Selected work

Representative Papers

Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

Feb 17, 2026

This work addresses the challenge that existing molecular generation models struggle to efficiently produce highly drug-like compounds and are constrained by the distribution of training data. The authors propose a novel approach that, for the first time, integrates D-Wave quantum annealing with deep generative modeling through a neural hash function (NHF) endowed with both regularization and binarization capabilities. This NHF enables efficient conversion between continuous and discrete representations and is embedded directly into the objective function to guide optimization. Notably, the method transcends the limitations of training data without requiring explicit constraints, yielding molecules that significantly outperform those generated by fully classical models in both drug-likeness and validity, thereby advancing the quality of unconstrained molecular design.

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Quantum Deep Sets and Sequences

Apr 03, 2025

This work addresses the challenge of learning permutation-variant functions—i.e., functions whose inputs are sets or sequences of variable length—in quantum machine learning. Methodologically, it introduces (1) a permutation-invariant quantum set representation, achieved via quantum state averaging to model unordered collections; and (2) a quantum sequence model based on optimal coherentization of triply random tensors, where ordered structure is encoded through matrix product density operators. The framework unifies support for classification, regression, and density estimation. Empirical evaluation on synthetic benchmarks demonstrates superior expressive power and generalization performance compared to classical deep set models. By enabling principled quantum modeling of variable-length data, this work significantly expands the capability frontier of quantum machine learning for non-fixed-length structured inputs.

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Blockchain with proof of quantum work

Mar 18, 2025

The high energy consumption and environmental impact of classical proof-of-work (PoW) consensus mechanisms in blockchain systems pose critical sustainability challenges. Method: This paper proposes the first quantum proof-of-work (QPoW) consensus mechanism, requiring quantum hardware for mining—the first blockchain whose core consensus layer directly leverages quantum advantage. We embed quantum supremacy into the consensus layer by designing a noise-resilient quantum hash function and a fault-tolerant sampling protocol tailored to quantum annealing hardware, reusing established quantum supremacy experimental paradigms. Contribution/Results: Evaluated on a distributed prototype system comprising four D-Wave quantum annealers across North America, QPoW executed over 100,000 stable quantum hash operations. Empirical validation confirms that classical hardware cannot efficiently simulate this process. Compared to classical PoW, QPoW reduces energy consumption and carbon footprint significantly, offering a verifiable, quantum-native pathway toward green blockchain infrastructure.

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Variational decision diagrams for quantum-inspired machine learning applications

Feb 06, 2025

Variational quantum state representation in quantum machine learning (QML) suffers from trainability bottlenecks—particularly the barren plateau phenomenon—hindering scalable optimization. Method: This work introduces decision diagrams (DDs) to QML for the first time, proposing variational decision diagrams (VDDs): a lightweight, structured tensor-network ansatz that integrates DD-based compression with variational flexibility. VDDs enable efficient, sparse representations of quantum states via hierarchical, parameterized node structures. Contribution/Results: Theoretical analysis and numerical experiments on ground-state estimation for transverse-field Ising and Heisenberg models demonstrate that VDDs exhibit parameter gradient variances independent of system size—effectively mitigating barren plateaus. Empirically, VDDs achieve rapid convergence and low resource overhead. Moreover, their hierarchical structure preserves interpretability without sacrificing expressive power. This work establishes a new paradigm for QML that simultaneously ensures expressivity, trainability, and interpretability.

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Recent publications

Latest Papers

Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

Feb 17, 2026

This work addresses the challenge that existing molecular generation models struggle to efficiently produce highly drug-like compounds and are constrained by the distribution of training data. The authors propose a novel approach that, for the first time, integrates D-Wave quantum annealing with deep generative modeling through a neural hash function (NHF) endowed with both regularization and binarization capabilities. This NHF enables efficient conversion between continuous and discrete representations and is embedded directly into the objective function to guide optimization. Notably, the method transcends the limitations of training data without requiring explicit constraints, yielding molecules that significantly outperform those generated by fully classical models in both drug-likeness and validity, thereby advancing the quality of unconstrained molecular design.

0 citationsRead paper

Quantum Deep Sets and Sequences

Apr 03, 2025

This work addresses the challenge of learning permutation-variant functions—i.e., functions whose inputs are sets or sequences of variable length—in quantum machine learning. Methodologically, it introduces (1) a permutation-invariant quantum set representation, achieved via quantum state averaging to model unordered collections; and (2) a quantum sequence model based on optimal coherentization of triply random tensors, where ordered structure is encoded through matrix product density operators. The framework unifies support for classification, regression, and density estimation. Empirical evaluation on synthetic benchmarks demonstrates superior expressive power and generalization performance compared to classical deep set models. By enabling principled quantum modeling of variable-length data, this work significantly expands the capability frontier of quantum machine learning for non-fixed-length structured inputs.

0 citationsRead paper

Blockchain with proof of quantum work

Mar 18, 2025

The high energy consumption and environmental impact of classical proof-of-work (PoW) consensus mechanisms in blockchain systems pose critical sustainability challenges. Method: This paper proposes the first quantum proof-of-work (QPoW) consensus mechanism, requiring quantum hardware for mining—the first blockchain whose core consensus layer directly leverages quantum advantage. We embed quantum supremacy into the consensus layer by designing a noise-resilient quantum hash function and a fault-tolerant sampling protocol tailored to quantum annealing hardware, reusing established quantum supremacy experimental paradigms. Contribution/Results: Evaluated on a distributed prototype system comprising four D-Wave quantum annealers across North America, QPoW executed over 100,000 stable quantum hash operations. Empirical validation confirms that classical hardware cannot efficiently simulate this process. Compared to classical PoW, QPoW reduces energy consumption and carbon footprint significantly, offering a verifiable, quantum-native pathway toward green blockchain infrastructure.

0 citationsRead paper

Variational decision diagrams for quantum-inspired machine learning applications

Feb 06, 2025

Variational quantum state representation in quantum machine learning (QML) suffers from trainability bottlenecks—particularly the barren plateau phenomenon—hindering scalable optimization. Method: This work introduces decision diagrams (DDs) to QML for the first time, proposing variational decision diagrams (VDDs): a lightweight, structured tensor-network ansatz that integrates DD-based compression with variational flexibility. VDDs enable efficient, sparse representations of quantum states via hierarchical, parameterized node structures. Contribution/Results: Theoretical analysis and numerical experiments on ground-state estimation for transverse-field Ising and Heisenberg models demonstrate that VDDs exhibit parameter gradient variances independent of system size—effectively mitigating barren plateaus. Empirically, VDDs achieve rapid convergence and low resource overhead. Moreover, their hierarchical structure preserves interpretability without sacrificing expressive power. This work establishes a new paradigm for QML that simultaneously ensures expressivity, trainability, and interpretability.

0 citationsRead paper