First semi-device independent quantum money scheme introduced, proving unforgeability.
problem Creating secure quantum money without full device independence.
method Inspired by semi-device independent quantum key distribution, relaxes mint's cooperation assumption.
result First unforgeable quantum money scheme with partially relaxed device independence.
MPE framework proves universal approximation for quantum data distribution.
problem Challenges in generating quantum data from underlying distributions.
method Many-body Projected Ensemble (MPE) framework for quantum state design.
result MPE can approximate any quantum distribution within 1-Wasserstein distance error.
Quantum walk algorithm optimizes quantum state preparation for financial simulations.
problem Efficiently loading classical data into quantum states for quantum computers.
method Split-step quantum walks (SSQW) to design parameterized quantum circuits (PQC).
result SSQW facilitates generating desired probability amplitude distributions for quantum simulations.
Novel quantum algorithm for financial market modeling.
problem Accurate quantum state preparation for financial simulation.
method Multi-Split-Steps Quantum Walk (multi-SSQW) with PQC and variational solver.
result Highly accurate modeling of complex financial distributions.
Single T-gate makes distribution learning hard for deep circuits.
problem Learning probability distributions from quantum circuits.
method Characterization of learnability and simulatability of quantum circuit outputs.
result Injection of a single T-gate into depth n^Ω(1) circuits makes distribution learning hard.
Quantum computing offers a quadratic speedup for estimating non-linear functionals.
problem Estimating non-linear functionals of probability distributions.
method Proposes a quantum-inside-quantum Monte Carlo algorithm for a broad class of non-linear estimation problems.
result Achieves a quadratic speedup for non-linear estimation problems, including nested conditional expectations and stochastic optimization.
Paper develops security model and pricing for stable digital currency in quantum blockchain network.
problem Securing and pricing stable digital currency in a quantum blockchain network.
method Developed a block-based quantum channel networking technology and a FinTech platform model with dynamic pricing.
result Established a generalized IoB security model using quantum channel networking and QKD.
Quantum computing improves training of binary neural networks.
problem Training binary neural networks (BiNNs) is challenging.
method Employing a Variational Quantum Algorithm to generate binary weights using quantum circuit measurements.
result The proposed methods improve trainability and generalization of BiNNs.
Quantum Reservoir Computing classifies complex probability distributions and identifies volatility regimes.
problem Statistical and financial classification problems with heavy-tailed distributions and correlated time series.
method Implemented QRC in a superconducting quantum circuit with Josephson junctions.
result QRC outperforms classical methods in limited information scenarios.
Quantum models improve data generation from noisy quantum processors.
problem Creating complex probability distributions from limited data.
method Quantum-noise-driven generative diffusion models.
result Quantum noise can be harnessed to generate more complex distributions efficiently.
Quantum computing poses a threat to Bitcoin and Ethereum, but only to spending and not mining.
problem Quantum computing threat to Bitcoin and Ethereum
method Separation of Shor's and Grover's quantum algorithms
result Quantum algorithms' impact on Bitcoin and Ethereum
QCircuitBench provides a dataset for evaluating AI's ability to design quantum algorithms.
problem Lack of datasets for evaluating AI's capability in designing quantum algorithms.
method Developed a comprehensive benchmark dataset with 120,290 data points, including 25 algorithms and 3 task suites.
result LLMs exhibit consistent error patterns and fine-tuning does not always outperform few-shot learning.
A quantum walk-based method for generating precise probability distributions efficiently.
problem Generating high-precision probability distributions for various applications.
method Integrates variational quantum circuits with split-step quantum walks to dynamically tune coin parameters and evolve quantum states.
result Achieves high simulation fidelity and reduces computational overhead compared to conventional methods.
Introduces noncommutative geometry for modeling quantum spacetime.
problem Modeling quantum spacetime.
method Operator algebras, K-theory, spectral geometry, quantum groups, and deformation quantization.
result Framework for quantum spacetime.
Quantum models learn unitary actions on entangled states from product states.
problem Generalization to out-of-distribution data in quantum machine learning.
method Proved out-of-distribution generalization for learning unitary actions.
result Learned unitary actions on entangled states from product states.
This work proposes efficient classical training protocols for IQP circuits to train quantum generative models.
problem Training quantum generative models on industrially relevant probability distributions is challenging due to high computational cost.
method Developed protocols for classical training of IQP circuits, which are hard to sample but have efficient gradient computation.
result Classically trained IQP circuits can efficiently sample from target probability distributions, demonstrating practical quantum advantage.
