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Expressive Equivalence of Classical and Quantum Restricted Boltzmann Machines - Paper Implementation Challenge #829

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musfarmuhamed opened this issue Feb 28, 2025 · 1 comment
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Paper Implementation Project Implement a paper using Classiq

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@musfarmuhamed
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Paper Link

arXiv:2502.17562v1

Summary

This paper introduces semi-quantum restricted Boltzmann machines (sqRBMs), a hybrid quantum-classical model that combines:

  • Commuting Pauli-Z terms on visible units (classical data).
  • Non-commuting Pauli-X/Y/Z terms on hidden units (quantum interactions).
  • Analytical gradients and 3× fewer hidden units than classical RBMs for equivalent expressivity.

Key contributions:

  1. Closed-form probabilities and gradients for sqRBMs.
  2. Theoretical proof of expressive equivalence: sqRBM_{n,m} ≡ RBM_{n,3m}.
  3. Numerical validation on parity/cardinality datasets.

Implementation Tasks

  1. Model Hamiltonian Encoding

    • Define visible units with Pauli-Z fields.
    • Implement non-commuting Pauli-X/Y/Z terms for hidden units.
    • Add parameterized Z⊗X/Y/Z couplings between visible and hidden units.
  2. Gibbs State Preparation

    • Use variational thermalization or Trotterization to approximate e^{-βH}/Z.
    • Optimize circuit depth for NISQ compatibility (leverage Classiq’s synthesis engine).
  3. Gradient Computation

    • Implement analytical gradients from Propositions 2–3 (closed-form expressions).
    • Integrate with Classiq’s parameter-shift/autodiff tools.
  4. Training Loop

    • Hybrid workflow: Quantum sampling + classical optimization (e.g., AMSGrad).
    • Reproduce parity/cardinality dataset experiments (Fig. 3/4 in paper).
  5. Benchmarking

    • Compare sqRBM vs. classical RBM resource requirements (hidden units, parameters).
    • Report TVD/KL divergence metrics.
@NadavClassiq NadavClassiq added the Paper Implementation Project Implement a paper using Classiq label Mar 2, 2025
@TomerGoldfriend
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Sounds good @musfarmuhamed, from a quick look it seems like you will have to implement a Gibbs state preparation as part of the algorithm. This is a non-trivial task in itself :-) .

Please note that we accept high-quality implementations in our repository and will be glad to accept a contribution that meets our standards.
Feel free to contact the community if you have any questions.

Good luck!

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