D-Wave quantum annealer used for facial image feature learning.
problem Learning features from large facial image datasets.
method Nonnegative/binary matrix factorization with D-Wave quantum annealer.
result D-Wave quantum annealer can extract features from large datasets.
Investigates quantum vs classical portfolio optimization of 60 stocks.
problem Optimizing risk vs return portfolios of 60 stocks using quantum and classical methods.
method Classical and quantum annealing approaches applied to historical data.
result Quantum and classical methods yield similar optimal portfolios.
Study analyzes 3,171 stocks to pick efficient portfolios using quantum and classical solvers.
problem Creating efficient stock portfolios from a large dataset.
method Used classical and quantum solvers to optimize portfolios of 3,171 US stocks.
result Demonstrated the effectiveness of quantum and classical solvers in portfolio optimization.
Quantum computer optimizes investment portfolios, outperforming traditional methods.
problem Minimizing risk while meeting return and budget constraints in investment portfolios.
method Used D-Wave quantum annealer and hybrid solvers to solve Portfolio Optimization problem.
result D-Wave quantum solution performs close to traditional commercial solvers for tested problem sizes.
Quantum annealing solves matrix factorization for large datasets.
problem Finding low-rank approximations of large real-valued matrices.
method Transformed real optimization into QUBO problems, solved on D-Wave quantum annealer.
result Quantum approach outperforms classical methods and finds better results.
D-Wave quantum annealing fails to improve sampling quality from RBMs compared to Gibbs sampling.
problem Improving sampling quality from RBMs using D-Wave quantum annealing.
method Comparison of D-Wave quantum annealing and Gibbs sampling for RBM sampling.
result D-Wave sampling does not significantly improve the number of local valleys compared to Gibbs sampling.
Regression algorithm uses quantum annealer for sparse inference in lattice QCD simulations.
problem Improving prediction accuracy in lattice QCD simulations.
method Proposes a regression algorithm that combines sparse inference with a D-Wave quantum annealer.
result Quantum annealer improves prediction performance in lattice QCD simulations.
Quantum annealer improves classification of handwritten digits.
problem Classical MCMC struggles with missing labels in RBM models.
method Embedding RBM into D-Wave quantum annealer for classification.
result D-Wave QA reduces classification error by more than two times.
Quantum SVMs outperform classical ones on limited data.
problem Classifying and regressing with limited training data.
method Trained SVMs on D-Wave quantum annealer and compared to classical SVMs.
result Quantum SVMs often generalize better to unseen data.
Quantum annealer speeds up RBM training for image classification.
problem Training RBM with contrastive divergence (CD) is slow and computationally expensive.
method Used D-Wave 2000Q quantum annealer to calculate model expectation of gradient learning for RBM.
result Quantum training yields similar classification performance to CD but faster.
Lossy compression of statistical data using quantum annealing.
problem Efficiently compressing statistical floating-point data.
method Representation learning with binary variables, classical optimization of basis vectors, quantum annealing for coefficients, bias correction.
result Quantum annealing shows promising results with 3.5x better compression than neural-network autoencoders.
Quantum computing techniques improve graph analysis and community detection.
problem Analyzing large graphs efficiently and accurately.
method Used quantum annealing and quantum gate computers for community detection and regularity checking.
result Demonstrated the effectiveness of quantum computing in solving complex graph problems.
Study explores D-Wave QA for RBM training, finds modest benefits but no significant improvements.
problem Improving RBM training using D-Wave quantum annealing.
method Hybrid sampling approach combining classical and D-Wave QA methods.
result No significant improvements in RBM training using D-Wave QA, but potential for CF mitigation.
Quantum computer helps optimize stock portfolios.
problem Finding the best mix of stocks for optimal risk and return.
method Classical and quantum approaches to portfolio optimization.
result Quantum computer improves portfolio selection.
Optimized reverse quantum annealing speeds up portfolio optimization.
problem Optimizing portfolios using quantum and classical methods.
method Hybrid quantum-classical approach, including reverse quantum annealing.
result Optimized reverse quantum annealing is 100 times faster than forward quantum annealing.
Optimizes train schedules and maintenance using CP and QA.
problem Optimizing train schedules and maintenance considering constraints.
method Used Constraint Programming and Quantum Annealing to model and solve the problem.
result Both CP and QA approaches produce comparable results on real quantum computers.