A new framework to perturbative quantum gravity is proposed following the geometry of nonholonomic distributions on (pseudo) Riemannian manifolds. There are considered such distributions and adapted connections, also completely defined by a metric structure, when gravitational models with infinite many couplings reduce…
Quantum dilogarithm function proven from a linear difference equation.
problem Proving Faddeev's quantum dilogarithm from a linear difference equation.
method Proved Faddeev's quantum dilogarithm using Borel summation of a formal power series solution of a linear difference equation.
result Borel summation of a formal power series solution produces Faddeev's quantum dilogarithm.
Study shows limitations and possibilities of learning quantum circuit output distributions.
problem Learnability of output distributions of local quantum circuits.
method Investigated within two oracle models: statistical query model and direct sample access model.
result Output distributions of super-logarithmic depth Clifford circuits are not efficiently learnable in the statistical query model.
This paper applies quantum probability theory to model asset returns, avoiding assumptions about quantum effects.
problem Modeling asset returns with classical probability theory.
method Derives a Schrödinger-like trading equation using quantum probability, linking it to traders' decisions and market behaviors.
result Quantum probability can describe multimodal distributions of asset returns without assuming quantum effects.
Quantum Gaussian processes enable scalable quantum learning.
problem Lack of simple, interpretable, scalable learning frameworks for quantum data.
method Bayesian framework using Gaussian processes with quantum kernels.
result Provable and scalable quantum Gaussian processes for quantum learning.
Analyzes magnetic Laplacian on hyperbolic surfaces, highlighting key quantum phenomena.
problem Understanding quantum phenomena on hyperbolic surfaces with magnetic fields.
method Semiclassical analysis and mathematical modeling of the magnetic Laplacian.
result Discovers new insights into quantum behavior on hyperbolic surfaces with magnetic fields.
The paper studies distributions and controllability in quantum mechanical systems.
problem Controlling quantum mechanical systems and their evolution.
method Analysis of distributions, controllability, and geodesics on sub-Finsler manifolds.
result Proves the Lie group decomposition and geodesics equivalence for quantum system steering.
This work shows how to efficiently simulate parts of quantum landscapes using classical computers.
problem Identifying where quantum computers are advantageous and offloading computations.
method Developed a quantum-enhanced classical algorithm to simulate sub-regions of quantum landscapes.
result It is possible to generate a classical surrogate of a sub-region of a quantum landscape.
Distributed Quantum Gaussian Processes improve modeling in multi-agent systems.
problem Limited expressivity of classical kernels in complex domains.
method Distributed Quantum Gaussian Process (DQGP) with DR-ADMM algorithm.
result Enhanced modeling capabilities and scalability in multi-agent systems.
We introduce DQFIM to quantify and improve generalization of quantum machine learning models.
problem Understanding and improving generalization of quantum machine learning models.
method Data quantum Fisher information metric (DQFIM) to quantify circuit parameters and training data.
result Improves generalization by breaking symmetries of training data and using a low number of training states.
Quantum statistical models with singularities are studied for state estimation and model selection.
problem Understanding statistical properties of quantum singular models.
method Classical singular learning theory extended to quantum state estimation and model selection using algebraic geometrical methods.
result Asymptotically unbiased estimator (QWAIC) for quantum generalization loss constructed.
Quantum gravity yields mapping class group representations.
problem Quantization of 3-manifold metrics and mapping class group invariance.
method Quantum dilogarithm functions and mapping class group action.
result Families of unitary representations of mapping class groups.
Unified framework for robust causal directionality in quantum systems under MNAR observation.
problem Determining causal directionality in quantum systems under MNAR observation.
method Integrates CVAE-based latent constraints, MNAR-aware selection models, GEE-stabilized regression, penalized empirical likelihood, and Bayesian optimization.
result Achieves lower bias and variance, near-nominal coverage, and superior quantum-specific diagnostics.
Quantum algorithm speeds up nested expectation estimation by nearly quadratically.
problem Estimating repeatedly nested expectations with quantum computing.
method Proposes a quantum algorithm achieving nearly quadratic speedup over classical methods.
result Achieves nearly quadratic speedup for RNEs, up to logarithmic factors.
Quantum annealers aim at solving non-convex optimization problems by exploiting cooperative tunneling effects to escape local minima. The underlying idea consists in designing a classical energy function whose ground states are the sought optimal solutions of the original optimization problem and add a controllable qua…
Paper connects knot theory with cluster algebra via Alexander polynomials.
problem Understanding Alexander polynomials for 2-bridge knots.
method Use of cluster variables and ancestral triangles.
result Alexander polynomials are specializations of cluster variables.
