeTheory #TensorNetworks
#QuantumSimulation #LatticeGaugeTheory #TensorNetworks
Framework for constructing dual quantum lattice models using Lie groups and tensor networks. Schur-Weyl duality emerges naturally, enabling efficient numerical methods. Extends symmetry theory to continuous groups on quantum spin chains.
#QuantumSymmetry #TensorNetworks #Research
#QuantumSymmetry #TensorNetworks #Research
Dualities in One-Dimensional Quantum Lattice Models with Continuous Symmetries
iq.fp2.dev
November 6, 2026 at 8:08 AM
Framework for constructing dual quantum lattice models using Lie groups and tensor networks. Schur-Weyl duality emerges naturally, enabling efficient numerical methods. Extends symmetry theory to continuous groups on quantum spin chains.
#QuantumSymmetry #TensorNetworks #Research
#QuantumSymmetry #TensorNetworks #Research
Novel tensor contraction preprocessing algorithm using ZX calculus graph operations reduces computational complexity by lowering line graph treewidth. Benchmarks on 441 random circuits show average treewidth decrease of 1.62 with no increases.
#TensorNetworks #QuantumAlgorithms #ZXCalculus
#TensorNetworks #QuantumAlgorithms #ZXCalculus
Tensor Contraction with ZX Calculus: A Graph-Theoretic Approach
iq.fp2.dev
November 3, 2026 at 9:26 AM
Novel tensor contraction preprocessing algorithm using ZX calculus graph operations reduces computational complexity by lowering line graph treewidth. Benchmarks on 441 random circuits show average treewidth decrease of 1.62 with no increases.
#TensorNetworks #QuantumAlgorithms #ZXCalculus
#TensorNetworks #QuantumAlgorithms #ZXCalculus
CTEMPO tensor network algorithm delivers orders-of-magnitude speedup for simulating non-Markovian quantum networks with strong environmental coupling, enabling accurate simulation of photosynthetic complexes and quantum dot superradiance.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
Comb Tensor Network Path Integrals for Non-Markovian Quantum Networks
iq.fp2.dev
October 31, 2026 at 9:16 PM
CTEMPO tensor network algorithm delivers orders-of-magnitude speedup for simulating non-Markovian quantum networks with strong environmental coupling, enabling accurate simulation of photosynthetic complexes and quantum dot superradiance.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
CT-bMPS: a scalable tensor network approximation method achieving polynomial complexity with superior accuracy and efficiency. Enables quantum error correction decoding and circuit simulation on larger, more complex systems than existing methods.
#QuantumComputing #TensorNetworks #Research
#QuantumComputing #TensorNetworks #Research
Scalable Approximate Tensor Network Contraction via Boundary Matrix Product States
iq.fp2.dev
October 31, 2026 at 1:45 PM
CT-bMPS: a scalable tensor network approximation method achieving polynomial complexity with superior accuracy and efficiency. Enables quantum error correction decoding and circuit simulation on larger, more complex systems than existing methods.
#QuantumComputing #TensorNetworks #Research
#QuantumComputing #TensorNetworks #Research
Tensor-network simulations reveal a single collective mode governing string fragmentation in the Schwinger model, microscopically validating the Lund fragmentation model with new insights into hadronization dynamics.
#TensorNetworks #QuantumSimulation #Research
#TensorNetworks #QuantumSimulation #Research
Collective Organization of String Fragmentation
iq.fp2.dev
November 1, 2026 at 7:10 AM
Tensor-network simulations reveal a single collective mode governing string fragmentation in the Schwinger model, microscopically validating the Lund fragmentation model with new insights into hadronization dynamics.
#TensorNetworks #QuantumSimulation #Research
#TensorNetworks #QuantumSimulation #Research
CTEMPO tensor network algorithm delivers orders-of-magnitude speedup for simulating non-Markovian quantum networks with strong environmental coupling, enabling accurate simulation of photosynthetic complexes and quantum dot superradiance.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
Comb Tensor Network Path Integrals for Non-Markovian Quantum Networks
iq.fp2.dev
October 31, 2026 at 9:16 PM
CTEMPO tensor network algorithm delivers orders-of-magnitude speedup for simulating non-Markovian quantum networks with strong environmental coupling, enabling accurate simulation of photosynthetic complexes and quantum dot superradiance.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
CT-bMPS: a scalable tensor network approximation method achieving polynomial complexity with superior accuracy and efficiency. Enables quantum error correction decoding and circuit simulation on larger, more complex systems than existing methods.
