A neural operator method parameterizes local quasiparticle dressing on a ground state to compute magnon dispersions and spectral weights in the J1-J2 Heisenberg model, including the (π,0) anomaly.
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Quantum computing and the entanglement frontier
16 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Quantum information science explores the frontier of highly complex quantum states, the "entanglement frontier." This study is motivated by the observation (widely believed but unproven) that classical systems cannot simulate highly entangled quantum systems efficiently, and we hope to hasten the day when well controlled quantum systems can perform tasks surpassing what can be done in the classical world. One way to achieve such "quantum supremacy" would be to run an algorithm on a quantum computer which solves a problem with a super-polynomial speedup relative to classical computers, but there may be other ways that can be achieved sooner, such as simulating exotic quantum states of strongly correlated matter. To operate a large scale quantum computer reliably we will need to overcome the debilitating effects of decoherence, which might be done using "standard" quantum hardware protected by quantum error-correcting codes, or by exploiting the nonabelian quantum statistics of anyons realized in solid state systems, or by combining both methods. Only by challenging the entanglement frontier will we learn whether Nature provides extravagant resources far beyond what the classical world would allow.
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Defines bridge degree monotone for fermionic non-Gaussianity from Bell-sampling eigenvalues of Lambda, shows non-increase under Gaussian protocols for stronger no-go theorems, and gives polynomial-sample tests for Gaussianity and 2-designs.
Efficient algorithms compute stabilizer Rényi entropy and mana for quantum states from vectors at O(N d^{2N}) cost using fast Hadamard transform, with open-source implementation.
Nonlocal magic in fermionic Gaussian states is bounded by the entanglement spectrum of the covariance matrix, is extensive in the Haar ensemble, peaks at criticality in the Kitaev chain, and grows diffusively under random circuits.
A polylog-sized quantum computer achieves exponential advantage over classical machines in classification and dimension reduction of massive classical data using quantum oracle sketching combined with classical shadows.
A classical polynomial-time sampler exists for the output distribution of amplitude-damped IQP circuits with logarithmic depth and arbitrary l-local diagonal gates.
Tensor-network decomposition converts entangled quantum wavepacket dynamics into independent lower-dimensional tasks executable asynchronously on distributed quantum hardware, demonstrated for vibrational spectra of a protonated water cluster agreeing with classical results to within 4 cm^{-1}.
Stabilizer Rényi entropies and Pauli spectrum cumulants show universal power-law scaling with driving rate in slow processes across quantum phase transitions, with the logarithmic Pauli spectrum asymptotically Gaussian, demonstrated in the transverse-field Ising model and long-range Kitaev models.
Introduces permutation-agnostic distance measures to quantify non-stabiliserness consumption and shows structured variational methods use it more efficiently than unstructured ones with greater classical optimisation freedom.
kA-QAOA matches MA-QAOA approximation ratios on 3-uniform hypergraphs while using significantly fewer function evaluations.
Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
Length asymmetry between counter-propagating chiral edges in a parafermion Josephson junction supplies a spontaneous phase bias that electrically controls Majorana (m=1) or parafermion (m>1) zero modes at Laughlin fillings.
Quantum state evolution in variational algorithms is governed by geometric phase rather than dynamical phase, with entanglement decoupled from evolution in hardware-efficient ansatzes but acting as a dynamical resource in Hamiltonian variational ansatzes.
Hybrid neuromorphic-ANN models outperform standard deep learning on few-shot benchmarks and under occlusion/impulse noise via astrocytic modulation and spiking dynamics.
Consciousness does not directly predict AI existential risk unlike intelligence, though it may indirectly affect risk through alignment or capability requirements.
citing papers explorer
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Neural Polaron: Learning Quasiparticle Operators in Quantum Many-Body Systems
A neural operator method parameterizes local quasiparticle dressing on a ground state to compute magnon dispersions and spectral weights in the J1-J2 Heisenberg model, including the (π,0) anomaly.
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Fermionic non-Gaussianity via Bell sampling: monotones and efficient quantum algorithms
Defines bridge degree monotone for fermionic non-Gaussianity from Bell-sampling eigenvalues of Lambda, shows non-increase under Gaussian protocols for stronger no-go theorems, and gives polynomial-sample tests for Gaussianity and 2-designs.
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Computing quantum magic of state vectors
Efficient algorithms compute stabilizer Rényi entropy and mana for quantum states from vectors at O(N d^{2N}) cost using fast Hadamard transform, with open-source implementation.
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Nonlocal nonstabilizerness in free fermion models
Nonlocal magic in fermionic Gaussian states is bounded by the entanglement spectrum of the covariance matrix, is extensive in the Haar ensemble, peaks at criticality in the Kitaev chain, and grows diffusively under random circuits.
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Exponential quantum advantage in processing massive classical data
A polylog-sized quantum computer achieves exponential advantage over classical machines in classification and dimension reduction of massive classical data using quantum oracle sketching combined with classical shadows.
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Efficient simulation of noisy IQP circuits with amplitude-damping noise
A classical polynomial-time sampler exists for the output distribution of amplitude-damped IQP circuits with logarithmic depth and arbitrary l-local diagonal gates.
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Tensor-Network-Based Distributed Quantum Dynamics on Independent Quantum Computers
Tensor-network decomposition converts entangled quantum wavepacket dynamics into independent lower-dimensional tasks executable asynchronously on distributed quantum hardware, demonstrated for vibrational spectra of a protonated water cluster agreeing with classical results to within 4 cm^{-1}.
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Universal Non-stabilizerness Dynamics Across Quantum Phase Transitions
Stabilizer Rényi entropies and Pauli spectrum cumulants show universal power-law scaling with driving rate in slow processes across quantum phase transitions, with the logarithmic Pauli spectrum asymptotically Gaussian, demonstrated in the transverse-field Ising model and long-range Kitaev models.
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Geometric and Resource-Theoretic Characterisation of Non-Stabiliserness in Quantum Algorithms
Introduces permutation-agnostic distance measures to quantify non-stabiliserness consumption and shows structured variational methods use it more efficiently than unstructured ones with greater classical optimisation freedom.
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Structured Parameterization and Non-Stabilizerness in Hypergraph QAOA
kA-QAOA matches MA-QAOA approximation ratios on 3-uniform hypergraphs while using significantly fewer function evaluations.
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Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
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Spontaneous fractional Josephson current from parafermions
Length asymmetry between counter-propagating chiral edges in a parafermion Josephson junction supplies a spontaneous phase bias that electrically controls Majorana (m=1) or parafermion (m>1) zero modes at Laughlin fillings.
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Calibrating the Role of Entanglement in Variational Quantum Algorithms from a Geometric Perspective
Quantum state evolution in variational algorithms is governed by geometric phase rather than dynamical phase, with entanglement decoupled from evolution in hardware-efficient ansatzes but acting as a dynamical resource in Hamiltonian variational ansatzes.
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The Neuromorphic Supremacy
Hybrid neuromorphic-ANN models outperform standard deep learning on few-shot benchmarks and under occlusion/impulse noise via astrocytic modulation and spiking dynamics.
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AI Consciousness and Existential Risk
Consciousness does not directly predict AI existential risk unlike intelligence, though it may indirectly affect risk through alignment or capability requirements.
- Computable fermionic non-Gaussianity from the covariance matrix