Quantum self-attention boosts automated market maker performance in crypto trading.
problem Improving automated market maker rebalancing in crypto trading.
method Quantum Adaptive Self-Attention (QASA) using variational quantum circuits and softmax attention.
result QASA-Sequence variant achieves best single-model risk-adjusted performance in crypto trading.
Quantum computing aids in predicting financial crashes.
problem Predicting financial crashes when markets are disrupted.
method Mapped financial network to quantum Hamiltonian, solved using quantum annealing.
result Quantum computing can efficiently assess financial equilibrium and predict crashes.
This work introduces a new quantum kernel, quantum tangent kernel, for improved performance.
problem Improving quantum machine learning performance beyond conventional methods.
method Developed a deep parameterized quantum circuit and used first-order expansion for training.
result The quantum tangent kernel outperforms conventional quantum kernel methods for ansatz-generated datasets.
Quantum walks model financial returns with flexibility and asymmetry.
problem Accurate modeling of financial asset price dynamics.
method Discrete-time quantum walks to model asset price evolution.
result Quantum walk models can generate asymmetric return distributions and higher probabilities for extreme events.
Quantum MC simulations generate financial risk distributions efficiently.
problem High computational cost in traditional Monte Carlo simulations.
method Integrates quantum amplitude estimation with stochastic models for equity, rate, and credit risk factors.
result Quantum advantage in scenario generation for financial risk analytics.
Unified 3D R-matrices from quantum cluster algebra.
problem Constructing new solutions to the tetrahedron equation.
method Symmetric butterfly quiver, quantum cluster algebra, quantum dilogarithms, q-Weyl algebra.
result Unified 3D R-matrices from various sources.
MPSTime uses matrix-product states for efficient time-series ML.
problem Learning complex correlations in time-series data.
method Developed an MPS-based algorithm for joint probability distribution learning.
result MPSTime efficiently learns time-series probability distributions.
Quantum invariant derived from ternary cohomology of self-distributive structures.
problem Defining and proving a quantum invariant from ternary cohomology.
method Constructing a ribbon category from a TSD set, showing it coincides with the cocycle invariant.
result The ribbon cocycle invariant is a quantum invariant.
Quantum correlations enhance generative models, providing a new resource for machine learning.
problem Capturing complex probability distributions in unsupervised learning.
method Theoretical and numerical analysis of quantum correlations in generative models.
result Quantum nonlocality and contextuality provide an expressivity advantage over classical models.
The paper calculates fractional quantum numbers on complex orbifolds with strong magnetic fields.
problem Understanding fractional quantum numbers in complex orbifolds with strong magnetic fields.
method The study uses Landau Hamiltonians on complex, compact 2D orbifolds and a nontrivial generalisation of the Nahm transform.
result Fractional quantum numbers are calculated as conductance and charge transport is refined.
Quantum circuit Born machines are generative models which represent the probability distribution of classical dataset as quantum pure states. Computational complexity considerations of the quantum sampling problem suggest that the quantum circuits exhibit stronger expressibility compared to classical neural networks. O…
Quantum link invariants derived from skein algebras.
problem Defining invariants for framed links with SL2 local systems.
method Theory of representations of stated skein algebras, quantum coadjoint action, Drinfeld double, Bonahon-Wong quantum trace.
result Explicit formulas for link invariants and alternative proof of Murakami-Murakami relation.
Quantum UCB algorithm reduces reinforcement learning regret exponentially.
problem Episodic reinforcement learning with quantum state evolution.
method Upper Confidence Bound (UCB) quantum algorithm with quantum mean estimation.
result Exponential improvement in regret from $\Tilde{\mathcal{O}}(\sqrt{K})$ to $\Tilde{\mathcal{O}}(1)$.
Infinite families of quantum modular invariants for 3-manifolds are discovered.
problem Quantum modular forms and invariants of 3-manifolds.
method Lattice cohomology and BPS q-series of 3-manifolds, recently developed theory by AJK. result First calculation of AJK series for an infinite family of 3-manifolds.
A quantum state generation method that respects physical constraints.
problem Generating quantum states with complex-valued Hermitian, positive semi-definite, and trace one properties.
method Mirror diffusion model with von Neumann entropy to enforce structural constraints.
result Demonstrated effective generation of quantum states with conditional guidance.
Quantum machine learning improves hedging in finance.
problem Improving hedging strategies in financial markets.
method Developed quantum reinforcement learning methods using policy-search and distributional actor-critic algorithms.
result Quantum models reduce parameter count and achieve comparable performance to classical methods.