We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and t…
Quantum algorithm for dynamic asset allocation using expected shortfall.
problem Dynamic risk management in finance, especially tail risks.
method Quantum annealing algorithm in QUBO form for expected shortfall constraint.
result Quantum algorithm provides a faster solution for dynamic asset allocation.
Quantum-assisted GAN learns MNIST and LSUN datasets.
problem Learning latent variable generative models with adversarial networks.
method Generative adversarial learning with quantum annealing.
result Quantum-assisted GAN successfully learns MNIST and LSUN datasets.
Quantum annealing speeds up training of deep neural networks.
problem Time-consuming generative training in deep learning.
method Used quantum annealing to estimate model expectations of RBMs.
result Quantum sampling-based training achieves comparable or better accuracy with fewer iterations.
Quantum machine learns to clean up blurry images.
problem Cleaning up blurry images using quantum computing.
method Uses Boltzmann machines, QUBO, and quantum annealing to balance image quality and noise.
result Quantum method produces cleaner images than noisy originals on average.
End-to-end portfolio optimization using quantum annealing for financial decision problems.
problem Optimizing financial portfolios with quantum computing constraints.
method Hybrid pipeline combining quantum and classical optimization.
result Quantum-assisted portfolio optimization can achieve competitive returns.
Proposes a quantum-inspired algorithm for selecting representative data subsets.
problem Selecting the most representative subset of data from a larger dataset.
method Uses a Quadratic Unconstrained Binary Optimization (QUBO) problem approach.
result Demonstrates the effectiveness of the selector algorithm in finance applications.
Quantum Annealing Enhanced Reinforcement Learning for Accurate RUL Prediction
problem RUL estimation in predictive maintenance
method QAQL framework combining quantum annealing and Q-learning
result Outperforms classical and quantum baselines
A new penalty-free method optimizes portfolios without quantum annealing penalties.
problem Optimizing portfolios with quantum annealing penalties.
method Removing the penalty term and using a classical feasibility projector.
result Significant reduction in chain-break fractions and post-processed regret.
This study optimizes currency arbitrage using quantum computing methods.
problem Optimizing profitable trading routes in currency markets.
method Quantum Annealing, QAOA, and Constraint Mapping.
result Quantum computing techniques enhance the identification of optimal arbitrage paths.
Paper tackles dynamic portfolio optimization using quantum and quantum-inspired methods.
problem Optimizing investment portfolios over time considering transaction costs and constraints.
method Implemented quantum and quantum-inspired algorithms on different hardware platforms for real data.
result D-Wave Hybrid and Tensor Networks handle the largest systems up to 1272 qubits.
Quantum Boltzmann Machines trained on quantum annealers produce noisy synthetic data.
problem Training quantum Boltzmann machines on quantum annealers for financial data generation.
method Used D-Wave Advantage 4.1 quantum annealer to train QBMs and compare with classical RBMs.
result Quantum Boltzmann Machines trained on quantum annealers are noisier and less effective than classical RBMs.
Quantum computing aids in optimizing currency reserves for central banks.
problem Optimizing currency composition in foreign exchange reserves.
method Comparison of quantum and classical algorithms for portfolio optimization.
result Quantum algorithms outperform classical methods in currency optimization.
D-Wave hybrid quantum-classical portfolio optimization shows classical decomposition is key, not quantum sampling.
problem Optimizing portfolios with constraints using hybrid quantum-classical methods.
method Operational decomposition audit of D-Wave's hybrid quantum-classical service on mean-variance-turnover instances.
result Classical decomposition and feasibility-aware reassembly are key to hybrid quantum-classical performance.
Quantum optimization for portfolios with risk and diversification constraints.
problem Implementing complex constraints in portfolio optimization for financial applications.
method Transformed portfolio optimization into a quadratic binary optimization problem suitable for quantum annealers.
result Demonstrated practical implementation of daily constraints in real data using quantum processors.
D-Wave computers struggle with sampling Boltzmann distributions efficiently.
problem Sampling Boltzmann distributions efficiently on D-Wave computers.
method Exploring various obstacles and remaining difficulties.
result Challenges remain in using D-Wave computers for efficient sampling.