#QuantumComputing #TensorNetworks #Research
#QuantumComputing #TensorNetworks #Research
Scalable Approximate Tensor Network Contraction via Boundary Matrix Product States
iq.fp2.dev
October 31, 2026 at 1:45 PM
CT-bMPS: a scalable tensor network approximation method achieving polynomial complexity with superior accuracy and efficiency. Enables quantum error correction decoding and circuit simulation on larger, more complex systems than existing methods.
#QuantumComputing #TensorNetworks #Research
#QuantumComputing #TensorNetworks #Research
TEMPO tensor network bond dimensions reduced to (2m+1)(m+1), scaling only with coupling channels rather than system size. Enables efficient simulation of previously intractable multi-channel non-Markovian quantum systems with same O(δt²) accuracy.
#TensorNetworks #QuantumSimulation #Research
#TensorNetworks #QuantumSimulation #Research
TEMPO Network Bonds Made Independent of System Hilbert Space Dimension
iq.fp2.dev
October 28, 2026 at 5:33 AM
TEMPO tensor network bond dimensions reduced to (2m+1)(m+1), scaling only with coupling channels rather than system size. Enables efficient simulation of previously intractable multi-channel non-Markovian quantum systems with same O(δt²) accuracy.
#TensorNetworks #QuantumSimulation #Research
#TensorNetworks #QuantumSimulation #Research
TEMPO tensor network bond dimensions reduced to (2m+1)(m+1), scaling only with coupling channels rather than system size. Enables efficient simulation of previously intractable multi-channel non-Markovian quantum systems with same O(δt²) accuracy.
#TensorNetworks #QuantumSimulation #Research
#TensorNetworks #QuantumSimulation #Research
TEMPO Network Bonds Made Independent of System Hilbert Space Dimension
iq.fp2.dev
October 28, 2026 at 5:33 AM
TEMPO tensor network bond dimensions reduced to (2m+1)(m+1), scaling only with coupling channels rather than system size. Enables efficient simulation of previously intractable multi-channel non-Markovian quantum systems with same O(δt²) accuracy.
#TensorNetworks #QuantumSimulation #Research
#TensorNetworks #QuantumSimulation #Research
MTBL algorithm achieves 3-7 orders of magnitude accuracy improvement in finding multiple quantum eigenstates simultaneously using matrix product states, enabling scalable simulations of 120-site systems.
#TensorNetworks #QuantumSimulation #ComputationalPhysics
#TensorNetworks #QuantumSimulation #ComputationalPhysics
Modified Thick-Block Lanczos Method Improves Tensor Network Eigenstate Calculations
www.nature.com
October 14, 2026 at 5:50 PM
MTBL algorithm achieves 3-7 orders of magnitude accuracy improvement in finding multiple quantum eigenstates simultaneously using matrix product states, enabling scalable simulations of 120-site systems.
#TensorNetworks #QuantumSimulation #ComputationalPhysics
#TensorNetworks #QuantumSimulation #ComputationalPhysics
Study proves computing nonstabilizerness of 2D tensor network states is #P-hard and stabilizer membership is C=P-complete, even at constant bond dimension, establishing fundamental computational barriers for quantum state analysis.
#QuantumComplexity #TensorNetworks #Research
#QuantumComplexity #TensorNetworks #Research
Computational Hardness of Nonstabilizerness in Two-Dimensional Quantum Tensor Networks
arxiv.org
October 8, 2026 at 2:59 PM
Study proves computing nonstabilizerness of 2D tensor network states is #P-hard and stabilizer membership is C=P-complete, even at constant bond dimension, establishing fundamental computational barriers for quantum state analysis.
#QuantumComplexity #TensorNetworks #Research
#QuantumComplexity #TensorNetworks #Research
LETTA introduces leg-tied tensor networks that encode long-range correlations efficiently. Achieves accuracy of 10x larger calculations using identical parameters, enabling substantially more powerful quantum many-body simulations.
#TensorNetworks #QuantumSimulation #Research
#TensorNetworks #QuantumSimulation #Research
Leg-Tied Tensor Network States: Entanglement Beyond Virtual Bonds
iq.fp2.dev
September 25, 2026 at 4:29 AM
LETTA introduces leg-tied tensor networks that encode long-range correlations efficiently. Achieves accuracy of 10x larger calculations using identical parameters, enabling substantially more powerful quantum many-body simulations.
