pith. sign in

arxiv: 1203.5813 · v3 · pith:AOO3WIU7new · submitted 2012-03-26 · 🪐 quant-ph · cond-mat.str-el

Quantum computing and the entanglement frontier

classification 🪐 quant-ph cond-mat.str-el
keywords quantumclassicalfrontiersystemsentanglementcomputerdonehighly
0
0 comments X
read the original 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 16 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Neural Polaron: Learning Quasiparticle Operators in Quantum Many-Body Systems

    cond-mat.str-el 2026-06 unverdicted novelty 7.0

    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.

  2. Fermionic non-Gaussianity via Bell sampling: monotones and efficient quantum algorithms

    quant-ph 2026-06 unverdicted novelty 7.0

    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 Gau...

  3. Nonlocal nonstabilizerness in free fermion models

    quant-ph 2026-04 unverdicted novelty 7.0

    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 ra...

  4. Exponential quantum advantage in processing massive classical data

    quant-ph 2026-04 unverdicted novelty 7.0

    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.

  5. Efficient simulation of noisy IQP circuits with amplitude-damping noise

    quant-ph 2026-04 unverdicted novelty 7.0

    A classical polynomial-time sampler exists for the output distribution of amplitude-damped IQP circuits with logarithmic depth and arbitrary l-local diagonal gates.

  6. Computing quantum magic of state vectors

    quant-ph 2026-01 accept novelty 7.0

    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.

  7. Computable measures of fermionic non-Gaussianity from the covariance matrix

    quant-ph 2026-07 unverdicted novelty 6.0

    Introduces occupation number entropies (Tsallis) and natural-orbital participation entropies (Renyi) as computable convex resource monotones for fermionic non-Gaussianity from the covariance matrix.

  8. Tensor-Network-Based Distributed Quantum Dynamics on Independent Quantum Computers

    quant-ph 2026-06 unverdicted novelty 6.0

    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...

  9. Structured Parameterization and Non-Stabilizerness in Hypergraph QAOA

    quant-ph 2026-05 unverdicted novelty 6.0

    kA-QAOA matches MA-QAOA approximation ratios on 3-uniform hypergraphs while using significantly fewer function evaluations.

  10. Universal Non-stabilizerness Dynamics Across Quantum Phase Transitions

    quant-ph 2026-03 unverdicted novelty 6.0

    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 Gaussia...

  11. Geometric and Resource-Theoretic Characterisation of Non-Stabiliserness in Quantum Algorithms

    quant-ph 2025-07 unverdicted novelty 6.0

    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.

  12. Calibrating the Role of Entanglement in Variational Quantum Algorithms from a Geometric Perspective

    quant-ph 2026-04 unverdicted novelty 5.0

    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 resourc...

  13. Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels

    quant-ph 2025-07 unverdicted novelty 5.0

    Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.

  14. Spontaneous fractional Josephson current from parafermions

    cond-mat.mes-hall 2022-08 unverdicted novelty 5.0

    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.

  15. The Neuromorphic Supremacy

    q-bio.NC 2026-06 unverdicted novelty 4.0

    Hybrid neuromorphic-ANN models outperform standard deep learning on few-shot benchmarks and under occlusion/impulse noise via astrocytic modulation and spiking dynamics.

  16. AI Consciousness and Existential Risk

    cs.AI 2025-11 unverdicted novelty 2.0

    Consciousness does not directly predict AI existential risk unlike intelligence, though it may indirectly affect risk through alignment or capability requirements.