Quantum machine learning classifies lung cancer subtypes.
problem Accurately classify Adenocarcinoma vs Squamous cell carcinoma patients.
method Amalgamation of classical and quantum machine learning models, feature selection, QCrush data representation, Quantum Boltzmann Machine.
result Successfully classified 104 non-small cell lung cancer patients.
Simulated Bifurcation outperforms quantum machines in community detection.
problem Community detection in complex networks
method Quantum-inspired Simulated Bifurcation algorithm for QUBO formulation
result Simulated Bifurcation achieves highest modularity in community detection
Reverse annealing boosts quantum matrix factorization performance.
problem Improving quantum matrix factorization performance.
method Combining forward and reverse annealing for nonnegative/binary matrix factorization.
result Combination of forward and reverse annealing significantly improves performance.
A new diffusion model uses efficient conditional estimators for discrete data.
problem Efficient estimation of conditional probabilities for discrete data.
method Discrete denoising diffusion framework with sample-efficient NeurISE conditional estimation.
result The method outperforms existing approaches in various metrics on binary and scientific data.
Quantum annealing outperforms classical in solving non-convex optimization problems crucial for machine learning.
problem Solving non-convex optimization problems efficiently.
method Designing a classical energy function and adding a quantum transverse field to facilitate tunneling.
result Quantum annealing converges efficiently to optimal solutions in a wide class of non-convex problems, unlike classical thermal annealing.
New algorithm uses quantum annealing to improve EM for Gaussian mixture models.
problem EM algorithm's sensitivity to initial values and local optima.
method Integrates quantum annealing into the EM algorithm to enhance stability.
result DQAEM algorithm is more stable and efficient in estimating Gaussian mixture models.
DQAEM uses quantum annealing to improve MLE.
problem EM algorithm's dependence on initial configurations and inability to find global optimum.
method Quantum annealing extension of EM (DQAEM).
result DQAEM outperforms EM in MLE.
Researchers propose a method to simulate quantum annealing with non-stoquastic Hamiltonians.
problem Negative sign problem in quantum Monte Carlo simulation of non-stoquastic Hamiltonians.
method Alternative approach using Suzuki--Trotter decomposition to avoid negative sign problem.
result Demonstrated method's validity through application to a simple problem.
This paper presents studies on a deterministic annealing algorithm based on quantum annealing for variational Bayes (QAVB) inference, which can be seen as an extension of the simulated annealing for variational Bayes (SAVB) inference. QAVB is as easy as SAVB to implement. Experiments revealed QAVB finds a better local …
Quantum annealing improves EM algorithm's performance in nonconvex optimization.
problem EM algorithm's tendency to get stuck in local optima for nonconvex problems.
method Integrates quantum fluctuations into EM algorithm to induce tunnel effect and avoid local optima.
result Quantum annealing EM algorithm converges and performs better in nonconvex optimization problems.
Quantum machine learns faster by reverse annealing on AQCs.
problem Training RBMs on AQCs is hard due to low qubit connectivity.
method Embedding RBM nodes to virtual qubits, semantic quantum search, reverse annealing schedule.
result Reverse annealing accelerates RBM training and improves reconstruction scores.
Quantum annealing improves VB inference, avoiding local minima.
problem Variational Bayes inference stuck in local minima.
method Quantum annealing approach to VB inference.
result Quantum annealing variational Bayes (QAVB) outperforms classical VB.
Quantum optimization aids in financial crash prediction and portfolio management.
problem Hard financial optimization problems.
method Quantum algorithms for financial crashes and portfolio optimization.
result Quantum strategies improve financial prediction and portfolio management.
QAPCA uses quantum annealing for robust PCA.
problem Outliers in data skew L2-norm principal components.
method Quantum annealing for L1-norm optimization.
result QAPCA's reconstruction error is comparable to L1-BF.
Quantum annealing speeds up extreme clustering.
problem Efficiently grouping large datasets into many representative clusters.
method Distributed quantum annealing method.
result Optimal clustering assignments achieved under separability assumption.
New method uses quantum annealing and VAN for better statistical mechanics calculations.
problem Difficulty in computing partition function in statistical mechanics.
method Combines quantum annealing samples with variational autoregressive networks.
result Enhanced accuracy in finite-size Sherrington-Kirkpatrick model.