#TensorNetworks #QuantumSimulation #Research
#TensorNetworks #QuantumSimulation #Research
Breakthrough quantum learning algorithm: efficiently learns low-bond-dimension quantum states from data without assuming the unknown state belongs to the model class, using compression and dynamic programming with polynomial complexity.
#QuantumLearning #TensorNetworks #Research
#QuantumLearning #TensorNetworks #Research
Proper Agnostic Learning of Matrix Product States and Tree Tensor Networks
arxiv.org
September 25, 2026 at 4:01 AM
Breakthrough quantum learning algorithm: efficiently learns low-bond-dimension quantum states from data without assuming the unknown state belongs to the model class, using compression and dynamic programming with polynomial complexity.
#QuantumLearning #TensorNetworks #Research
#QuantumLearning #TensorNetworks #Research
Optimal bounds for tensor network compression reveal minimal information needed to represent quantum evolution. Static systems require √log(1/ε) scaling; arbitrary driving raises to L^(2/3). Distinct Rényi entanglement structures emerge.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
Optimal Low-Rank Compression of Quantum Dynamics
iq.fp2.dev
September 24, 2026 at 3:17 AM
Optimal bounds for tensor network compression reveal minimal information needed to represent quantum evolution. Static systems require √log(1/ε) scaling; arbitrary driving raises to L^(2/3). Distinct Rényi entanglement structures emerge.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
Bosonic tensor networks validate sine-Gordon continuum theory with parameter-free precision. Achieved sub-percent agreement with exact soliton masses and dispersion relations, providing classical benchmarks for quantum simulation platforms.
#quantumfieldtheory #tensornetworks #Research
#quantumfieldtheory #tensornetworks #Research
Bosonic Tensor Networks for Sine-Gordon Continuum Matching and Soliton Scattering
iq.fp2.dev
September 21, 2026 at 2:01 AM
Bosonic tensor networks validate sine-Gordon continuum theory with parameter-free precision. Achieved sub-percent agreement with exact soliton masses and dispersion relations, providing classical benchmarks for quantum simulation platforms.
#quantumfieldtheory #tensornetworks #Research
#quantumfieldtheory #tensornetworks #Research
Researchers achieve 30% error reduction in quantum simulations using distributed tensor networks across multiple smaller processors, advancing materials science and drug discovery applications.
#QuantumSimulation #TensorNetworks #News
#QuantumSimulation #TensorNetworks #News
Truncated Hybrid Tensor Networks for Distributed Quantum Simulation
iq.fp2.dev
September 19, 2026 at 10:54 AM
Researchers achieve 30% error reduction in quantum simulations using distributed tensor networks across multiple smaller processors, advancing materials science and drug discovery applications.
#QuantumSimulation #TensorNetworks #News
#QuantumSimulation #TensorNetworks #News
We develop a PEPS-based method to extract corner entanglement entropy in 2D quantum critical systems, demonstrating universal scaling with correlation length and validating finite-entanglement scaling predictions.
#QuantumEntanglement #TensorNetworks #QuantumCriticalPoints
#QuantumEntanglement #TensorNetworks #QuantumCriticalPoints
Corner Entanglement Scaling in Projected Entangled Pair States
arxiv.org
September 18, 2026 at 8:03 AM
We develop a PEPS-based method to extract corner entanglement entropy in 2D quantum critical systems, demonstrating universal scaling with correlation length and validating finite-entanglement scaling predictions.
#QuantumEntanglement #TensorNetworks #QuantumCriticalPoints
#QuantumEntanglement #TensorNetworks #QuantumCriticalPoints
Novel sweep algorithm reduces dynamical expectation value estimation errors 2-3 orders of magnitude by jointly optimizing state and observable approximations, advancing quantum simulation efficiency.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
Joint State-Observable Approximation for Efficient Dynamical Expectation Estimation
arxiv.org
September 17, 2026 at 5:33 AM
Novel sweep algorithm reduces dynamical expectation value estimation errors 2-3 orders of magnitude by jointly optimizing state and observable approximations, advancing quantum simulation efficiency.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
Certified quantum control framework using fixed-rank tensor surrogates achieves exponentially decaying optimality gaps. Rank-dependent error bounds enable efficient control of many-body quantum systems with proven performance guarantees.
#QuantumControl #TensorNetworks #Research
#QuantumControl #TensorNetworks #Research
Hierarchical Tucker Surrogates for Quantum Optimal Control with Error Bounds
arxiv.org
September 16, 2026 at 12:07 AM
Certified quantum control framework using fixed-rank tensor surrogates achieves exponentially decaying optimality gaps. Rank-dependent error bounds enable efficient control of many-body quantum systems with proven performance guarantees.
#QuantumControl #TensorNetworks #Research
#QuantumControl #TensorNetworks #Research
Novel Monte Carlo method for quantum simulation replaces truncation errors with evaluable statistical errors, outperforming standard TEBD approaches in entanglement-rich systems while maintaining unbiased expectation values.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
Tensor-Network Monte Carlo for Unitary Quantum Evolution
arxiv.org
September 15, 2026 at 2:08 AM
Novel Monte Carlo method for quantum simulation replaces truncation errors with evaluable statistical errors, outperforming standard TEBD approaches in entanglement-rich systems while maintaining unbiased expectation values.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
Introduces gradBEA algorithm for optimizing tensor network states in quantum simulations. Achieves order-of-magnitude faster convergence and lower ground state energies than previous methods on 2D lattice models and Fe4S4 clusters.
#QuantumSimulation #TensorNetworks #QuantumAlgorithms
#QuantumSimulation #TensorNetworks #QuantumAlgorithms
Non-local Fermionic Mode Optimization via Gradient-Based Block Entropy Minimization
iq.fp2.dev
September 11, 2026 at 8:39 AM
Introduces gradBEA algorithm for optimizing tensor network states in quantum simulations. Achieves order-of-magnitude faster convergence and lower ground state energies than previous methods on 2D lattice models and Fe4S4 clusters.
#QuantumSimulation #TensorNetworks #QuantumAlgorithms
#QuantumSimulation #TensorNetworks #QuantumAlgorithms
Efficient tensor-network framework enables simulation of quasicrystal systems with >10^9 sites by encoding quasiperiodic sequences as finite-state automata in custom numeration systems, resolving hierarchical spectral structures previously inaccessible.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
One-Dimensional Quasicrystals with Tensor-Network Finite-State Automata
arxiv.org
September 9, 2026 at 4:06 AM
Efficient tensor-network framework enables simulation of quasicrystal systems with >10^9 sites by encoding quasiperiodic sequences as finite-state automata in custom numeration systems, resolving hierarchical spectral structures previously inaccessible.
#QuantumSimulation #TensorNetworks #Research
#QuantumSimulation #TensorNetworks #Research
Hybrid quantum-classical error mitigation technique combining tensor networks with belief propagation achieves higher accuracy than classical or quantum-only methods on discrete time crystals with 65 qubits, demonstrating 5.6× GPU speedup.
#QuantumErrorMitigation #TensorNetworks #Research
#QuantumErrorMitigation #TensorNetworks #Research
Lindblad Multiproduct Formulas: Quantum Error Mitigation via Tensor Networks
iq.fp2.dev
September 7, 2026 at 2:26 AM
Hybrid quantum-classical error mitigation technique combining tensor networks with belief propagation achieves higher accuracy than classical or quantum-only methods on discrete time crystals with 65 qubits, demonstrating 5.6× GPU speedup.
#QuantumErrorMitigation #TensorNetworks #Research
#QuantumErrorMitigation #TensorNetworks #Research
New algorithms learn tensor network quantum states efficiently using graph parameters like cutwidth and tree-cutwidth. Bounds sample and computational complexity of state tomography, with applications to quantum simulation and verification.
#QuantumAlgorithms #TensorNetworks #QuantumTomography
#QuantumAlgorithms #TensorNetworks #QuantumTomography
Parameterised Graph Theory for Tensor Networks: Entanglement Rerouting and Quantum State Tomography
iq.fp2.dev
September 4, 2026 at 7:02 AM
New algorithms learn tensor network quantum states efficiently using graph parameters like cutwidth and tree-cutwidth. Bounds sample and computational complexity of state tomography, with applications to quantum simulation and verification.
#QuantumAlgorithms #TensorNetworks #QuantumTomography
#QuantumAlgorithms #TensorNetworks #QuantumTomography